# Follow-up of the Post-seismic reconstruction in Le Teil from the November 11th 2019 seismic event to now: Insights and zoom over building rehabilitation

Caterina Negulescu, Johanna Vieille, Pierre Gehl, Nicolas Taillefer, Konstantinos Trevlopoulos, Samuel Auclair

► To cite this version:

Caterina Negulescu, Johanna Vieille, Pierre Gehl, Nicolas Taillefer, Konstantinos Trevlopoulos, et al. Follow-up of the Post-seismic reconstruction in Le Teil from the November 11th 2019 seismic event to now: Insights and zoom over building rehabilitation. International Journal of Disaster Risk Reduction, 2025, 122, pp.105438. 10.1016/j.ijdrr.2025.105438. hal-05057467

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# Follow-up of the Post-seismic reconstruction in Le Teil from the November 11th 2019 seismic event to now: Insights and zoom over building rehabilitation

C. Negulescu<sup>a, *</sup>, J. Vieille<sup>a</sup>, P. Gehl<sup>a</sup>, N. Taillefer<sup>b</sup>, K. Trevlopoulos<sup>a</sup>, S. Auclair<sup>a</sup>

## ABSTRACT

The magnitude Mw 4.9 earthquake of November 11, 2019 strongly affected the municipality of Le Teil, Rhone Valley, France. A quarter of around 2,800 exposed buildings were damaged, which led to safety evacuation orders. BRGM, the French Geological Survey, was commissioned by the French Ministry of Ecological Transition and Territorial Cohesion to collect quantitative data about the progress of the reconstruction. This work focuses on field monitoring of certain resilience parameters, such as the time for reconstruction and repairs. On the basis of emergency post-earthquake building safety inspections done by the French Association for Earthquake Engineering (AFPS), the reconstruction of buildings in the municipality of Le Teil is studied at different scales: building, infra-communal and communal. Interpreting damage observations and modelling reconstruction requires multiple sources of information, hence the importance of having data at these three different scales. Based on their reference state in the damaged buildings database, and in order to assess the progress of the reconstruction, the buildings are assigned a reconstruction stage based on annual visual inspections conducted from street level. In addition, a set of indicators has been employed to provide quantitative information evolving over time, such as the number of evacuation orders (issued and lifted) or the number of buildings whose reconstruction process has begun. Moreover, this study includes a detailed classification of the buildings, estimations for their vulnerability and predictive risk scenario calculations depending on assumptions about the level of structural reinforcement. These elements raise important questions about the reconstruction, particularly from the point of view of design standards. Finally, we highlight the importance of physical vulnerability and recovery capacities, two main components of the reconstruction process. This work constitutes a pioneering effort in terms of collecting records and observations of post-earthquake reconstruction in France, over a multi-year timespan.

## 1. Introduction

The November 11th' 2019 Le Teil earthquake (M<sub>w</sub> 4.9), in the Rhone valley, is the most destructive earthquake in mainland France since the Arette earthquake in 1967 [1]. At that time, the reconstruction had lasted 7 years and had mobilized significant resources at the national and local levels. Since then, the constructive and insurance contexts have changed a lot, as have the urban planning tools. Le Teil is an average-sized municipality (8,700 inhabitants), which was very heavily impacted by the earthquake of November 11th, 2019 (850 dangerous structure notices were issued – or “arrêté de péril” in French – out of around 2,800 buildings located in the municipality). This particularly marked impact on Le Teil is due to the fact that the fault rupture was located in the municipality, at a very shallow depth [2]. Observing the recovery process from the point of view of buildings' reconstruction in the town of Le Teil (Fig. 1) will help to learn lessons to improve the resilience of territories exposed to seismic risk. This is the first documented case of post-earthquake reconstruction in France at city level. Besides the value of the documentation itself, a major implication of the work is

<footnote><sup>a</sup> BRGM, Risk and Prevention Department, Orléans, France</footnote>

<footnote><sup>b</sup> BRGM, Mining Security and Prevention Department, Orléans, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: c.negulescu@brgm.fr (C. Negulescu).</footnote>
that the collected data will serve as reference for the future efforts to model and estimate the process of recovery over the course of time, which is a subject that currently concerns a significant part of the disaster risk reduction community.

Following the earthquake, the implementation of the emergency system of the French Association for Earthquake Engineering (AFPS) allowed to collect valuable field data on the physical damage of buildings. The AFPS post-seismic mission also helped to better identify the actors involved in this process and the recovery strategies implemented locally [3]. With the support of AFPS, the municipality of Le Teil has also carried out an in-depth lessons-learned work, highlighting the main lessons learned from earthquake management at local level (Mairie du Teil, 2021). Other missions have also been carried out by the AFPS or by academic teams to continue to acquire information as part of feedback on post-event management. The Ministry of Ecological and Solidarity Transition (MTES), through its Directorate of Risk Prevention, wanted to improve knowledge on the state of reconstruction following the earthquake. In 2021, it commissioned BRGM to collect information to quantify the progress of the process, and to identify the institutional and financial levers that are involved throughout the reconstruction phase. This action benefits greatly from the work of the AFPS [3,4], BRGM [5] and University of Grenoble [6,7].

For over 50 years, the *Learning from Earthquakes* (LFE - EERI) program has been conducting earthquake surveys to learn from earthquake disasters and build community resilience. This program has been the cornerstone of the earthquake engineering community and of the continuous improvement and innovation of field and site investigation techniques. In addition, several studies worldwide (e.g., Opricovic and Tzeng, 2002; [8,9]) have been conducted for the survey and the temporal evolution [10] of post-earthquake reconstruction of damaged buildings (e.g., Ref. [8,11]). Generally, these studies are part of a more complex analysis of post-earthquake situations, in which social and economic dimensions are also assessed. Some of the studies of post-earthquake reconstruction of damaged buildings concern only a typical local building typology. Whatever the approach, all these studies provide valuable information for understanding the different facets of post-earthquake reconstruction. We are here-by focused on the studies that are directly related to the survey of post-earthquake reconstruction of damaged buildings. More specifically, we attempt to highlight and provide elements for comparing the different reconstruction dynamics between our study and recent post-earthquake reconstruction studies (e.g., Ref. [8,11]). Other studies, such as that by Opricovic and Tzeng [12], develop a fuzzy multicriteria model for sustainable post-earthquake reconstruction planning. They illustrated the approach with the post-earthquake reconstruction problem in central Taiwan, considering different scenarios for rebuilding urban areas and infrastructure. They used several sources of statistical information, such as the article by Horikiri and Odani [13], in which it is stated that, three years after the Kobe earthquake in Japan, the average ratio of rebuilt buildings to the total number of structures that had collapsed was 45 %.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Map of Le Teil municipality and its nine districts (coloured polygons in the map).</figcaption></figure>
FEMA [14] gives estimations of the repair time for buildings as a function of the intensity of the ground motion compared to the design intensity. Moreover, repair times are provided for different building classes, depending on the material of construction (e.g., reinforced concrete versus steel) and on therein-defined Risk Categories, such as Office/Healthcare (Medical Office Building or Laboratory), Office (Emergency Operations Center) or Healthcare (Hospital). The repair times given by FEMA [14] are independent of the number of storeys. They concern only the repair work itself, they exclude any preparation period and they are based on assumptions such as that all repairs are executed in parallel rather than in series. Because of these limitations, the proposed values correspond to the lower bound of the repair time. The post-disaster reconstruction process has been modelled by Wang et al. [15] using agent-based simulations, which take into account the effect of construction companies and their personnel, which are originally based outside the study area (out-of-town contractors), the recovery speed, and the temporary housing demand. Wang et al. [15] suggest further developments to account for buildings with multiple households, housing needs for the homeless, contractors housed outside the study area, and contractors for different types of repairs. We note that contractors could be distinguished between those working on repairs for structural and non-structural components, with the latter probably doing repairs after the first, in some cases. Recovery-based design aims not only at satisfying the building code but also at achieving goals related to community resilience, by considering repair and reinforcement for loads exceeding code requirements. Issa et al. [16] propose surrogate models for forecasting recovery metrics under different types of repairs and/or reinforcements. They inform and support the stakeholders for the optimization of decision-making. Using machine-learning-based optimization framework to support recovery-based design of an office building, Issa et al. [16] conclude that the increase of the buildings strength through the importance factor in the design makes it less likely to meet the objective of a 21-day recovery time associated with critical commercial activities. Cook et al. [17] have developed a method for quantifying the loss of building functionality related to damage and response characteristics, which uses the FEMA P-58 fragility and consequence models and which aims to support the decision making for building retrofit. This method uses fault trees to map component damage states to systems-level operational performance, and then to building-level performance states. Moreover, Cook et al. [17] also propose a repair scheduling algorithm to estimate the time required to restore buildings' occupancy or function, considering impeding factors that delay the start of repairs. Molina Hutt et al. [18] use probabilistic modelling of post-earthquake recovery to estimate downtime, a quantitative performance measure used in decision-making. They apply their methodology, which estimates the probability of achieving a target recovery state, in the case of an existing high-rise reinforced concrete building. Birkmann et al. [19] discuss the reconstruction after extreme events, and they draw insights from areas of Germany hit by the 2021 floods. They identify the stages of reconstruction after a disastrous event: 1. emergency response, 2. relief support, 3. debris cleaning, 4. reconstruction of structures and infrastructure, 5. community recovery. Moreover, they recommend long-term policies for resilience based on a build-back-better approach instead of current practice of funding based on the characteristics of the damaged elements. They base their recommendation on the fact that systems are already in a destabilized state even before a disastrous event, e.g. due to high exposure to hazard, multidimensional vulnerabilities, and low risk awareness. Polese et al. [8] use field observations after the 2009 L'Aquila (Italy) earthquake, i.e. reinforced concrete buildings classified as unusable due to earthquake damage, in order to perform logistic regressions to calculate the probability of demolition as a function of different variables. The parameters that have been identified as having the most important effect on the demolition probability are the construction age, the normalized repair cost, and the percentage of the safety level before the damaging earthquake compared to the standard for a new building.

All these studies add a new brick to the complex and multifaceted edifice of recovery process. Our study attempts to provide some qualitative elements of building reconstruction after the Le Teil earthquake, and thus to participate with the other studies in understanding the various stages of the recovery process. Section 2 describes Le Teil's urban dynamics in a multi-hazard context, which is an important prerequisite to understand the distribution of the damages. Section 3 details the actions in the post-seismic rapid response period and Section 4 describes the tasks that have been performed during the five years following the earthquake. Section 5 compares the data obtained from the field studies with predictive damage scenarios, while introducing assumptions for potential reinforcement measures.

## 2. Description of urban dynamics of Le Teil municipality in the multi-hazard context

Le Teil's development began in the 12th century around a castle and grew near the Rhône River, with port activities starting in the 16th century. The 19th century saw industrialization, with the arrival of railroads, the Lafarge factories, and significant urban expansion, particularly in districts like Mélas and Frayol. The population declined from the 1960s to the 1970s due to the decline in rail and industrial activities but grew again after 2008. Geographically constrained by the Rhône River and Vivarais Mountains, the town's growth was limited, with areas like the Sablière district less affected by the earthquake. The town's urbanization reflects both industrial influences and natural limitations. Le Teil currently has 9 districts that have developed over time and are presented by the coloured polygons in the map in Fig. 1: Centre, Croix Rouge, Frayol, Isole, La Rouvière, Laparel, Melas, Sablière, Violette. Appendix 2 describes Le Teil's history and urban dynamics in greater detail.

