# A methodology for stock assessment and traceability at the component level: the case of structural steel reuse

Chloé Ruda, Myriam Saadé, E Sorin, Cyril Douthe, François Lecompte,
Olivier Baverel

► To cite this version:

Chloé Ruda, Myriam Saadé, E Sorin, Cyril Douthe, François Lecompte, et al. A methodology for stock assessment and traceability at the component level: the case of structural steel reuse. CISBAT 2025, Sep 2025, Fribourg, Switzerland. pp.162013, 10.1088/1742-6596/3140/16/162013. [hal-05408076](https://hal.science/hal-05408076v1)

HAL Id: hal-05408076

https://hal.science/hal-05408076v1

Submitted on 9 Dec 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

PAPER • OPEN ACCESS

# A methodology for stock assessment and traceability at the component level: the case of structural steel reuse

To cite this article: C Ruda et al 2025 J. Phys.: Conf. Ser. 3140 162013

View the [article online](#) for updates and enhancements.

You may also like

- URMIBALI project: How can digital documentation technologies be a support for urban mining and reuse of building materials? A new method for data acquisition on traditional residential buildings in Liège
S. Trachte, O. Noël, A. Schreurs et al.

- Holistic Decision-Making Approach for Re-Defining Positive Energy Districts: A Framework for Neighbourhood Renovation
A Brunetti, M Cellura, S Cellura et al.

- IFC Whisperer: Querying Building Information Models with Large Language Models and IFC-based Knowledge Graphs
Yu Hsiu Tung, Samaneh Rezvani, Fatemeh Asgharzadeh et al.

<figure><img src="image_3.png" /></figure>
# A methodology for stock assessment and traceability at the component level: the case of structural steel reuse

C Ruda<fnref n="1" />,<fnref n="2" />, M Saadé<fnref n="3" />, E Sorin<fnref n="4" />, C Douthe<fnref n="1" />, F Lecompte<fnref n="2" />, O Baverel<fnref n="1" />

E-mail: chloe.ruda@enpc.fr

**Abstract.** To reduce the environmental burden of the construction industry, reusing reclaimed steel instead of recycling it is seen as a promising option to move the structural steel industry towards a more circular economy. Assessing the material composition of the built environment is key to develop efficient circular strategies. Reliable sectorial data is required, per material type. This paper unvails the potentials of using first-hand construction documents to assess material stocks of structural steel within existing industrial buildings. It integrates into a bottom-up Material Flow Analysis (MFA) framework. A component-based prototype of an industrial steel structure is defined and its material composition is calibrated thanks to construction documents of built structures. The accuracy of stock evaluation will depend on the number and the variety of buildings available in the dataset. Data extraction from the available construction documents is still ongoing. Yet, the proposed method provides valuable insights on the potentials of construction document-based archetypes. As for future reuse strategies, leveraging such documentation is also interesting to ensure the traceability of today's buildings. This paper explores the opportunities offered by construction documents in relation with state-of-the-art methods that aims at designing structures from reclaimed elements. Shipping lists are identified as highly relevant to inform decision-making on future reuse alternatives.

## 1. Introduction

Structural steel in buildings represents about 10% of the global demand for steel products, with processes related to steel production accounting for approximately one quarter of the global annual industrial emissions [1]. As for the life-cycle of structural steel, 75 to 80% of the total impact on climate change is due to the production stage, according to the French environmental product declarations (EPDs) [2]. In a context of high environmental burdens on the climate and the ecosystems, reusing structural steel members is seen as a promising lever to mitigate the impacts of the steel construction industry.

### 1.1. Assessing building stocks for circular strategies

At a market scale, assessing plausible stocks of steel components contained in the aging built environment is required to evaluate the potentials of reuse. With a constant demand for new

<figure><img src="image_4.png" /></figure>

<footnote n="1">¹ Navier, ENPC, Institut Polytechnique de Paris, Univ Gustave Eiffel, CNRS, Marne-la-Vallée, France</footnote>

<footnote n="2">² Viry - Fayat Group, Eloyes, France</footnote>

<footnote n="3">³ UTOPII, Paris, France</footnote>

<footnote n="4">⁴ Scientific and Technical Centre for Buildings (CSTB), Champs-sur-Marne, France</footnote>
buildings, it could also provide valuable insights on the share of new constructions that could be made out of reclaimed steel components.

