# BIM to Develop Integrated, Incremental and Multiscale Methods to Assess Comfort and Quality of Public Spaces

Thibaut Delval, Brice Geffroy, Mehdi Rezoug, Alexandre Jolibois, Fabrice

Oliveira, Samuel Carré, Mélanie Tual, Julien Soula

► To cite this version:

Thibaut Delval, Brice Geffroy, Mehdi Rezoug, Alexandre Jolibois, Fabrice Oliveira, et al. BIM to Develop Integrated, Incremental and Multiscale Methods to Assess Comfort and Quality of Public Spaces. Lecture Notes in Civil Engineering, 2021, Proceedings of the 18th International Conference on Computing in Civil and Building Engineering ICCCBE 2020, 98, pp.160-179. 10.1007/978-3-030-51295-8_14. hal-04111690

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Submitted on 31 May 2023

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# BIM to develop integrated, incremental and multiscale methods to assess comfort and quality of public spaces

Thibaut Delval¹, Brice Geffroy², Mehdi Rezoug¹, Alexandre Jolibois¹, Fabrice De Oliveira¹, Samuel Carré¹, Mélanie Tual¹

¹ Centre Scientifique et Technique du Bâtiment, 84 avenue Jean Jaurès 77420 Champs sur Marne, France

² Paris La Défense, 110 esplanade du Général-de-Gaulle 92932 Paris La Défense, France

**Abstract.** This BIM approach applied to the urban scale has allowed implementation of territorial digital models in standard and open formats, including IFC and CityGML. New datasets have been generated to feed multiscale simulations with input data and to collect results which can then be used to create global indicators to evaluate current urban projects, in any field of interest. In our case all the available data of the territory - including new and existing buildings and infrastructure - of the National Interest Operation of Paris La Défense (which is France's largest business district) has been collected and formatted into several databases. Our achieved interoperable BIM model has been adapted to perform complex multi-physical studies and simulations in several technical fields (including noise exposure, wind comfort, artificial and natural lighting, energy consumption, environmental impacts and global comfort) in order to understand and assess the quality of use of public spaces.

This paper shows a smart combination and integration of these databases and parameters which made it possible to define new methodologies to evaluate urban comfort, as well as the quality of use of public spaces. It also presents an application of this research in the context of public consultation, where results and methodologies were used to develop a tool to show to end users the impact of land use decisions on the quality of use of public spaces.

Indeed, overall comfort is a complex and global concept on which several research studies have been already carried out at the building scale as well as urban environments, but rarely at the scale of a whole district and including time variability across an entire year. To achieve this, one of the main challenges has been to work with a large set of parameters that are often heterogeneous, multiscale (building, city and territory) and multiformat.

Another challenge has been to deal with the interdependence of physical phenomena in the perception of comfort, which led us into redefining use cases and time series for simulation. The multi-scale BIM model proved itself extremely useful as the underlying structure for the centralization of usable data for comfort assessment, even though dealing with this amount of data brought new issues regarding data storing.

Concomitantly, CSTB conducted a research to experiment how “Paris La Défense” could use the BIM-integrated digital model to evaluate the urban quality of public spaces. A methodology was built by CSTB social scientists in partnership with urban designers of the local authority to define indicators to grasp and assess the urban quality of public spaces. The tool is meant to help making urban design choices and was applied to the same urban area where the comfort study was carried out in order to capitalize data.
# 1 Background: setting up an interoperable SIM Ready Data Base

Paris La Défense, the first European business district, is the only vertical district in France with a high urban density. The territory hosts 70 high-rise buildings, 245.000 m² of shops and more than 3.6 million m² of offices. The urban planning of La Défense, based on a slab, has created a 31-hectare pedestrian public space where 180,000 employees, 42,000 inhabitants and 45,000 students meet daily. The public establishment Paris La Défense is responsible for the development, management, animation and promotion of the business district. The establishment continues the urban transformation of the territory by promoting the diversity and by arranging the spaces in urban places. To meet its objectives and strengthen the attractiveness of the territory on a global scale, Paris La Défense also develops services and improves the customer experience by opening the territory to experimentation.