Considering recent situation form administrative point of view, the town of Le Teil is located in the south-east of the Ardèche "département" (in French, sub-regional administrative unit), on the border with the Drôme département. The town is part of the "Ardèche Rhône Coiron Communauté de Communes", a group of towns, created in 2016. Le Teil's proximity to Montélimar, the second-largest city in the Drôme département, makes it an anchor point in the Ardèche département. Montélimar is also Le Teil's main area of influence, although the two cities do not belong to the same Public Establishments for Intercommunal Cooperation, nor to the same département. In 2014, Ardèche was the least populated and lowest-density département in the Rhône-Alpes region. Nevertheless, it remains an attractive département, with a positive migratory balance [3]. Along with Le Viviers, Le Teil is one of the département's ten main towns. The town of Le Teil had 8,769 inhabitants in 2018 (INSEE, https://www.insee.fr/), its population has been growing
since the late 1990s, mainly due to a positive migratory balance between 2013 and 2018. This balance of around 0.94 % remains in line with the Montélimar Agglomeration (0.96 %) and the neighbouring municipalities (1.03 %). The town of Le Teil faces five natural hazards (https://www.georisques.gouv.fr/) and several technological risks, which are not taken into account in this study. The natural hazards are floods, earthquakes, landslides, clay-shrinkage and underground cavities. As a result, almost all of Le Teil's territory is subject to at least one natural hazard imposing severe constraints on urban development. Only the seismic risk is discussed in detail below.

# 3. Actions in the post-seismic rapid response period

## 3.1. Overview of crisis management organization and timeline

The impact of the earthquake was very significant, affecting communities with extremely large population differences (Le Teil: 8,700 inhabitants; Montélimar: 39,000 inhabitants) in two départements, Drôme and Ardèche. In the Drôme département, damage was much lower than in the Ardèche one, so the situation remained easily manageable. In Ardèche, on the other hand, the damage was so extensive that crisis management was coordinated by the Prefecture, with a particular focus on the commune of Le Teil (the remainder of this section concentrates on crisis management for the town of Le Teil). This part is mainly based on the Perez [5] and AFPS [3,4] description of the organization and chronology of crisis management. They mentioned that the crisis management involved a wide range of actors, including.

- The Prefecture: The mobilization of the Ardèche Prefecture was immediate, as the earthquake was felt directly on the premises of the Prefecture's building. The Prefect went to the area as soon as it could be identified as being severely affected, in order to activate the French civil protection "ORSEC" system, to convene the Departmental Operations Center (COD) under their authority and to open a crisis unit in Le Teil, in cooperation with the mayor's office. While the initial assessment of the earthquake's impact indicated a disaster at departmental level, the extent of the damage was such that departmental capacities were quickly exceeded. A taskforce was therefore requested from the Ministry of the Interior by the Prefecture, in order to increase its coordination capacities.

- The mayor's office and elected representatives: The mayor's office is primarily responsible for safety and the organization of rescue operations. The mayor and elected representatives were in direct contact with the Prefecture throughout the crisis. The communal crisis unit ensured that decisions were taken quickly, and the systematic presence of elected officials enabled rapid relay to the departmental level. Decisions concerning the assistance of affected populations were the responsibility of the mayor's office (creation of reception points, opening of gymnasiums for emergency accommodation, mobilization of campsites for longer-term accommodation, etc.). It also ensured coordination between the many actors involved in its territory (setting up the municipal crisis center at the town hall, involving elected representatives in the crisis unit, accommodating the AFPS emergency coordination unit in its technical services premises to facilitate exchanges, etc.).

- Departmental fire and rescue service (SDIS): During the earthquake, firefighters from the Le Teil rescue center felt the tremors directly, shortly before their switchboard was rapidly saturated with calls from the public. As the tremor was first described as an explosion, the rescue center raised doubts and gave instructions to leave the buildings. The SDIS directed those affected to gymnasiums, and reconnaissance rounds were carried out to identify the areas most at risk. In view of the extent of the damage and the safety issues linked to the stability of the buildings, the COD and SDIS quickly requested the mobilization of building experts, which led to the activation of the AFPS emergency group by the Ministry of the Interior. Given the scale of the needs, the building audits were carried out in parallel on the one hand by AFPS experts (see below), and on the other hand by a team led by the SDIS (later named as the "SDIS team"). This second team was staffed by firefighters coming from all over France, specialized in building hazards and usually acting as "structural experts" on Urban Search and Rescue (USAR) teams. Globally, more than 4,000 building audits were carried out in 19 of the 26 municipalities, with the largest number found in Le Teil, where the AFPS carried out about 600 inspections.

- The AFPS's mechanism for emergency post-seismic building safety inspections, operated under a convention with the Directorate-General for Civil Protection of the French Ministry of Interior: This mechanism meets the need for rapid assessment of the immediate risk to people in damaged buildings. The AFPS has set up an "emergency" system that offers "not only a methodology for carrying out diagnostics on buildings - both individual dwellings and establishments open to the public, but also an organization for efficiently relaying information and training volunteer inspectors." The methodology used to assess the damage to a building, in order to determine whether it is habitable, is based on a diagnosis established according to relevant and homogeneous criteria that take into account the different elements of the building, their materials and their condition; the conclusion of this diagnosis is then indicated at the entrance to the building. If the building can be occupied, this is indicated by the green color. Each diagnosis is carried out by a team of two trained volunteer inspectors. More details about this point and an exhaustive list of all those involved in crisis management at Le Teil, along with their roles and responsibilities, are presented in the AFPS reports (2020; 2021).

## 3.2. Description of the emergency building safety inspections

In AFPS emergency inventories, after a field inspection, each diagnosis assigns a conclusion to a building, symbolized by a color: Green - building inspected, occupancy possible, Yellow - Dangerous building, do not occupy and Red - Risk of collapse, do not enter. In order to reach a color conclusion for a building, 39 items are entered into the AFPS form. These items are broken down into four
categories (or fields).

1. Damage to site and soil (Section A of the form, with 3 items),

2. Damage to neighbouring elements (Section B of the form, with 3 items),

3. Damage to structural elements (Section C, with 26 items),

4. Damage to non-structural elements (Section D, with 7 items).

Each of these items is assigned a color (green, yellow, red) by the inspector. For each category, a color conclusion is entered, based on the color of the item with the highest level of damage for the category in question. The matrix below (Fig. 2) is then used to assign a color to the building. This matrix shows the 15 possible configurations. If a structure, soil or environment is red, the other factors no longer play a part in the decision, and the building is automatically classified as red (see columns 1 to 3 of the matrix). However, it should be noted that a red color for a non-structural element is not decisive in assigning the red color to the building, as shown by configurations 10 and 13. When it comes to assigning the yellow color to a building, a single dimension is enough to qualify it as such (columns 4 to 15, excluding column 12).

## 4. Data collection for a database for the reconstruction process

In order to quantify the reconstruction or renovation of buildings damaged during the Le Teil earthquake, an observation and monitoring method was set up on the basis of existing data and within the time and means limits imposed by the action and by the reconstruction phenomenon itself.

### 4.1. Setting up the follow-up database of this study

Following the earthquake, the AFPS and the firefighters specialized in building risk management, SDIS, intervened in the emergency diagnosis of the buildings of the municipality of Le Teil. Following this diagnosis, the buildings that were inspected were given a color (green, yellow or red) indicating the habitability or not of the building and at the same time an estimate of its damage level. Since the inspection is not systematic for all buildings in the municipality, only buildings for which a request for inspection has been made are displayed in Fig. 3.

A preliminary step of processing and sorting the diagnostic sheets had to be carried out in order to remove any duplications and to check/confirm the location of each building [20]. The database thus contained a set of 922 buildings diagnosed green, yellow and red by the AFPS and the SDIS in the municipality of Le Teil. The meaning of the diagnostics is presented in Table 1 below.

Then, a reference state of the damaged building stock was established from the database of diagnostic sheets of the emergency cell of the AFPS and the firefighters specialized in building risk [5]. This database, which was created by BRGM, in collaboration with AFPS and the SEISM Institute (https://www.institut-seism.fr/), served as a starting point for the collection of field data on the condition of the buildings concerned (repair in progress, completed, or not started).

In order to assess the progress in terms of structural reconstruction, the 922 buildings in the database were visually examined from the outside to associate them with a level of renovation/reconstruction. During the field missions, several states of renovation were distinguished.

* No renovation started;

* Recent renovation visible on the walls;

* Recent apparent renovation on the roof;

* Cracks filled without coating redone;

* Work in progress (presence of scaffolding or construction companies);

* New reconstruction of the building (destruction beforehand).

Fig. 4 shows different intervention types (e.g. tie roads, cracks treatments, roofs interventions ...) at several states of renovation (e. g. work not yet start, work in progress, repair finished) in Le Teil municipality.

This information was structured in a GIS dataset and is corresponding to the Follow-up Database of this study. The data were

<table><caption>Fig. 2. Damage assessment matrix based on the AFPS emergency diagnosis methodology (source: AFPS inspection form).</caption><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th><th>13</th><th>14</th><th>15</th></tr></thead><tbody><tr><td>Damage assessment and decision</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Structural elements</td><td></td><td>R</td><td></td><td></td><td>Y</td><td>Y</td><td>Y</td><td>G</td><td>G</td><td>G</td><td>G</td><td>G</td><td>G</td><td>G</td><td>G</td></tr><tr><td>Soil</td><td></td><td>R</td><td></td><td></td><td>Y</td><td>Y</td><td>G</td><td>G</td><td>Y</td><td>G</td><td>G</td><td>G</td><td>G</td><td>Y</td><td>Y</td><td>Y</td></tr><tr><td>Environment</td><td></td><td></td><td>R</td><td></td><td>Y</td><td>G</td><td>Y</td><td>G</td><td>Y</td><td>Y</td><td>G</td><td>G</td><td>G</td><td>G</td><td>G</td><td>G</td></tr><tr><td>Non-structural elements</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>R</td><td>Y</td><td>G</td><td>R</td><td>Y</td><td>G</td></tr><tr><td>Result</td><td></td><td>R</td><td>R</td><td>R</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td><td>G</td><td>Y</td><td>Y</td><td>Y</td><td>Y</td></tr></tbody></table>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Diagnosed building repartition over the municipality of Le Teil.</figcaption></figure>

<table><caption>**Table 1**
Diagnosed AFPS/SDIS buildings database description.</caption><thead><tr><td>Diagnosis</td><td>Number of inspected buildings in the municipality</td><td>Meaning of the diagnosis</td><td>Indications to follow</td></tr></thead><tbody><tr><td>Green</td><td>401</td><td>Safe building</td><td>Occupation is possible</td></tr><tr><td>Yellow</td><td>272</td><td>Dangerous building</td><td>Do not stay</td></tr><tr><td>Red</td><td>249</td><td>Collapse risk</td><td>Do not enter</td></tr></tbody></table>

collected during annual (Fig. 5) field inspections by filling in a GIS form created for this purpose.

## 4.2. Reconstruction referential

The “build back better” (BBB) approach to disaster recovery was first introduced in 2006 by the United Nations Secretary-General’s Special Envoy for Tsunami Recovery, former US President Clinton. In December 2006, on the second anniversary of the 2004 Indian Ocean Tsunami, the United Nations Secretary-General’s Special Envoy for Tsunami Recovery issued a report titled “Key Propositions for Building Back Better” [21]. Since this initiative, several research teams (e.g., Ref. [22]) are continuously working on the recovery and reconstruction procedures and practical guidelines. Moatty [23] have noted that post-disaster reconstruction is not treated as such in France, and even internationally, at least for the richest countries. The proposed reference for the observation of reconstruction and its associated indicators aims to follow its dynamics over time. Sets of physical and urban-building indicators have been identified in various studies (e.g., Ref. [23]; Build Back Better) to provide quantitative information that evolves over time; we have chosen two building-related indicators (see Table 2).