Stock assessment can be carried out with Material Flow Analysis (MFA). Bottom-up approaches based on material intensity coefficient (MIC, in $kg/m^2$) or on material per gross volume (GV, in $kg/m^3$) have been implemented to evaluate the global material quantities present in a given territory. Existing studies mostly focus on residential buildings [3] [4] [5] but some were also done for non-residential buildings in Germany [6] [7]. As an alternative to MIC or GV, a bottom-up approach based on functional components can be used [5] [8] [9] [10]. In both cases, assessment on product and materials relies on the combination of design handbooks, feedbacks from experts and/or extrapolation of the available data on a limited sample of buildings. Uncertainties about the assessed quantities are thus difficult to evaluate. In most studies, global masses per material categories, from structure to finishing, are assessed with the objective to inform waste management policies. As for reuse, reliable sectorial data at the components' level are missing to forecast potential markets.

Recent studies have investigated more sophisticated approaches such as Building Information Modelling (BIM) of existing buildings [11] [12] [13] and machine learning processing of street view images [14]. However, these workflows encounter challenges similar to those of conventional bottom-up approaches regarding data quality and uncertainty.

## 1.2. Traceability requirements for future reuse

Another challenge faced by the reuse industry pertains to the need to anticipate reuse pathways of buildings designed today. Traceability of products and materials can be handled with a BIM framework combined with materials passports integrated to the physical components, through RFID or QR Code [15]. As part of a *design-for-reuse* framework, this data formatting should account for the constraints of future design methodology, namely *stock-constrained* design.

This paper investigates the potentials of using first-hand construction documents to inform both stock assessment and traceability for future designs. *Construction documents* refer to any documentation exchanged during the construction process between the main contractor, the sub-contractors, the project's owner and the designer. The documents collected for this study contain as-built plans, shop-drawings, BIM models and shipping lists. They were transmitted by 3 steel contractors and cover 28 industrial buildings. In Section 2, the component-based, bottom-up MFA framework is presented and the methodology for defining a new archetype of an industrial steel structure is described. A practical example is developed in Section 3, in which the opportunities to accurately characterize the archetype with exhaustive information from the construction documents are discussed. Section 4 explores the advantages to leverage from shipping lists. Building component matrices are introduced as a new stock representation to inform decision-making on future reuse.

## 2. Methodology

### 2.1. Global framework for material stock characterization

This study integrates into the BTP-Flux model [9, 16] developed by the French Scientific and Technical Centre for Buildings (CSTB). The framework relies on a Geographic Information System (GIS) - the BDNB [17] - which collates various sources on French buildings and enables to analyse the built environment specific to a given region. The framework comprises a component-based bottom-up model of buildings. Buildings are classified into archetypes based on specific fields from the GIS database, such as *building use* and *construction date*. Material quantities are then calculated for each building according to its archetype, taking into account the specific rules of composition and dimensioning of the macro-components. Quantities are then extrapolated to all buildings in a given territory, based on a sample of well-characterized buildings.
<figure><img src="image_5.png" /><figcaption>Figure 1: Definition of a single-bay industrial hall prototype</figcaption></figure>

The framework was primarily tested on residential buildings. In the following, an explicit
archetype of portal-frame industrial steel structures is described and calibrated thanks to the
database of construction documents.

## 2.2. *New component-based prototype of industrial steel structures*

The definition follows the multi-level tree taxonomy described by [9]. The studied unit corresponds to a single-bay hall and is defined according to the structural hierarchy of load-bearing components, namely primary and secondary structures. The prototype is a combination of 4 macro-components : *main superstructure* (collating components of the primary structural system), *gable*, *long-side* and *roof* (the last three describing secondary structural systems like façade supports). The schematic representation of the *main superstructure* macro-component is shown in Figure 1a. The breakdown of the *gable* macro-component into elementary components is shown in Figure 1b.

For each macro-component, fields are defined to specify the geometry and the elementary
components' layout. The mass of a component is then deduced from its cross-sectional area. As
an example, specific fields attached to the *main* macro-component are described in Table 1.