The use of an evaluation tool, developed by the CSTB, allows global and scientific vision of the urban comfort and its qualities of use. This tool meets the challenges of the planners by allowing the comparison between projects with objective criteria, in order to make informed decisions during studies. By appreciating the quality of the public spaces according to their uses over time, the establishment disposes of a solution of assessment of the quality of exploitation of the sites. Finally, this tool optimizes the layout and installations of ephemeral events to improve the level of satisfaction of users during animations.

## 1.1 Adaptation of the model geometry for simulations

Multi-physical simulations models are important tools to study, understand and predict many complex applications such as urban comfort levels. Among the various categories of these models, there are those based on 3D analysis that aims at “reconstructing” the 3D environment (i.e. the building shapes and basic geometrical characteristics and dimensions), its properties and governing physical laws [1].

Tremendous progress has been observed in computational sciences and engineering over the past decades, making it now possible to perform large scale computations based on geometric models spanning many spatial and temporal scales.

On the other hand, many important scientific and technological developments in the last few decades have resulted in a great increase in availability of intelligent data format, able to represent the 3D environment on different scales, and support semantic information attached to geometric models.

To name a few, 3D Geographic Information Systems (GIS) such as CityGML are a many-scale models with resolutions from continental down to building scale; and Building Information Models (BIM) are starting to revolutionize the construction sector, both in buildings and infrastructure.
CityGML is the international standard of the Open Geospatial Consortium (OGC) for the representation and exchange of 3D city models. This format is not restricted to modelling buildings only, but also covers all relevant features one can find within urban areas. For each feature, a semantic definition, a set of attributes, relationships to other objects and a 3D spatial representation are provided [2,3,4].

In CityGML geometries, features are represented geometrically by the well-known Boundary Representation (B-rep): Volume features such as buildings are represented by solids, which are defined by their bounding surfaces. These surfaces must be mutually nonoverlapping, non-penetrating, and completely seal a volume without gaps. In general, the advantage of a solid representation of features is that the volume can be computed. This is necessary, for example, in environmental applications for the computation of energy related performances [5,6].

When a solid representation for a building is not available, alternatively a representation by surfaces that do not completely seal the feature (multi surfaces) is possible. Area or line features such as single walls, roads or energy system network are represented by surface or line geometries.

The CityGML features are organized into modules (building module, vegetation module, transportation module, etc.), which can be arbitrarily combined as needed for a specific simulation.

The Building module aggregates several objects like RoofSurface, CeilingSurface, GroundSurface, FloorSurface, WallSurface. Each object can be enriched, for example with thermal characteristics of the surfaces (i.e. WallConductivity, WallThermalResistance, etc.) required for energy analysis.

The Transportation module provides roads, railways and similar features. Physical and technical characteristics associated with these classes (ex: type of vehicles, traffic density, etc.) can be used to simulate road traffic noise in urban areas.

Solitary vegetation objects like trees and plant covers are provided by the vegetation module. Physical properties of such objects (ex: foliar index, vegetation density, evapotranspiration, etc.) may be used for microclimate simulations.

The City Furniture module is used to represent city and street furniture like streetlights. Such objects are relevant for street lighting simulation.