* Number of dangerous structure notices issued. Each notice is for one dwelling. They are mainly the result of emergency diagnostics. Notices are lifted when the owner can provide proof that the building has undergone consolidation and safety work. The lifting of the dangerous structure notice means that the danger has been averted, but does not mean that the building will be fully renovated.
<figure><img src="image_6.png" /><figcaption>Fig. 4. Different types of intervention at different stages of completion in the commune of Le Teil.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Timetable of the reconstruction follow-up.</figcaption></figure>

<table><caption>**Table 2**

Physical indicators evolution as a function of time (D: time of the earthquake).</caption><thead><tr><th>Indicators</th><th>References (D)</th><th>D+2 years</th><th>D+3 years</th><th>D+4 years</th></tr></thead><tbody><tr><td>Actual number of dangerous structure notices issued</td><td>850 dangerous structure notices issued following the earthquake</td><td>400</td><td>332</td><td>273</td></tr><tr><td>Number of buildings having undergone work</td><td>Number of buildings in the diagnosis database: 922 buildings</td><td>395</td><td>543</td><td>844</td></tr></tbody></table>

Over the years, the number of dangerous structure notices issued has become the number of dangerous structure notices lifted, meaning that the danger has been averted.

* Number of buildings under construction or repair (Number of buildings having undergone work). The GIS database (from an AFPS/ BRGM/SEISM Institute collaboration) of building diagnostics contains most of the buildings for which a visible impact of the earthquake has been reported.

These indicators are associated with each specific building in the monitoring database. The database can therefore be used to check
whether there are differences in these indicators according to building characteristics, class, color label assigned by the inspection, district, etc. This is done in Section 4.3 and Section 4.4.

For the population indicator, at the municipality level, the evolution of the population is measured by the national census. This is a standard procedure updated every five-year. Data are available for each census. The two censuses framing the date of the earthquake are 2019 and 2023. An interview with the mayor of Le Teil in October 2022 raised the possibility of obtaining data from primary schools, as originally envisaged. During 2022, in parallel with the monitoring of the building reconstruction, this action also resulted in a more in-depth analysis of the urban planning component [6] and of the perception of the population [7] in connection with this reconstruction, as a multi-thematic analysis of the reconstruction phase. Many factors influencing the reconstruction dynamics are not accessible to the observatory, such as insurance data for example. This is not the object of this paper.

### 4.3. Field observations and monitoring

Several phases of field work have followed since the study began in 2021. A summary of the various missions and actions carried out is presented in Table 3 and in Fig. 5. It is important to note that the method of observing the reconstruction of buildings from street level does not allow to evaluate the reconstruction/renovation of buildings that present only interior damage. This bias should be considered, especially with regards to the diagnoses categorized in «green», which are highly likely to present only interior damage.

Based on data collected during 2021, 2022 and 2023, an analysis of the evolution of reconstruction at D+2, D+3 and D+4 years is presented in the next section.

### 4.4. Reconstruction state D+2 years, D+3 years and D+4 years

Figs. 6–8 show the evolution of the reconstruction of buildings after 2, 3 and 4 years (noted D+2, D+3 and D+4 respectively). Fig. 6 shows the differences between the structural retrofitting and cosmetics interventions, while Fig. 7 details the distribution of all type of repair in the municipality of Le Teil.

It appears from a first analysis of the results at D+2, that about 40 % of the studied buildings that had been significantly impacted by the earthquake were already repaired or undergoing repairs two years after the earthquake. These are mostly individual houses (Fig. 6). The remaining 60 % of buildings often have complex configurations: joint ownership, joint occupancy, adjacent structures, etc.

The assessment of the reconstruction of the municipality at D+3 reveals a renovation of the municipality that keeps on progressing. It may sometimes be complicated by long procedures, but it does not remain frozen in time (almost 60 % of the buildings were renovated or under renovation, knowing that the buildings diagnosed in green and examined in 2021 were yet been re-observed). The evolution between 2021 and 2022 is not very significant (only 148 additional buildings renovated or under renovation in one year). This can be explained by the fact that the first years after an earthquake are more conducive to the renovation of buildings (simple, fast processing works, with no bottlenecks for construction companies, etc.) and that, in the following years, the longer works will gradually be completed.

Figs. 6–8 also show that at D + 4 years, around 90 % of buildings have undergone technical interventions, either to bring them back to the same level of resistance as before the earthquake, or to reinforce them.

If we look in more detail at the distribution of the types of repairs at the scale of the municipality of Le Teil at D + 3 years (Fig. 7), most of the reconstructions carried out involve recent renovations for the walls (367 buildings out of 543 buildings rebuilt or under reconstruction in the municipality). 87 buildings have filled cracks. 41 buildings have been renovated at roof level and the remaining buildings are under reconstruction or have been completely destroyed for a complete reconstruction of the building. An analysis of the construction type of renovated buildings or not shows that there was no prioritization of renovation according to the construction type of damaged building (stone, old masonry, recent masonry, concrete ...). However, the dynamics of the compensation procedure

<caption>**Table 3**</caption>

Field inventories to assess the reconstruction/renovation of buildings in the Follow-up Database.

<table><thead><tr><td>Time of the inventory</td><td>Year (D: year of the earthquake)</td><td>Type of buildings evaluated</td><td>Other actions</td></tr></thead><tbody><tr><td>29 Nov. - 3 Dec. 2021</td><td>D+2 years</td><td>Buildings diagnosed as red and yellow</td><td>Meetings with the crisis management and reconstruction stakeholders<br>Evaluation of the seismic vulnerability of housing buildings</td></tr><tr><td>7 March - 10 March 2022</td><td>D+2 years</td><td>Buildings diagnosed as green</td><td>Meetings with the crisis management and reconstruction stakeholders<br>Field study towards the population regarding the reconstruction phase</td></tr><tr><td>26 Oct. - 28 Oct. 2022</td><td>D+3 years</td><td>Building diagnosed as red and yellow with no renovation in 2022</td><td>Meetings with the crisis management and reconstruction stakeholders</td></tr><tr><td>22 Nov. - 24 Nov. 2023</td><td>D+3 years</td><td>Building diagnosed as red and yellow with no renovation in 2023</td><td>Meetings with the crisis management and reconstruction stakeholders</td></tr><tr><td>9 Apr. - 11 Apr. 2024</td><td>D+4 years</td><td>Monitoring the lifting or maintenance of initial dangerous structure notices</td><td>Collecting data on reinforcement methods for numerical modelling purposes</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Fig. 6. Distribution of repairs in the municipality of Le Teil by type of repair up to D + 4 years (D is the day of the earthquake).</figcaption></figure>

Distribution of repairs in the municipality of Le Teil

<figure><img src="image_9.png" /><figcaption>Fig. 7. Distribution of repairs in the municipality of Le Teil by type of repair D + 4 years after the earthquake.</figcaption></figure>

suggests that the progress of renovation is closely linked to the complexity of the cases, the multiplicity of insurance companies involved, the expertise, and the availability of construction companies [24,25]. Considering current efforts to model the reconstruction and the resilience of urban areas, the development of models based on the data, such as these in Figs. 6 and 8, should take into account the time lag between the damaging seismic event and the beginning of the reconstruction. This would translate into models that take values higher than zero only after the time D + L (L is the lag). In the case of Le Teil earthquake, the reconstruction started significantly two years after the earthquake. This is explained by the heavy administrative procedure for insurance fees but also, in this case, by the Covid-19 pandemic.

Fig. 7 suggests that a percentage of the damaged buildings were rebuilt identically since a large number of buildings have just undergone repairs with filled cracks and cosmetics works. Some renovated buildings have been reinforced with tie rods or seismic techniques recommended for retrofitting.

Finally, Fig. 8 seems to conclude that there has been no prioritization of work related to coloured markers, and therefore to the
<figure><img src="image_10.png" /><figcaption>Fig. 8. Evolution of percentage of buildings renovated or in progress corresponding to each colour tag.</figcaption></figure>

severity of the structure's stability, since the number of interventions is fairly similar each year between red and yellow markers.

## 4.5. Assessment of the typological classes and level of vulnerability of buildings in Le Teil

In order to calculate the potential damage suffered by the municipality's building stock (residential buildings only), a vulnerability assessment was carried out beforehand, using the RISK-UE approach [26–28] implemented by BRGM in many areas [29]. Buildings' statistics were available at the level of the town's districts. A few adjustments were made to the boundaries of these districts, in particular to account for the different types and extent of site effects. Once the buildings were grouped into blocks of "homogeneous" vulnerability, a specific typology was established. This typology was established following a preview of the study area via Google Earth Explorer. This work was corrected or validated during field inspection missions. The following building types, described with photos in Appendix 1, were selected.

### Type 1: Stone buildings

* Type 1i: Stone building, isolated, old (built before 1970s)

* Type 1m: Old, adjoining stone building (built before 1970)

* Type 1mc: Old, adjoining stone building (built before 1970s) with ground-floor shops

### Type 2: Unreinforced masonry buildings

* Type 2i: Unreinforced, isolated, old masonry building (built before the 1970s)

* Type 2m: Masonry building, unreinforced, adjoining, old (built before the 1970s)

* Type 2mc: Old, unreinforced, adjoining masonry building (built before the 1970s) with ground-floor shops

### Type 3: Reinforced masonry buildings

* Type 3i: Reinforced masonry building, isolated, relatively recent (built between 1970s and 2000s)

* Type 3m: masonry building, linked, adjoining, relatively recent (built between the 1970s and 2000s)

* Type 3mc: Masonry, chain-linked, adjoining, relatively recent building (built between 1970 and 2000) with shops on the ground floor.

* Type 3ir: Masonry building, linked, detached, recent (built after 2000s)

* Type 3mr: Masonry building, chained, adjoining, recent (built after 2000)

* Type 3mrc: masonry building, chained, adjoining, recent (built after 2000), with shops on first floor.

### Type 4: Concrete wall buildings

* Type 4i: relatively recent (built between the 1970s–1990s)

* Type 4r: Building with cast concrete walls, recent (built after the 1990)

### Type 5: RC buildings

* Type 5i: RC with infilled masonry Building, relatively recent (built 1970s–1990s)

* Type 5r: RC with infilled masonry, recent (built after the 1990s)

where the indices “i”, “m”, “c” and “r” stand for “isolated”, “adjoining (in French - mitoyen)”, “commercial”, and “recent”, respectively.

The 1970s saw the advent of the first construction standards for common buildings in France (DTU standards, CCBA68 rules),
which notably involved design or the implementation of constructive measures that are favourable in the event of an earthquake, such as wall ties, even if seismic regulations at that time did not apply to the structures in question. The year 1990 was chosen as the pivotal date for the application of earthquake-resistant rules to standard buildings (excluding single-family homes), with the PS69 and then PS92 rules, and the associated seismic zonings including the town of Le Teil in zones of significant seismicity. Finally, the year 2000 was chosen as the date from which the application of seismic-resistant construction rules became generalized to single-family homes, via the simplified PSMI 89 rules. It should be stressed that identifying the year of construction of a building by visual observation is subjective, so it was not possible to refine the time ranges by taking into account other regulatory developments. In the end, the corresponding building stock must be sufficiently numerous to obtain statistically meaningful distributions.