## 2.3. *Archetype calibration*

Each building in the dataset is associated with an *archetype specimen*. Field variables are set
according to construction documents. The prototype completion is carried out either manually
or semi-automatically, depending on the existence of a BIM model within the available data.
For each specimen, total mass of structural steel can then be recomputed and compared to the
*actual* structural mass.

The *actual mass* is given by shipping lists. The typical layout of a shipping list is described
<table><caption>Table 1: Fields for main macro-component definition</caption><thead><tr><th colspan="4">geometry</th></tr></thead><tbody><tr><td>span<br>(m)</td><td>long_side<br>(m)</td><td>N_frames<br>(-)</td><td>h_eave_purlin<br>(m)</td></tr><tr><td>h_apex<br>(m)</td><td>roof_slope<br>(%)</td><td>N_purlins<br>(-)</td><td>N_cladding_rail<br>(-)</td></tr><tr><th colspan="4">elementary components</th></tr><tr><td>girder section<br>(-)</td><td>column section<br>(-)</td><td>frameAbs steel Grade<br>(MPa)</td><td>purlin section<br>(-)</td></tr><tr><td>cladding_rail section<br>(-)</td><td>bracing section<br>(-)</td><td>othersAbs steel Grade<br>(MPa)</td><td></td></tr></tbody></table>

further in Table 2. It is attached to any shipment made to the worksite, collating all the components exiting the workshop. It can be edited by any professional software, including BIM application, and is reviewed manually to match each specific shipment.

The mass of a component includes the main section and all the secondary organs welded and/or bolted to it. These lists are more accurate than the building surveys usually used for archetype calibration [6, 9, 16]. Indeed, shipping lists are crucial documents for communication between operators and quality control during the construction process. Building surveys established after construction are inevitably rougher estimates of material quantities.

<table><caption>Table 2: Structure of shipping list</caption><thead><tr><th>tag</th><th>quantity</th><th>grade</th><th>cross-section</th><th>length(mm)</th><th>type</th><th>mass(kg)</th></tr></thead><tbody><tr><td>C1</td><td>32</td><td>S235JR</td><td>L50*50*5</td><td>600</td><td>BRACING</td><td>72.5</td></tr><tr><td>A1</td><td>2</td><td>S275JR</td><td>IPE300</td><td>7340</td><td>GIRDER</td><td>721.7</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td></td><td></td><td></td></tr></tbody></table>

### 3. Case study

#### 3.1. Sub-set of warehouses from the dataset

The new archetype described in Section 2.2 was tested on a sub-set of 4 buildings located in the Aquitaine region in France. The buildings' use and geometrical features are described in Table 3.

Eventually, the archetype will be tested on a set of 28 buildings covering the South and West of France. For comparison purposes, a similar study based on GV quantities was carried out in Germany in 2017. It included 19 non-residential buildings [6].

<table><thead><tr><th>field</th><th>unit</th><th>bat0</th><th>bat1</th><th>bat2</th><th>bat3</th></tr></thead><tbody><tr><td>span</td><td>m</td><td>20</td><td>25</td><td>27</td><td>30</td></tr><tr><td>long_side</td><td>m</td><td>30</td><td>48</td><td>42</td><td>50</td></tr><tr><td>height</td><td>m</td><td>4.8</td><td>4</td><td>6</td><td>6</td></tr><tr><td>use*</td><td>-</td><td>indus.</td><td>agri.</td><td>agri.</td><td>agri.</td></tr></tbody></table>

<caption>Table 3: Global geometry and use for the 4 buildings in the sub-set</caption>

<footnote>(* agri. = agricultural, indus. = industrial)</footnote>
## 3.2. *Results*

Comparison of data from the archetype specimens and from as-built documentation is plotted in Figure 2. Comparison on MIC (fig. 2a) shows that the archetype correctly models global material quantities for **bat0** and **bat2**, with relative deviations inferior to 10%. However, it clearly overestimates the MIC of **bat1** and **bat3**. Figure 2b suggests that this discrepancy is partly due to the design rule on frame spacing (related to the number of frames, *N frames* field, from Table 1). A greater number of buildings in the sub-set will help adjust this design rule and reveal if it should be tuned to another structural parameter. For instance, it is likely that we observe a correlation between the frame spacing and the *purlin section* field, related to the longitudinal structure. Alternatively, **bat1** and **bat3** may be used to calibrate a second different archetype of industrial steel structure.