For risk scenarios purposes, it was then necessary to associate a level of vulnerability with each sub-category. The vulnerability index ($V_i$) is introduced by the RISK-UE approach to represent and quantify a building's membership to a certain vulnerability class. The values of the vulnerability index range from 0 to 1 (1 being the most vulnerable), and are scores that quantify the building's seismic behaviour in a conventional way. As the vulnerability index of the RISK-UE method [27] have been developed for European buildings, it was possible to use them directly to estimate the vulnerability of the different typologies. The vulnerability index ($V_i$) associated with each type is composed according to the RISK-UE classification and is presented in Appendix 1. The categorization of Le Teil's buildings into typological classes was also useful for interpreting the data information, considering this parameter (see Fig. 9).

Fig. 9 shows that the most impacted class is the stone masonry buildings built before 1970 ($T1_m$). Fig. 9 presents only the typologies that had yellow and red tags during the emergency inspection. The typologies that are not in this Figure correspond to green diagnoses or no diagnoses at all.

Finally, Fig. 10 presents the distribution of percentage of red and yellow tags corresponding to each of the districts (Fig. 1) of the Municipality of Le Teil. The nine districts, which are represented geographically in Fig. 1, are presented here in ascending order of the percentage of red labels, i.e. from the least to the most damaged districts. We can notice that the most impacted districts are the districts situated in the South of the Municipality, i.e. Violette and La Rouvrière. If we add the percentages of red and yellow labels, almost one building in three in these neighbourhoods was in the "do not occupy" or "do not enter" situation. This can be explained by two factors, firstly by the proximity to the epicentre (see Fig. 1) and secondly, by the location of the soil classes that can amplify strong motions (see Fig. 11 in the next Section). Apart from these two districts, the other most damaged districts are Melas, Centre and Frayol. The Centre district is the oldest part of the city, but some buildings have been rebuilt more recently. The most representative typology in the Centre district is $T1_m$, i.e. small, long, stone masonry buildings. Mélas and Frayol were part of the urban expansion of the town at the same time as the arrival of the railway station in 1876. The Mélas district is one of the least modified since it was built, which explains the very old and vulnerable buildings. The explanation for this extensive damage in these three districts lies in the high vulnerability of the buildings, but perhaps also in the site response, as described in the next Section. The Sablière and Isolé districts suffered little or no damage, which was to be expected since they are the most recent districts built on rock or hard soil types.

<figure><img src="image_11.png" /><figcaption>Fig. 9. Distribution of the percentages of red and yellow tags according to building type in the town of Le Teil.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Distribution of the percentages of red and yellow tags according to the districts in the town of Le Teil, and considering data at Municipality level (last bar chart).</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Soil classes and amplification coefficients in Le Teil Municipality.</figcaption></figure>

# 5. Observed damage analysis and risk scenarios for the reconstructed city

## 5.1. Building damage analysis with respect to soil classes

To investigate the influence of site effects (Fig. 11) on damage levels, we examine the proportion of damaged buildings to the total number of buildings in each zone with the same site effect. The site effect map is provided by Monfort and Roulle [30] who carried out a statistical analysis of the distribution of soil classes defined by Eurocode 8 (A, B, C, ...) in low, moderate and high seismicity zones in
France. Table 4 presents the ratio between the number of inspected building and the total number of existing buildings for each site effect zone.

Soil class A, which shows no amplification, is the most represented soil class here. Only 15 % of residential buildings in this soil class have undergone post-seismic emergency diagnoses. Soil class B, which has a low amplification (1.35), is very rarely found in Le Teil. This soil class covers very little of the inhabited area. It mainly covers the "Sablière" district, which consists mainly of recent buildings, and a small part of the "Isolé" district, which contains very few buildings. Less than 1 % of the residential buildings included in this soil class have been subjected to a post-seismic emergency building diagnosis, however the number of buildings belonging to this soil class may not be statistically significant. Soil class B/C, with a slightly higher amplification than the previous class (1.43), is the second least represented soil class. However, around 40 % of residential buildings in this soil class have undergone post-seismic emergency building diagnoses. Soil class C, with the highest amplification in the Le Teil (1.5), covers a large part of the built-up area of Le Teil. With almost half of residential buildings having undergone a post-seismic emergency building diagnosis, this is the soil class with the highest ratio of "Number of diagnoses" to "Number of buildings". Finally, if we disregard the ratio for soil class B, which is farthest from the epicenter (Fig. 11), covers very few residential buildings, and which only concerns recent buildings with low seismic vulnerability, we can observe a consistency between the amount of damages and the level of amplification (the greater the soil amplification, the greater the building damage). However, even if this is clearly observed for this situation, it must be taken with caution, as these findings can in no way draw a definitive conclusion without more a detailed analysis of the soil response. In any case, this observation is in line with other studies on the subject (e.g., Ref. [31]).

## 5.2. Predictive damage scenarios and assumptions on the level of building reinforcement

Collecting data on the vulnerability of buildings and their apparent state of reconstruction or repair provides an overall view of the inspected building stock. This allows us to better constrain the elements of comparison for the correspondence between colour tags assigned by the AFPS/SDIS inspections and EMS-98 damage grades [32], by comparing the results of damage scenarios and the data collected on the field. In addition, the identification of a building type for each entry in the database enables a comparative analysis of the damage observed depending on the building type. This is a unique opportunity to test the Risk-UE method on buildings in a city located in a moderate seismicity zone. The interest of using scenarios is to study the various factors that could influence the area's response in the future, if a similar earthquake were to occur again. By comparing the effects of the 2019 earthquake, it is possible to identify strategies for improvement (risk reduction), or weigh up actions specifically aimed at building vulnerability against the impact of factors linked to the future development of the town (urban expansion, renewal of the housing stock, etc.).

For damage estimation we used the seismic risk analysis tool Armagedom [29], which runs on the VIGIRISKS platform [33]. The Armagedom tool implements the semi-empirical Macroseismic method developed by the RISK-UE project (Lagomarsino and Giovignazzi, 2006). This semi-empirical Macroseismic method calculates the mean EMS-98 damage grade as a function of the Macroseismic intensity and the vulnerability index (Vi). Vulnerability index represents the seismic behaviour of the building and ranges between 0 and 1. These indices, that are presented in Appendix 1, have been assigned to building classes in the exposure model. Subsequently, the semi-empirical Macroseismic method applied in Armagedom assumes a beta distribution to calculate the probabilities of exceeding the EMS-98 damage grades (D1 to D5) as a function of Macroseismic intensity.

We note that the seismic source parameters needed to produce the scenario have a strong influence on the outcome. Depending on the institute delivering source parameters, the location of the epicenter of the earthquake varies significantly, especially if we are interested in the effects in the epicentral area, which is the case for Le Teil municipality. This was not the scope of this paper. For more details on this part, the authors strongly recommend the study by Trevlopoulos et al. [34], which use a dataset based on emergency post-seismic assessments made after the Le Teil 2019 earthquake, third-party estimations of Macroseismic intensity for this seismic event, shake maps, and scenario damage calculations to compare estimations under different modelling assumptions.

For the damage scenario in this paper, we do not make parametrisation but we try to choose a set of parameters in order to have the best match with the observed Macroseismic intensity map produced by Schlupp et al. [35]. Schlupp et al. [35] reported an EMS-98 Macroseismic intensity of 7–8 for the municipality of Le Teil. This conclusion was the product of expert judgement considering the EMS-98 definitions of the intensity degrees and damage grades, the field observations from the Macroseismic Response Group, and the EMS-98 vulnerability classes of the buildings based on land registration data.

At this purpose, the damage scenario consists of.

* Selection of earthquake parameters (location, magnitude, depth): BCSF-RéNass (44.53, 4.64); Mw = 4,9; depth 2 km;

* Calculating the ground acceleration map for the reference soil (rock) using empirical propagation laws (ground-motion prediction equations - GMPE): Atkinson and Boore [36]; Akkar and Bommer [37] and Chiou and Young [38];

<table><caption>**Table 4**
Ratio between the numbers of damaged building to the number of total buildings for each site effect zone.</caption><thead><tr><td>Soil Class</td><td>Amplification coef.</td><td>Number of inspected buildings</td><td>Total number of buildings</td><td>Ratio number of inspected/total number of buildings</td></tr></thead><tbody><tr><td>Class A</td><td>1.00</td><td>157</td><td>1038</td><td>0.15</td></tr><tr><td>Class B</td><td>1.35</td><td>2</td><td>53</td><td>0.04</td></tr><tr><td>Class B/C</td><td>1.43</td><td>146</td><td>356</td><td>0.41</td></tr><tr><td>Class C</td><td>1.50</td><td>624</td><td>1332</td><td>0.47</td></tr></tbody></table>
* Taking into account site effects leading to local lithological amplification of seismic motion, on the basis of a site effects map (specific zoning associated with amplification values presented in Fig. 11);

* Converting ground acceleration into intensity: Atkinson and Sonley [39];

* Describing the building exposure by specifying the distribution of building types and their associated vulnerability index (Appendix 1);

* Using Macroseismic intensity and vulnerability index, to calculate statistical damage levels (number of damaged buildings for a given damage level, or percentage of damaged buildings) according to the damage classification given by the EMS98 macroseismic scale, based on RISK-UE approach.

Once we obtained the simulated Macroseismic intensity which fit with the observed values, we did not change these values afterwards, for Sections 5.2.1 and 5.2.2.

For Section 5.2.1, we calculated simulated damage grades (EMS-98, D1 to D5) using simulated Macroseismic intensity and the distribution of building types and their associated vulnerability index, and compared them with observed damage (from the field investigations after the earthquake).

For Section 5.2.2, we performed the same calculation, but assumed that the building's seismic resistance is improved after repair. In practice, this means that the vulnerability index is modified (reduced), e.g. buildings that were classified as unreinforced masonry may be classified as reinforced masonry. The assumptions and limitations of this approach are detailed in Section 5.2.2.

### 5.2.1. Comparison between simulated damage (from damage scenarios) and actual building damage (from field surveys)

Table 5 gives the percentage of different damage levels for BCSF-RéNass [40] scenario, which is subsequently compared with the observed damage. The columns with the headers "%D0-D1", "nD ≥ 2", "nD ≥ 3", "%nD ≥ 2" and "%nD ≥ 3" contain approximate values of the proportion of buildings affected. In the following, buildings for which a damage level greater than or equal to D2 (cracks in structural elements) is estimated are considered to be damaged. Buildings with a damage level equal to or greater than D3 are considered requiring to be evacuated. In the table below, around 70 % of the buildings in Le Teil are affected by damage levels D0-D1, i. e. no damage or negligible damage. This means that the remaining 30 % of the municipality are affected by damage levels of at least D2, i.e. from moderate damage to total destruction of the building.

The number of buildings diagnosed as green/yellow/red by the AFPS and SDIS was 922 (Table 6): 401 buildings diagnosed as green and 521 buildings diagnosed as yellow/red. The proportion of buildings diagnosed as green/yellow/red in Le Teil following the earthquake is around 33 %.

As mentioned in several publications [5,34,41,42] the correspondence between the colour tags assigned by emergency diagnosis teams such as the one of AFPS and the damage levels defined by the EMS98 scale is a challenging task, for which there is no widely accepted solution. Nevertheless, we have attempted to interpret this correspondence for the Le Teil data set. As previously mentioned, the signification of the results of the diagnostics carried out by AFPS is as follows (source: AFPS website, explanations of the method and instructions to be followed by the experts).

* Green Diagnosis - occupancy is possible/no apparent danger noted;

* Yellow diagnosis - do not remain inside/degraded building which could present a danger to its occupants;

* Red diagnosis - do not enter, risk of collapse/very degraded building, which could collapse in whole or in part.

On the other hand, the damage level descriptions in the EMS98 scale are.

* D1: Negligible to slight damage (no structural damage, slight non-structural damage);

* D2: Moderate damage (slight structural damage, moderate non-structural damage);

* D3: Substantial to heavy damage (moderate structural damage, heavy non-structural damage);

<caption>**Table 5**</caption>

Percentage of damage levels calculated for different scenarios %D0-D1: rounded estimate of the percentage of buildings with no or very little damage; nD ≥ 2: rounded estimate of the number of buildings with damage levels above D2 (included); %nD ≥ 2: rounded estimate of the percentage of buildings with damage levels above D2 (included); nD ≥ 3: rounded estimate of the number of buildings with damage levels above D3 (included); % nD ≥ 3: rounded estimate of the percentage of buildings with damage levels above D3 (included).

<table><thead><tr><td>Scenario</td><td>Number of buildings</td><td>%D0-D1</td><td>nD≥2</td><td>%nD≥2</td><td>nD≥3</td><td>%nD≥3</td></tr></thead><tbody><tr><td>CENTRE</td><td>637</td><td>70 %</td><td>200</td><td>30 %</td><td>60</td><td>10 %</td></tr><tr><td>CROIX ROUGE</td><td>311</td><td>60 %</td><td>120</td><td>40 %</td><td>40</td><td>15 %</td></tr><tr><td>FRAYOL</td><td>392</td><td>65 %</td><td>140</td><td>35 %</td><td>50</td><td>15 %</td></tr><tr><td>ISOLE</td><td>21</td><td>65 %</td><td>8</td><td>35 %</td><td>&lt;5</td><td>10 %</td></tr><tr><td>LA ROUVIERE</td><td>169</td><td>60 %</td><td>70</td><td>40 %</td><td>25</td><td>15 %</td></tr><tr><td>LAPAREL</td><td>121</td><td>75 %</td><td>30</td><td>25 %</td><td>8</td><td>5 %</td></tr><tr><td>MELAS</td><td>417</td><td>70 %</td><td>115</td><td>25 %</td><td>40</td><td>10 %</td></tr><tr><td>SABLIERE</td><td>509</td><td>95 %</td><td>30</td><td>5 %</td><td>5</td><td>&lt;5 %</td></tr><tr><td>VIOLETTE</td><td>198</td><td>70 %</td><td>60</td><td>30 %</td><td>20</td><td>10 %</td></tr><tr><td>Entire town</td><td>2775</td><td>Approx. 70 %</td><td>770</td><td>28 %</td><td>250</td><td>10 %</td></tr></tbody></table>
<table><caption>**Table 6**
Observed building damage caused by the 2019 Le Teil earthquake based on the SDIS and AFPS field surveys.</caption><thead><tr><th>Scenario</th><th>Number of buildings</th><th>Without inspection or green tag</th><th>% Without inspection or green tag</th><th>Yellow tag</th><th>% Yellow tag</th><th>Red tag</th><th>% Red tag</th></tr></thead><tbody><tr><td>CENTRE</td><td>637</td><td>487</td><td>76 %</td><td>83</td><td>13 %</td><td>67</td><td>11 %</td></tr><tr><td>CROIX ROUGE</td><td>311</td><td>236</td><td>76 %</td><td>51</td><td>16 %</td><td>24</td><td>8 %</td></tr><tr><td>FRAYOL</td><td>392</td><td>297</td><td>76 %</td><td>48</td><td>12 %</td><td>47</td><td>12 %</td></tr><tr><td>ISOLE</td><td>21</td><td>20</td><td>95 %</td><td>0</td><td>0 %</td><td>1</td><td>5 %</td></tr><tr><td>LA ROUVIERE</td><td>169</td><td>116</td><td>69 %</td><td>18</td><td>11 %</td><td>35</td><td>21 %</td></tr><tr><td>LAPAREL</td><td>121</td><td>110</td><td>91 %</td><td>4</td><td>3 %</td><td>7</td><td>6 %</td></tr><tr><td>MELAS</td><td>417</td><td>339</td><td>81 %</td><td>38</td><td>9 %</td><td>40</td><td>10 %</td></tr><tr><td>SABLIERE</td><td>509</td><td>504</td><td>99 %</td><td>5</td><td>1 %</td><td>0</td><td>0 %</td></tr><tr><td>VIOLETTE</td><td>198</td><td>145</td><td>73 %</td><td>25</td><td>13 %</td><td>28</td><td>14 %</td></tr><tr><td>Entire town</td><td>2775</td><td>2254</td><td>81 %</td><td>272</td><td>10 %</td><td>249</td><td>9 %</td></tr></tbody></table>

* D4: Very heavy damage (heavy structural damage, very heavy non-structural damage);

* D5: Destruction (very heavy structural damage).

With this description in mind, from a structural point of view, damage levels D1, D2, D4 and D5 can be fairly easily associated with the colors of the tags assigned by the inspections. Only damage level D3 seems to fall between two colors: yellow and red diagnoses. Indeed, D3 damage levels indicate buildings with sensitive to severe damage. However, yellow diagnoses reflect moderate damage, a term that can be interpreted as slightly less important than "sensitive damage", and red diagnoses reflect severe damage, the type of damage cited in the second part of the D3 damage description. Two probable hypotheses are finally considered, as presented in Table 7. For the first hypothesis, green diagnoses include D0 and D1 damage levels, yellow diagnoses include D2 damage level and half of D3 damage level, and red diagnoses include half of D3, D4 and D5 damage levels. For the second hypothesis, green diagnoses include D0 and D1 damage levels, yellow diagnoses include D2 damage levels, and red diagnoses include D3, D4 and D5 damage levels.

At municipality level, the results are fairly conclusive (Table 8), with around 5 % difference between undiagnosed buildings, green AFPS/SDIS diagnoses and damage levels D0 and D1 (no damage, negligible to slight damage). The largest difference at the municipality level is about 10 % difference between yellow diagnoses and damage levels D2+1/2D3 for the 1st hypothesis. This difference is smaller, at around 5 % for the 2nd hypothesis. We can therefore conclude that, at town level, the distribution of damage resulting from the simulation is fairly close to reality.

On the other hand, when we look at a finer scale, i.e. the distribution of damages at district level, we find larger discrepancies (Table 8). The majority of districts show differences close to 10 %. The most glaring discrepancies are in the "Croix-Rouge" and "Isolé" neighbourhoods, with 15 % and 24 % respectively for non-existent, green diagnoses and D0, D1 damage. For red diagnoses and D3, D4 and D5 damage, the greatest difference is 14 % in the La Rouvière district, considering the 1st hypothesis. This percentage is decreasing at around 8 % for the 2nd hypothesis. For yellow diagnoses and D2 or D2 and ½ D3 damage, the most significant differences are found in the "Isolé" and "La Rouvière" districts, with 24 % and 18 % respectively for the 1st hypothesis, and 19 % and 13 % respectively for the 2nd hypothesis. Based on these numbers, we can say that the 2nd hypothesis has fewer differences with the observed damages that the 1st hypothesis. Overall, the simulated damage results are quite higher than the observations for the most significant damages (D2 and above).

### 5.2.2. Assumptions on the level of building reinforcement

In order to evaluate assumptions considering the level of building reinforcement, we define three different scenario parameters that are tested.

* Scenario based on the 2019 seismic event, with *buildings rebuilt as is*;

* Scenario based on the 2019 seismic event, with *reconstruction to seismic standards of completely destroyed buildings and buildings diagnosed as red*;

* Scenario based on the 2019 seismic event, with *reconstruction to seismic standards of completely destroyed buildings, buildings diagnosed as red and buildings diagnosed as yellow*.

The reconstruction hypothesis based on current seismic construction standards comes from the following rationale.

<table><caption>**Table 7**
Hypotheses for the correspondence between color tags of the diagnoses carried out by SDIS and AFPS field surveys and the EMS-98 damage levels (D0 is the damage levels which is implied by the EMS-98 for no structural and no non-structural damage).</caption><thead><tr><th>Hypothesis</th><th>Color tags of the diagnoses</th><th>Green</th><th>Yellow</th><th>Red</th></tr></thead><tbody><tr><td>1st Hypothesis</td><td>EMS-98 damage grade</td><td>D0, D1</td><td>D2, 50 % of D3</td><td>50 % of D3, D4, D5</td></tr><tr><td>2nd Hypothesis</td><td>EMS-98 damage grade</td><td>D0, D1</td><td>D2</td><td>D3, D4, D5</td></tr></tbody></table>
<caption>**Table 8**</caption>

Comparison expressed in terms of percentage of buildings between simulated damage (from damage scenarios) and actual building damage (from field surveys) – 1st and 2nd hypotheses.

<table><thead><tr><th rowspan="2">Districts</th><th colspan="3">Color tags</th><th colspan="4">EMS-98 (1st hypothesis)</th><th colspan="3">EMS-98 (2nd hypothesis)</th></tr><tr><th>No diag + Green</th><th>Yellow</th><th>Red</th><th>D0+D1</th><th>D2+1/2 D3</th><th>1/2 D3+D4+D5</th><th>D0+D1</th><th>D2</th><th>D3+D4+D5</th></tr></thead><tbody><tr><td>CENTRE</td><td>76.5 %</td><td>13.0 %</td><td>10.5 %</td><td>77.2 %</td><td>19.6 %</td><td>3.1 %</td><td>77.2 %</td><td>17.3 %</td><td>5.5 %</td></tr><tr><td>CROIX ROUGE</td><td>75.9 %</td><td>16.4 %</td><td>7.7 %</td><td>61.1 %</td><td>30.5 %</td><td>8.4 %</td><td>61.1 %</td><td>25.1 %</td><td>13.8 %</td></tr><tr><td>FRAYOL</td><td>75.8 %</td><td>12.2 %</td><td>12.0 %</td><td>69.9 %</td><td>24.0 %</td><td>6.1 %</td><td>69.9 %</td><td>19.9 %</td><td>10.2 %</td></tr><tr><td>ISOLE</td><td>95.2 %</td><td>0.0 %</td><td>4.8 %</td><td>71.4 %</td><td>23.8 %</td><td>4.8 %</td><td>71.4 %</td><td>19.0 %</td><td>9.5 %</td></tr><tr><td>LA ROUVIERE</td><td>68.6 %</td><td>10.7 %</td><td>20.7 %</td><td>64.5 %</td><td>28.4 %</td><td>7.1 %</td><td>64.5 %</td><td>23.7 %</td><td>11.8 %</td></tr><tr><td>LAPAREL</td><td>90.9 %</td><td>3.3 %</td><td>5.8 %</td><td>86.8 %</td><td>11.6 %</td><td>1.7 %</td><td>86.8 %</td><td>10.7 %</td><td>2.5 %</td></tr><tr><td>MELAS</td><td>81.3 %</td><td>9.1 %</td><td>9.6 %</td><td>74.8 %</td><td>20.6 %</td><td>4.6 %</td><td>74.8 %</td><td>17.5 %</td><td>7.7 %</td></tr><tr><td>SABLIERE</td><td>99.0 %</td><td>1.0 %</td><td>0.0 %</td><td>94.7 %</td><td>4.9 %</td><td>0.4 %</td><td>94.7 %</td><td>4.5 %</td><td>0.8 %</td></tr><tr><td>VIOLETTE</td><td>73.2 %</td><td>12.6 %</td><td>14.1 %</td><td>70.7 %</td><td>22.7 %</td><td>6.6 %</td><td>70.7 %</td><td>18.7 %</td><td>10.6 %</td></tr><tr><td>Entire town</td><td>81.2 %</td><td>9.8 %</td><td>9.0 %</td><td>76.4 %</td><td>19.4 %</td><td>4.3 %</td><td>76.4 %</td><td>16.4 %</td><td>7.2 %</td></tr></tbody></table>

* Red diagnoses mainly concern buildings which primary structure (within the meaning of Eurocode 8) is seriously damaged;

* Yellow diagnoses also relate to a damaged primary structure, less heavily that the red ones, but still requiring intervention on the structure;

* Intervention on the primary structure (here for repair) is one of the cases of potentially aggravating work within the meaning of the decree of October 22, 2010 (article 3 paragraph 3, JORF 2010 [43]).