<figure><img src="image_6.png" /><figcaption>Figure 2: Comparison between reconstructed archetype specimens and as-built values</figcaption></figure>

# 4. Perspectives for future reuse of components

Among the collected documents, shipping lists are also identified as highly relevant for addressing the challenges of future steel reuse, with the potential to constitute components databases [15].

## 4.1. *Databank formulation compatible with stock-constrained design algorithms*

Shipping lists display the minimal geometrical and mechanical information required for the requalification of structural components, as stated in recent professional recommendations [18]. Additionally, fields contained in the lists are matching the parameters required for stock-constrained design [19]. These charts can thus be structured in a vector fashion to feed design-from-stock algorithms. The global framework is outlined in Figure 3. If made available throughout the life span of a building, shipping lists directly enable to consider decommissioned structures as stocks of reclaimed elements.

## 4.2. *Definition of building component matrices*

Based on shipping lists, we define building component matrices. An example is drawn on Figure 3. This flexible representation enables to plot the structural composition of a building based on different properties of the components. In Figure 3, components are ordered per ascending cross sectional area and per ascending length. Sub-groups of components intended to different reuse pathways can be quickly assessed by practitioners. As an example, one can decide that
components longer than 5 meters and heavier than an IPE200 section, plotted in the top right-hand corner, would be suitable for a reuse as bending-dominated members (joists, portal frame girders, etc.). Components displaying a smaller cross sectional area could rather be used for designing new reticular structures such as trusses.

For stock assessment purposes, component matrices could also be relevant input data for machine learning algorithms. They could be used to extrapolate the archetype definition and refine the sizing rules. However, this would require to have much more buildings in the dataset. For comparison, in another context, predictions of buildings' environmental impacts using machine learning were made based on datasets of 300 to 500 specimens [20].

<figure><img src="image_7.png" /><figcaption>Figure 3: Building components matrices for stock-constrained design</figcaption></figure>

**5. Conclusion**

The framework for systematic processing of construction documents is composed of both an automatable protocol for archetype definition and an automatic procedure for capitalizing on the information contained in the shipping lists.

An prototype of industrial hall was defined to carry out stock assessment of structural steel at the component level. To validate the prototype, completing prototype specimens for each building of the dataset is necessary. This procedure is currently semi-automated and is completed with a manual review of drawings and/or BIM models. Although data extraction is still ongoing, the example in Section 3 shows that the method, informed by exhaustive as-built documentation, has the potential to generate comprehensive and reliable data on steel stocks. The workflow could be easily extended to other construction industries used to fabricating components in a workshop, as they are likely to produce similar documents for tracking production.

Shipping lists were identified as key documents for constituting components databases directly compatible with the matrix formulation of stock-constrained design algorithms. As simplified versions of BIM databanks, these tables will not help tracking the potential damages to the structure. Yet, their intuitive format makes them easily accessible and exchangeable. In that sense, they are complementary to more sophisticated data format [15]. In addition, the definition of building component matrices derived from shipping lists could inform strategic decision at early stages of reuse projects.

Eventually, the compilation of component databases for a substantial number of built projects will convey the potential to better define the archetypes. For that purpose, machine learning is identified as an appropriate tool.
Acknowledgments

This paper has been produced as part of a PhD thesis founded by French steel contractor VIRY, FAYAT GROUP in collaboration with the national association for research and technology ANRT. In addition, the authors would like to thank O. Destefani, O. Merland and P. Gautraud, from Fayat Metal compagnies, and V. Fiorello, from CSTB.