Strict application of the regulations requires verification that the work does not increase the vulnerability of existing buildings, that is to say that the repaired buildings have a seismic performance at least equal to that before the earthquake. The simulation hypothesis chosen goes beyond these requirements, considering an “obligation” to respect the seismic rules of new construction for these structures, in order to significantly improve their behaviour. In certain cases, the necessary reinforcement work could be expensive, and would go beyond repairing the damaged elements alone. It should also be noted that the regulations leave it to the project owner to set their performance objective in the event of seismic reinforcement. Therefore, there is theoretically no obligation to comply with the seismic performance level that would be expected for a new building. The hypothesis adopted here may seem logical at first glance (“since we are doing some major repairs, let us try to improve the situation”), but it should be kept in mind that it goes well beyond prescriptive requirements. However, it allows us to simulate the idea of seizing the opportunity of reconstruction to reduce the vulnerability of the building stock. The economic implications of such a choice would merit a specific examination, which goes beyond the scope of this study.

The number of buildings affected by a red diagnosis is 250 (around 9 % of the stock) and those affected by a yellow diagnosis is 270 (around 10 % of the stock). For these buildings, the Risk-UE vulnerability index (Milutinovic, 2003; Lagomarsino, 2002) is modified, considering the values of Appendix 1, as follows.

* Without seismic provision: the vulnerability index remains the same depending on the typology of the building;

* With seismic reconstruction: all buildings concerned by the types T1 (stone: T1ᵢ = 0.76; T1ₘ = 0.88; T1ₘc = 0.92), T2 (unreinforced masonry: T2ᵢ = 0.74; T2ₘ = 0.8; T2ₘc = 0.84) and non-recent T3 (old reinforced masonry: T3ᵢ = 0.62; T3ₘ = 0.66; T3ₘc = 0.7) are assigned the building vulnerability index T3ᵢr, T3ₘr or T3ₘc (recent reinforced masonry) or T4, T4ᵣ (concrete), depending on whether they are isolated, adjoining or adjoining with commercial function on the ground floor; namely the respective vulnerability indices 0.31, 0.35 or 0.39 for buildings T3 and 0.41 and 0.24 for T4 buildings, respectively. These values corresponding to the building types are presented in Appendix 1 (Table Determination of Vulnerability indices Vi from RISK-UE document for le Teil – Montélimar typologies).

The location of the buildings is not changed, and for buildings that have not been diagnosed as red or yellow, the vulnerability index is unchanged, because it is considered that the repairs do not have a significant impact on the behavior of the building. Even if the buildings are located in the city, as the method we used to calculate the damage is a semi-empirical Macroseismic method, we interpret the results in a global statistical way at the scale of the Municipality. Moreover, the aim of these assumptions is not to have a precise value, but to have an overall vision and an order of magnitude of the reduction in damage if buildings were reinforced by considering the two assumptions mentioned. Below, Table 9 summarizes the results at the scale of the Municipality of the various hypothesis adopted in damage simulations.

From the point of view of the variation in damage levels from one rebuilding assumption scenario to another, as expected, the greater the tendency towards seismic-standard reconstruction, the lower the damage. Indeed, -10 % average damage in the case of seismic-standard reconstruction of buildings diagnosed as red and destroyed, and -25 % average damage in the case of seismic-standard reconstruction of buildings diagnosed as yellow, red and destroyed. The absolute differences in the number of damaged buildings in the different scenarios remain small, given the small variations in the number of buildings within the different construction types. At the town level, the overall reduction in vulnerability of the housing stock resulting from the implementation of seismic-resistant techniques only if the buildings are rebuilt is small, but nonetheless important to note. The best tool currently available to reduce the vulnerability of France's housing stock is to regulate and supervise construction, by means of building standards
<caption>**Table 9**</caption>

Comparative table of numbers and percentages of damaged buildings in the case of identical reconstruction and in the case of reconstruction to earthquake-resistant standards (destroyed buildings and buildings diagnosed as red, destroyed buildings and buildings diagnosed as red and yellow) - bld: building, nD ≥ 2 number of damaged buildings categorized as D2 (Moderate damage) and above, nD ≥ 3 number of damaged buildings categorized as D3 (Substantial to heavy damage) and above.

<table><thead><tr><th rowspan="2">Damage scenario</th><th colspan="5">Number of</th><th rowspan="2">nD<br>≥ 2</th><th rowspan="2">% of<br>variation of<br>nD ≥ 2</th><th rowspan="2">nD<br>≥ 3</th><th rowspan="2">% of<br>variation of<br>nD ≥ 3</th></tr><tr><th>bld</th><th>T1</th><th>T2</th><th>T3<br>old</th><th>T3<br>recent</th><th>T4</th></tr></thead><tbody><tr><td>BCSF-RéNass Epicentre</td><td>2778</td><td>1197</td><td>510</td><td>773</td><td>265</td><td>33</td><td>769</td><td>-</td><td>251</td><td>-</td></tr><tr><td>Buildings rebuilt as is</td><td></td><td>1135</td><td>490</td><td>785</td><td>333</td><td>35</td><td>706</td><td>-8%</td><td>226</td><td>-10%</td></tr><tr><td>Reconstruction to the building standards used for the initial construction of the completely destroyed buildings and the buildings tagged red</td><td></td><td>913</td><td>417</td><td>769</td><td>644</td><td>35</td><td>601</td><td>-22%</td><td>188</td><td>-25%</td></tr><tr><td>Reconstruction to the building standards used for the initial construction of the completely destroyed buildings, buildings tagged red and buildings tagged yellow</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

to be applied according to seismic zoning.

## 6. Conclusion

BRGM has provided data documenting the reconstruction phase, focusing on the Le Teil. A large amount of data has been collected, directly by BRGM in the field, and thanks to close collaboration with the services of the Le Teil municipality, the Prefecture and the disaster victims' collective. The richness of these collaborations enables us to highlight a number of recommendations at the end of the 4-year study of the post-earthquake reconstruction process in Le Teil.

By the end of 2023, the town's reconstruction may have reached 90%, given that the buildings diagnosed as green have been assumed to be systematically renovated. Moreover, around 70% of the prohibition orders may have been lifted by the end of 2023, provided that the database supplied by the mayor's office is up to date.

The lessons learned from the Le Teil post-earthquake reconstruction observatory project have enabled us to build up a database that is as complete as possible, right from the start of the project. Indeed, after an earthquake such as the one that hit the town, several organizations (mayor's office and administration, emergency managers, crisis managers, SDIS, AFPS, researchers, academics, associations, etc.) are involved in the post-earthquake emergency phase, during which a great deal of data is acquired. As a result of the work carried out here, it is clear that a period devoted to collecting pre-existing data is essential before setting up a database to monitor post-seismic reconstruction.

Monitoring the lifting or maintenance of initial dangerous structure notices as part of the reconstruction process is also an important parameter that should not be overlooked, hence the need for regular updates on the evolution of such data.

The implementation of dangerous structure notices and emergency diagnoses in the immediate aftermath of an event are designed to ensure the safety of people during crisis management and, as is frequently the case following a seismic event, the possible occurrence of aftershocks. Against this backdrop, it is only natural that safety decisions should be taken. We then recommend that buildings be re-inspected in a non-emergency/crisis-management context (e.g., 6 months after the event), with the aim of distinguishing between dwellings requiring major structural renovation/reconstruction work and those requiring only safety work on non-structural or aesthetic elements. This second phase of building analysis could make it possible to identify certain dwellings that could be re-occupied by families, with safety work less restrictive than work aimed at structurally reinforcing the building.

In the field, it would be interesting to consider setting up a more technical analysis of the work carried out, where possible and beneficial. In the current project in Le Teil, the aim of updating the database (essentially based on data from the AFPS and SDIS emergency diagnoses) is to determine whether or not the building has undergone more or less extensive work (filling cracks, shoring up, tie rods, etc.) with a view to bringing it back into service, but no technical advice is given. Future projects to monitor the post-earthquake reconstruction process would need to be designed with this in mind, as it would require more time and the addition of new fields to be updated in the database as annual missions are carried out, leading to a longer field phase than at Le Teil. A final noteworthy point concerns the classification of emergency diagnoses. When inspecting sites, it would be useful to classify buildings according to EMS-98 damage levels, in order to facilitate the processing and exploitation of the database. Indeed, emergency diagnostics classify buildings into three categories (Green; Yellow; Red), which are very useful in the crisis management phase, but difficult to exploit in the research phase during which validation of seismic scenarios and analysis of prevention methods are key points. We either recommend adding classification according to the EMS-98 damage scale, in existing forms or in new types of inspections to be introduced (cf. proposal for a second "cold" inspection for buildings classified as yellow and red). This work and the aforementioned recommendations will be implemented for future post-earthquake reconstruction monitoring (e.g., for monitoring reconstruction following the 2023 La Laigne earthquake, in France).
CRediT authorship contribution statement

C. Negulescu: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. J. Vieille: Writing – review & editing, Visualization, Validation, Supervision, Software, Project administration, Methodology, Formal analysis, Data curation. P. Gehl: Writing – review & editing, Validation, Supervision, Methodology. N. Taillefer: Validation, Supervision, Project administration, Methodology, Investigation, Conceptualization. K. Trevlopoulos: Writing – review & editing, Validation, Supervision, Methodology. S. Auclair: Formal analysis, Data curation.

Disclaimer

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

Special issue statement

This article is not part of a special issue. It is not associated with a conference.

Financial support

This research has been supported by the Directorate-General for Risk Prevention (DGPR) of the French Ministry of Ecological Transition and Territorial Cohesion (Convention BRGM – DGPR 2021–2023).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was made possible by the contributions of many partners, in terms of data collection and analysis of the reconstruction process. We would like to thank the French Association for Earthquake Engineering (AFPS) for providing access to the original earthquake damage inspection form. We would like to thank the services of the City of Le Teil, the “Victims collective”, the reconstruction mission manager of the Prefecture of Ardèche, the laboratory LAGAM (University Paul-Valéry), the laboratory PACTE (University of Grenoble) and their student interns, Sautier L., Perez R. and Lemaire L-P. The database, set up as part of a collaboration between BRGM, the AFPS and the SEISM Institute, served as a starting point for the collection of field data on the condition of the buildings concerned (repair in progress, completed, or not started). This study was also supported by an internal BRGM project. Developments of the risk evaluation procedure used here have been supported by various projects, including funding by the French Ministry of Ecological Transition and Territorial Cohesion, France and the European Union. We thank: Frederic Leone and Stéphane Cartier for supervising trainees for data compilation and GIS treatment.