References

[1] Allwood J M, Cullen J M and Carruth M A 2012 *Sustainable Materials with Both Eyes Open* (UIT Cambridge Limited) ISBN 978-1-906860-05-9 google-Books-ID: QJ8OywAACAAJ URL https://www.uselessgroup.org/publications/book/chapters

[2] Base INIES URL https://base-inies.fr/consultation/

[3] Augiseau V and Kim E 2021 *Resources, Conservation and Recycling* **170** 105512

[4] Yang D, Guo J, Sun L, Shi F, Liu J and Tanikawa H 2020 *Resources, Conservation and Recycling* **159** 104824 ISSN 09213449 URL https://linkinghub.elsevier.com/retrieve/pii/S0921344920301452

[5] Lanau M and Liu G 2020 *Environmental Science & Technology* **54** 4675–4685 ISSN 0013-936X publisher: American Chemical Society URL https://doi.org/10.1021/acs.est.9b07749

[6] Schebek L, Schnitzer B, Blesinger D, Köhn A, Miekley B, Linke H J, Lohmann A, Motzko C and Seemann A 2017 *Resources, Conservation and Recycling* **123** 24–36 ISSN 0921-3449 URL https://www.sciencedirect.com/science/article/pii/S0921344916301380

[7] Ortlepp R, Karin G, and Schiller G 2016 *Building Research & Information* **44** 840–862 ISSN 0961-3218 publisher: Routledge eprint: https://doi.org/10.1080/09613218.2016.1112096 URL https://doi.org/10.1080/09613218.2016.1112096

[8] Mastrucci A, Marvuglia A, Popovici E, Leopold U and Benetto E 2017 *Resources, Conservation and Recycling* **123** 54–66 ISSN 09213449 URL https://linkinghub.elsevier.com/retrieve/pii/S0921344916301665

[9] Tirado R, Aublet A, Laurenceau S, Thorel M, Louërat M and Habert G 2021 *Sustainability* **13** 13159 ISSN 2071-1050 number: 23 Publisher: Multidisciplinary Digital Publishing Institute URL https://www.mdpi.com/2071-1050/13/23/13159

[10] Francart N, Gummidi S R B, Hoxha E and Birgisdottir H 2023 *Journal of Physics: Conference Series* **2600** 192001 ISSN 1742-6596 publisher: IOP Publishing URL https://dx.doi.org/10.1088/1742-6596/2600/19/192001

[11] Volk R, Stengel J and Schultmann F 2014 *Automation in Construction* **38** 109–127 ISSN 0926-5805 URL https://www.sciencedirect.com/science/article/pii/S092658051300191X

[12] Honic M, Ferschin P, Breitfuss D, Cencic O, Gourlis G, Kovacic I and De Wolf C 2023 *Developments in the Built Environment* **13** 100110 ISSN 2666-1659 URL https://www.sciencedirect.com/science/article/pii/S2666165922000448

[13] Triantafyllidis G and Huang L 2023 *Journal of Physics: Conference Series* (IOP Publishing) URL https://iopscience.iop.org/article/10.1088/1742-6596/2600/19/192015/meta

[14] Raghu D, Bucher M and De Wolf C 2023 *Resources Conservation and Recycling Volume 198*

[15] Bertin I, Mesnil R, Jaeger J M, Feraille A and Le Roy R 2020 *Sustainability* **12** 3147 ISSN 2071-1050 number: 8 Publisher: Multidisciplinary Digital Publishing Institute URL https://www.mdpi.com/2071-1050/12/8/3147

[16] Sorin E, Tirado R, Gully E, Louërat M and Laurenceau S 2022 *IOP Conference Series: Earth and Environmental Science* **1078** 012027 ISSN 1755-1315 publisher: IOP Publishing URL https://dx.doi.org/10.1088/1755-1315/1078/1/012027

[17] Centre Scientifique et Technique du Bâtiment (CSTB) La Base de Données Nationale des Bâtiments (BDNB) URL https://bdnb.io/

[18] Beyer A and Ben Larbi A 2024 *Réemploi d'éléments structuraux en acier. Recommendations professionnelles. Tech. Rep. 1ère Ed.* CTICM URL https://www.cticm.com/vous-certifier/certification-elements-de-reemploi/

[19] Brütting J, Senatore G and Fivet C 2018 *Advanced Computing Strategies for Engineering* Lecture Notes in Computer Science ed Smith I F C and Domer B (Cham: Springer International Publishing) pp 139–163 ISBN 978-3-319-91635-4

[20] Fenton S K, Munteanu A, De Rycke K and De Laet L 2024 *Building and Environment* **257** 111523 ISSN 0360-1323 URL https://www.sciencedirect.com/science/article/pii/S0360132324003652