Appendix 1. Determination of Vulnerability indices Vi from RISK-UE document for le Teil – Montélimar typologies

<table><thead><tr><th>Type</th><th>Type RISK-UE</th><th>Sous-type</th><th>Description</th><th>Period of construction</th><th>Photo</th><th>V<sup>-</sup></th><th>V<sup>*</sup></th><th>V<sup>+</sup></th></tr></thead></table>

T1: Unreinforced masonry rubble stone, simple stone, massive stone

M1.1

T1<sub>i</sub>

Stone building, isolated, old

<figure><img src="image_14.png" /></figure>

(continued on next page)
(continued)

<table><thead><tr><th>Type</th><th>Type RISK-UE</th><th>Sous-type</th><th>Description</th><th>Period of construction</th><th>Photo</th><th>V<sup>-</sup></th><th>V<sup>*</sup></th><th>V<sup>+</sup></th></tr></thead><tbody><tr><td rowspan="2"></td><td rowspan="2">T1<sub>m</sub></td><td rowspan="2"></td><td rowspan="2">Old, adjoining stone building</td><td rowspan="2">before 1970</td><td rowspan="2"></td><td rowspan="2">0.65</td><td rowspan="2">0.88</td><td rowspan="2">0.90</td></tr><tr><td></td></tr><tr><td rowspan="2"></td><td rowspan="2">T1<sub>mc</sub></td><td rowspan="2"></td><td rowspan="2">Old, adjoining stone building with ground-floor shops</td><td rowspan="2">before 1970</td><td rowspan="2"></td><td rowspan="2">0.65</td><td rowspan="2">0.92</td><td rowspan="2">0.90</td></tr><tr><td></td></tr><tr><td rowspan="3">T2: Unreinforced masonry buildings</td><td>M3.1</td><td>T2<sub>i</sub></td><td>Unreinforced, isolated, old masonry building</td><td>before 1970</td><td></td><td>0.65</td><td>0.74</td><td>0.83</td></tr><tr><td>M3.1</td><td>T2<sub>m</sub></td><td>Masonry building, unreinforced, adjoining, old</td><td>before 1970</td><td></td><td>0.65</td><td>0.80</td><td>0.83</td></tr><tr><td>M3.1</td><td>T2<sub>mc</sub></td><td>Old, unreinforced, adjoining masonry building (built before the 1970s) with ground-floor shops</td><td>before 1970</td><td></td><td>0.65</td><td>0.84</td><td>0.83</td></tr><tr><td rowspan="3">T3: Reinforced masonry building</td><td>M3.4</td><td>T3<sub>i</sub></td><td>Reinforced masonry building, isolated, relatively recent</td><td>built between 1970 and 200</td><td></td><td>0.49</td><td>0.62</td><td>0.79</td></tr><tr><td>M3.4</td><td>T3<sub>m</sub></td><td>Masonry building, reinforced, adjoining, relatively recent</td><td>built between 1970 and 200</td><td></td><td>0.49</td><td>0.66</td><td>0.79</td></tr><tr><td>M3.4</td><td>T3<sub>mc</sub></td><td>Masonry, reinforced, adjoining, relatively recent building with shops on the ground floor.</td><td>built between 1970 and 200</td><td></td><td>0.49</td><td>0.70</td><td>0.79</td></tr></tbody></table>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

(continued on next page)
(continued)

<table><thead><tr><th>Type</th><th>Type RISK-UE</th><th>Sous-type</th><th>Description</th><th>Period of construction</th><th>Photo</th><th>V<sup>-</sup></th><th>V<sup>*</sup></th><th>V<sup>+</sup></th></tr></thead><tbody><tr><td></td><td>M4</td><td>T3<sub>ir</sub></td><td>Masonry building, reinforced detached, recent</td><td>built after 2000</td><td></td><td>0.33</td><td><strong>0.31</strong></td><td>0.63</td></tr><tr><td></td><td>M4</td><td>T3<sub>mr</sub></td><td>Masonry building, reinforced, adjoining, recent</td><td>built after 2000</td><td></td><td>0.33</td><td><strong>0.35</strong></td><td>0.63</td></tr><tr><td></td><td>M4</td><td>T3<sub>mrc</sub></td><td>Masonry building, reinforced, adjoining, recent with shops on first floor.</td><td>built after 2000</td><td></td><td>0.33</td><td><strong>0.39</strong></td><td>0.63</td></tr><tr><td rowspan="2">T4: Concrete wall buildings<br/>Type 5: o Type 5: o<br/>Type 5r: RC with infilled masonry, recent (built after the 1990s)</td><td>RC3.1</td><td>T4</td><td>Relatively recent</td><td>built between the 1970s to 1990s</td><td></td><td>0.01</td><td><strong>0.41</strong></td><td>0.76</td></tr><tr><td>RC3.1</td><td>T4<sub>r</sub></td><td>Building with cast concrete walls, recent</td><td>built after the 1990</td><td></td><td>0.01</td><td><strong>0.24</strong></td><td>0.76</td></tr><tr><td rowspan="2">T5: RC buildings</td><td>RC4</td><td>T5</td><td>RC with infilled masonry Building, relatively recent</td><td>built 1970s to 1990s</td><td></td><td>0.01</td><td><strong>0.55</strong></td><td>0.67</td></tr><tr><td>RC4</td><td>T5<sub>r</sub></td><td>RC with infilled masonry, recent</td><td>built after the 1990s</td><td></td><td>0.01</td><td><strong>0.24</strong></td><td>0.67</td></tr></tbody></table>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

where V\* most probable value of the Vulnerability Index Vi and [V-, V+] bounds of the plausible range of the Vulnerability Index Vi.

- T1: Base of vulnerabilities associated with M1.2 + isolated adjustment (+0.04) + transparency (+0.04)

Base Vulnerability index, type M1.2: 0.74.

Floor number (1 or 2): 0 0.02.

In plan irregularity: +0.04.
Example of vulnerability index calculation for T1i: 0.74-0.02 + 0.04 = 0.76

Base Vulnerability index, type M1.2: 0.74.

Floor number (3 or 5): +0 0.02.

In plan irregularity: +0.04.

Non-isolated: +0.04.

Floors at different heights: +0.04.

Example of vulnerability index calculation T1m: 0.74 + 0.02+0.04 + 0.04+0 0.04 = 0.88

- T2: Base of vulnerabilities associated with M3.1, M3.4 and M4 + isolated adjustment (+0.04 or +0.06) + transparency (+0.04)

- T3: Basis of vulnerabilities associated with RC3.1 or RC4 + date adjustment (after 2000: 0.16, between 1970 and 2000: +0.16)

Final adjustment of V*, taking into account the V - and V + limits, to ensure consistency between the different typologies and the
reality on the ground.

Typology abbreviations.

- i = isolated

- m = adjoining (mitoyen)

- c = commercial

- r = recent (after 2000)

The vulnerability index (Vi) associated with each type is composed, according to the RISK-UE classification.

A basic Vi* value for the construction type:

o Type M1 for stone buildings (type 1): 0.76

o Type M3.1 for older, unreinforced masonry buildings (type 2): 0.74

o Type M3.4 for reinforced masonry buildings, relatively recent (type 3i, type 3m, type 3mc): 0.62

o Type M4 for reinforced masonry buildings, recent (type 3ir, type 3mr, type 3mrc): 0.45

o Type RC4 for Concrete wall buildings (type 4, type 4r): 0.39

o Type RC3.1 for RC buildings (type 5, type 5r): 0.41

Aggravating factors that increase the vulnerability index to obtain the final Vi* value associated with each typology:

o Period of construction for concrete buildings: ± 0.16

o Adjoining buildings: ± 0.04

o Presence of shops on the ground floor (transparency): +0.04

Appendix 2. Details on Le Teil's historic and urban dynamics

Le Teil's urban dynamics are an important factor in understanding the discrepancy between the damage caused in different parts of the town. The ancient center of Le Teil, then called "Tiliou", first grew up around the castle of the lord of the Adhémar family in the 12th century, then near the Rhône, with the development of a port activity from the 16th century onwards. During this period, Le Teil was divided into several villages surrounded by farmland. The first bridge linking Le Teil to Montélimar dates from the 19th century and it contributed to the town's development. By the end of the 19th century, the riverbed had narrowed and the town had retreated inland, partly due to the ravages caused by the Rhône River. The town then expanded inland, gaining farmland. Port trade came to an end in 1870, replaced by the railroads. The arrival of the railway station in 1876 led to the town's southward expansion and the intensification of village nuclei, notably Mélas and Frayol [3]. From the beginning of the 19th century, the industrial and railway sectors have strongly influenced the urbanization of the region until the 20th century. "Rapid development linked to industrial and railway activity led to urbanization in the form of suburbs along the road from Le Teil to Viviers, linking the town center and the neighborhoods adjacent to the Lafarge quarries" [3].

Two companies left their mark on the history of Le Teil in the 19th and 20th centuries. First, the Lafarge factories, founded in 1833, which later expanded thanks to the railroad. Secondly, the hamlets of Mélas, Teillaret, Frayol and La Violette (Fig. 1 of the Manuscript) formed the town of Le Teil, where railway workers lived in large numbers and made up the majority of the population. With the rise of
the railroads, the French train company SNCF built new housings for its railway workers, a purchasing cooperative, a leisure center and a library, thus playing an active role in the town's development between 1875 and 1950. Indeed, from 1947 onwards, aerial photographs show a progression of urban sprawl, an increase in building density, and thus a concentration of exposure.

Rail activity began to decline in the 1960s, followed by the decommissioning of the line to Alès and the closure of the line to passengers in the 1970s, combined with the disappearance of certain industries in Le Teil. From 1968 to 1975, the town's population declined by 0.8 % per year. This decline continued into the 1990s, with the population stagnating. The municipality's residential function, mostly linked to economical activities in the neighbouring city of Montélimar, has enabled a population growth of 0.9 % per year from 2008 to 2013 and even 1.2 % per year between 2013 and 2018. Le Teil's urbanization grew rapidly thanks to the activities offered by the region. Geographically, given the town's confinement between the Vivarais Mountains and the Rhône River, expansion possibilities remained limited. The Sablière district was one of the least affected by the earthquake, due to its geographical location (outside the site effects of the Rhône valley) and its age (recent urbanization).

## Appendix 3. Example of Summary sheet for District Centre.

### Summary sheet "Building reconstruction" : District "Centre"

<figure><img src="image_30.png" /></figure>

Number of diagnosis green, yellow, red : 276

Data from field campaigns carried out by the BRGM 2 years, 2 and a half years and 3 years after the earthquake.

Just over half of the buildings diagnosed green/yellow/red by the AFPS and the firefighters have not yet been renovated 3 years later.

In fact, only about 80 buildings are completely renovated. This is due to the fact that buildings in the city centre are mostly adjoining, which complicates the file and the compensation agreements with the insurance companies.

We can also highlight the difficulties of access by construction companies in the narrow streets of the city center.

At the scale of the "Centre" district, almost half of the buildings diagnosed as green, yellow or red have been rebuilt or are being rebuilt 3 years after the event.

The buildings diagnosed as yellow, with almost 50% renovation, have the highest renovation rate. Buildings diagnosed as green were rebuilt at about 32% and those diagnosed as red at about 46%.

Since green buildings are mainly buildings with little damage visible from the outside, it is difficult to assess their level of renovation. In contrast, buildings diagnosed as red require heavy repair or destruction followed by a complete reconstruction of the building. These renovations, clearly visible, require more tedious procedures and a delay in the completion of the work more substantial which slows down their reconstruction.

### Buildings under renovation vs buildings without any trace of reconstruction

<figure><img src="image_31.png" /></figure>
Summary sheet "Building vulnerability & diagnosis" : District "Centre"

<figure><img src="image_32.png" /></figure>

Distribution of buildings and diagnoses according to their typology on the district "Centre"

• Vulnerability associated to each typology

<table><thead><tr><th>Typologie</th><th>Description</th><th>Vulnerability (V*)</th></tr></thead><tbody><tr><td>T1</td><td>Stone</td><td>0,85</td></tr><tr><td>T2</td><td>Unconfined masonry</td><td>0,79</td></tr><tr><td>T3</td><td>Confined masonry</td><td>0,51</td></tr><tr><td>T4</td><td>Concrete</td><td>0,36</td></tr></tbody></table>

■ % of buildings
■ % of diagnosis green, yellow and red

<figure><img src="image_33.png" /></figure>

Number of buildings : 637

According to the various graphic representations, there is a significant share of TI buildings (about 75%), made of stone, which is the type with the highest vulnerability on the neighborhood.

Among the diagnoses, it is the TI type that is the majority with about 85% of diagnoses that are of this type.

No type T3 buildings have been diagnosed while this type represents almost 10% of the buildings in the neighborhood.

• Distribution of green, yellow and red diagnostics on the district "Centre"

Distribution of buildings diagnosed green, yellow and red on the district "Centre"

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /><figcaption>67 red diagnosis</figcaption></figure>

<figure><img src="image_36.png" /><figcaption>83 yellow diagnosis</figcaption></figure>

<figure><img src="image_37.png" /><figcaption>126 green diagnosis</figcaption></figure>

<figure><img src="image_38.png" /></figure>

Data availability

Data will be made available on request.

References

[1] J.F. Ritz, S. Baize, M. Ferry, C. Larroque, L. Audin, B. Delouis, E. Mathot, Surface rupture and shallow fault reactivation during the 2019 Mw 4.9 Le Teil earthquake, France, Communications Earth & Environment 1 (1) (2020) 10.

[2] M. Causse, C. Cornou, E. Maufroy, J.R. Grasso, L. Baillet, E. El Haber, Exceptional ground motion during the shallow Mw 4.9 2019 Le Teil earthquake, France, Communications Earth & Environment 2 (1) (2021) 14.

[3] AFPS, Rapport du séisme du Teil du 11 novembre 2019 (Ardèche), Novembre 2020, 2020.

[4] AFPS, Topoguide : Sur les traces du séisme du 11 novembre 2019. Un parcours sismique sur la commune du Teil (Ardèche), 2021.

[5] R. Perez, Stage en risque sismique pour l'analyse des dommages observés suite au séisme du Teil. Rapport Master en Géographie et Aménagement. Université Paul Valéry - Montpellier III, Septembre 2020, 2020.

[6] L. Sautier, Reconstruction post-sismique et renovation urbaine en France métropolitaine – Le cas de la commune du Teil après le séisme du 11 novembre 2019, Mémoire de fin d'étude M2, Université Grenoble Alpes, 2022.

[7] L.-P. Lemaire. Le Teil face à sa reconstruction post-sismique : apphension du territoire et de sa population dans son relèvement – deux ans après, Géographie, Université Paul Valéry - Montpellier III. Mémoire de stage M1, 2022, p. 123.

[8] M. Polese, M. Di Ludovico, A. Prota, Post-earthquake reconstruction: a study on the factors influencing demolition decisions after 2009 L'Aquila earthquake, Soil Dynam. Earthq. Eng. 105 (2018) 139-149, https://doi.org/10.1016/j.soildyn.2017.12.007.

[9] D. Leousis, N. Pnevmatikos, Earthquake losses assessment in the municipality of kifissia (athens-Greece) using the earthquake loss estimation routine (ELER), International Journal of Earthquake Engineering and Hazard Mitigation (IREHM) 6 (1) (2018) 11-20. Mairie du Teil (2021) "Séisme du 11 novembre 2019 – Retour d'expérience de la gestion de crise communale.

[10] Y. Wang, J. Li, H. Chen, Z. Zou, The time process of post-earthquake recovery: the Yao'an earthquake in China, Disasters 38 (4) (2014) 774-789.

[11] N. Pnevmatikos, F. Konstandakopoulou, N. Koumoutsos, Seismic vulnerability assessment and loss estimation in Cephalonia and Ithaca islands, Greece, due to earthquake events: a case study, Soil Dynam. Earthq. Eng. 136 (2020) 106252, https://doi.org/10.1016/j.soildyn.2020.106252.M. ISSN 0267-7261.

[12] S. Opricovic, G. Tzeng, Multicriteria planning of post-earthquake sustainable reconstruction, Comput. Aided Civ. Infrastruct. Eng. 17 (3) (2002) 211-220, https://doi.org/10.1111/1467-8667.00269.

[13] M. Horikiri, M. Odani, Analysis of residents' evacuation behavior after the great Hanshin-Awaji earthquake in Japan, Proceedings of the Eastern Asia Society for Transportation Studies 2 (1999) 431-446.

[14] FEMA, Seismic Performance Assessment of Buildings, Volume 5 - Expected Seismic Performance of Code-Conforming Buildings, 2018. FEMA P-58-5.

[15] C. Wang, R. Costa, J.W. Baker, Simulating post-disaster temporary housing needs for displaced households and out-of-town contractors, Earthq. Spectra 38 (2022) 2922-2940, https://doi.org/10.1177/87552930221112690.

[16] O. Issa, R. Silva-Lopez, J.W. Baker, H.V. Burton, Machine-learning-based optimization framework to support recovery-based design, Earthq Engng Struct Dyn 52 (2023) 3256-3280, https://doi.org/10.1002/eqe.3860.
[17] D.T. Cook, A.B. Liel, C.B. Haselton, M. Koliou, A framework for operationalizing the assessment of post-earthquake functional recovery of buildings, Earthq. Spectra 38 (2022) 1972–2007, https://doi.org/10.1177/87552930221081538.

[18] C. Molina Hutt, T. Vahanvaty, P. Kourehhpaz, An analytical framework to assess earthquake-induced downtime and model recovery of buildings, Earthq. Spectra 38 (2022) 1283–1320, https://doi.org/10.1177/87552930211060856.

[19] J. Birkmann, H. Schüttrumpf, J. Handmer, et al., Strengthening resilience in reconstruction after extreme events – insights from flood affected communities in Germany, Int. J. Disaster Risk Reduct. 96 (2023) 103965, https://doi.org/10.1016/j.ijdrr.2023.103965.

[20] N. Taillefer, J. Vieille, F. Andriamasinoro, Vers un observatoire de la reconstruction post-séisme – Application à la commune du Teil, Rapport Final. BRGM/RP-71635-FR (2021) 58.

[21] W.J. Clinton. Lessons Learned from Tsunami Recovery: Key Propositions for Building Back Better, Office of the UN Secretary-General’s Special Envoy for Tsunami Recovery, New York, 2006.

[22] S. Mitoulis, S. Argyroudis, V. Villa, V. Domaneschi, Reconstruction Prioritisation for Optimising Critical Infrastructure Resilience and Sustainability, 18th WCEE, Milan, 2024, 30th June –5th July.

[23] A. Moatty. Pour une Géographie des Reconstructions post-catastrophe : Risques, Sociétés et Territoires. Thèse de doctorat, Géographie, Université Paul Valéry, Montpellier, 2015, p. 491. < tel.01293718 >.

[24] N. Taillefer, J. Vieille, C. Negulescu, Vers un observatoire de la reconstruction post-séisme – Application à la commune du Teil. Rapport d'avancement 2022, Rapport Final. BRGM/RP-72419-FR (2023) 35.

[25] J. Vieille, N. Taillefer, C. Negulescu, Post-seismic Reconstruction in Le Teil Following the November 11th 2019 Seismic Event: Zoom over Building Reconstruction, 18th WCEE, Milan, 2024, 30th June –5th July.

[26] S. Lagomarsino, S. Giovinzazzi, S. Podesta, S. Resemini, WP4-Vulnerability assessment of current buildings: I level methodology for the vulnerability assessment of current buildings and guidelines for the implementation. RISK-UE project: an advanced approach to earthquake risk scenarios with applications to different European towns, Contract No. EVK4-CT-2000-00014 (2002) 28.

[27] Z.V. Milutinovic, G.S. Trendafiloski, Risk-UE an advanced approach to earthquake risk scenarios with applications to different european towns, Contract: EVK4-CT-2000-00014, WP4: Vulnerability of Current Buildings (2003) 1–111.

[28] S. Giovinzazzi, S. Lagomarsino, A Macroseismic Model for the vulnerability assessment of buildings. 13th World Conference on Earthquake Engineering, Canada, Vancouver, 2004.

[29] O. Sedan, C. Negulescu, M. Terrier, A. Roulle, T. Winter, D. Bertil, Armagedom — a tool for seismic risk assessment illustrated with applications, J. Earthq. Eng. 17 (2013) 253–281, https://doi.org/10.1080/13632469.2012.726604.

[30] D. Monfort, A. Roulle, J.-M. avec la collaboration de Schroetter, M. Terrier, C. Cartannaz, B. Troadec, L. Grabenstaetter, et al.C. Greffié, - Estimation statistique de la répartition des classes de sol Eurocode 8 sur le territoire français. Phase 1. Rapport final, Rapport BRGM/RP-66250-FR (2016) 48, 14 fig., 7 tab., 1 ann.

[31] R. Fayjaloun, C. Negulescu, A. Roulle, S. Auclair, P. Gehl, M. Faravelli, Sensitivity of earthquake damage estimation to the input data (soil characterization maps and building exposure): case study in the Luchon Valley, France, Geosciences 11 (6) (2021) 249.

[32] G. Grünthal, "European macroseismic scale 1998, Cah. Cent. Eur. Geodyn. Seismol. 15 (1998) 1–97.

[33] C. Negulescu, F. Smai, R. Quique, et al., VIGIRISKS platform, a web-tool for single and multi-hazard risk assessment, Nat. Hazards 115 (2023) 593–618, https://doi.org/10.1007/s11069-022-05567-6.

[34] K. Trevlopoulos, P. Gehl, C. Negulescu, H. Crowley, L. Danciu, Comparing components for seismic risk modelling using data from the 2019 Le Teil (France) earthquake, Nat. Hazards Earth Syst. Sci. 24 (2024) 2383–2401, https://doi.org/10.5194/nhess-24-2383-2024.

[35] A. Schlupp, C. Sira, E. Maufroy, L. Provost, R. Dretzen, E. Bertrand, E. Beck, M. Schaming, EMS98 intensities distribution of the “Le Teil” earthquake, France, 11 November 2019 (Mw 4.9) based on macroseismic surveys and field investigations, CR Geosci 353 (2022) 465–492, https://doi.org/10.5802/crgeos.88.

[36] G.M. Atkinson, D.M. Boore, Ground motion prediction equations in the east of North America, Bull. Seismol. Soc. Am. 96 (2006) 2181–2205.

[37] S. Akkar, J.J. Bommer, Empirical equations for predicting PGA, PGV and spectral accelerations in Europe, Mediterranean and Middle East 81 (2010) 195–206. Letters on seismological studies.

[38] B.S.-J. Chiou, R.R. Youngs, An NGA model for the average horizontal component of peak ground motion and response spectra, Earthq. Spectra 24 (2008) 173–215.

[39] G.M. Atkinson, E. Sonley, Relationships between modified mercalli intensity and response spectra, Bull. Seism. Soc. Am. 90 (2000) 537–544.

[40] BCSF RéNaSS, Rapport préliminaire de la mission du Groupe d'intervention macrosismique (G.I.M.), 2019.

[41] J. Douglas, D.M. Climent, C. Negulescu, A. Roulle, O. Sedan, Limits on the potential accuracy of earthquake risk evaluations using the L'Aquila (Italy) earthquake as an example, Ann. Geophys. 58 (2) (2015). S0214-S0214.

[42] E. Riga, A. Karatzetzou, S. Apostolaki, et al., Verification of seismic risk models using observed damages from past earthquake events, Bull Earthquake Eng 19 (2021) 713–744, https://doi.org/10.1007/s10518-020-01017-5.

[43] JORF n°0248 du 24 octobre 2010, Arrêté du 22 octobre 2010 relatif à la classification et aux règles de construction parasismique applicables aux bâtiments de la classe dite « à risque normal », JORF n° 0248 du 24 octobre 2010 - Légifrance (legifrance.gouv.fr).