In order to prepare the necessary input data for each simulation model, we have structured the physical characteristics by parameter sets, in the appropriate modules, as a generic attribute. Figure 1 illustrates an example of how these attributes can be structured according to the type of application.
<table><caption>Fig. 1. Extract of the used data dictionary</caption><thead><tr><th>Thematic simulation</th><th>CityGML Thematic Module</th><th>CityGML Feature type</th><th>IFC Feature</th><th>Simulation attribut (Input/Output parameters)</th><th>Attribute type</th><th>Geometry Representation</th><th>LOD</th></tr></thead><tbody><tr><td rowspan="10">Urban Heat Island Simulation</td><td rowspan="3">Building</td><td>Building, BuildingPart</td><td>16:Building</td><td>Building ID<br>Usage<br>Date of construction</td><td>integer<br>string<br>integer</td><td rowspan="3">MultiSurface, Solid</td><td>LOd2, Lod3, Lod4</td></tr><tr><td>WallSurface</td><td>16:Wall, 16:CurtainWall</td><td>Wall ID<br>Openment of the wall</td><td>integer<br>Double<br>integer</td><td rowspan="2">Surface</td></tr><tr><td>RoofSurface, OuterFloorSurface, OuterCelingSurface</td><td>16:Roof, 16:Covering.</td><td>Roof ID<br>roof type<br>Roof materials</td><td>integer<br>string</td><td>LOd4</td></tr><tr><td>Relief</td><td>TINRelief, BreaklineRelief, MassPointRelief, RasterRelief</td><td>16:TriangulatedFaceSet</td><td>Relief ID<br>hydros deficit (mm)<br>Parasitoly index (%)</td><td>integer<br>Double<br>Double</td><td rowspan="2">TriangulateSurface, MultiPoint, MultiCurve, RectifiedGmdCoverage</td><td>LOd0-4</td></tr><tr><td>Transportation</td><td>Road, TrackTrafficAreaSquare, AuxiliaryTrafficArea</td><td>16:GeographicElement</td><td>Section ID<br>Width of street road (space between buildings) (W in m)<br>length of section (in m)</td><td>integer<br>Double<br>Double</td><td>LOd4</td></tr><tr><td>Vegetation</td><td>PlantCover, SolitaryVegetationObject</td><td>16:GeographicElement</td><td>the plant or vegetated area ID<br>Number of trees per built squares meter (Tres m2)<br>Percentage of low vegetation in the vegetated area (%)</td><td>integer<br>integer<br>Double</td><td rowspan="2">MultiSurface</td><td>LOd2-4</td></tr><tr><td>WaterBody</td><td>WaterSurface</td><td>16:GeographicElement</td><td>WaterSurface ID<br>Type of WaterSurface<br>Surface (m2)</td><td>integer<br>List of string<br>Double</td><td>Surface</td><td>LOd2-4</td></tr><tr><td>CityFurniture</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LandUse</td><td>LandUse</td><td>16:SpacialZone, 16:Building<br>ElementProxy or<br>16:GeographicElement</td><td>LandUse ID<br>FunctionType<br>Surface (m2)</td><td>integer<br>string<br>Double</td><td rowspan="2">MultiSurface</td><td>LOd0-4</td></tr><tr><td>Generics<br>(GenericCityObject)</td><td>(core:CityObject)<br>GenericAttributeSet</td><td>16:BuildingElementProxy<br>(No matching semantic<br>class is available)</td><td>Group ID<br>Building Density (%)<br>Road Coverage (%)</td><td>integer<br>Double<br>Double</td><td>LOd0-4</td></tr><tr><td colspan="8" style="border-bottom: 1px solid black;"></td></tr><tr><td colspan="8" style="border-bottom: 1px solid black;"></td></tr><tr><td colspan="8" style="border-bottom: 1px solid black;"></td></tr><tr><td colspan="8" style="border-bottom: 1px solid black;"></td></tr><tr><td colspan="8" style="border-bottom: 1px solid black;"></td></tr></tbody></table>

In this study, several simulations were performed using numerical tools that are directly interoperable with the CityGML format. Some of the most important parameters required by the different numerical tools are directly derived from data such as surface geometry and orientation, material, etc. that exist in 3D CityGML models. In addition, global urban parameters like the roughness length in urban wind and airflow simulation, result “automatically” from 3D data about obstacle shape, dimensions and relative position, thus making the use of a CityGML extremely advantageous in comparison with the way that it is currently being done.

However, the geometric requirements needed for mesh-based 3D simulations using Computational Fluid Dynamics (CFD) models, such as simulation of wind load on buildings, pollution dispersion, flood propagation, etc., makes the use of the CityGML geometry much less efficient.

In order to carry out the CFD aeraulic simulations, aimed to quantify the pedestrian comfort regarding the wind, we have developed and applied a geometric processing algorithm, using Rhino python API, to automatically simplify and adapt CityGML geometry.

Many projects such as CityDoctor or CityGML Quality Interoperability Experiment concentrate on creating automated procedures which validate and identify sources of errors in city models. However, mainly designed for engineering applications, commercial CFD tools require computer-aided design (CAD) data formats which are used in these disciplines. Hence, a conversion from the GIS data format (i.e. CityGML) to a CAD data format is necessary first [7].

For the application of the processing algorithm, the CityGML model LoD (Level of Detail) 1 and 2, are used as an input data. It is converted into a CAD data structure (STL Format). In this process, not only point objects are needed: additional geometrical and topological information, e.g. surface groups and solid representation, is necessary as well.

In most cases 3D city models still cannot be used in CFD simulations, even if they are transferred to a CAD format. The main reason for this is the presence of geomet-
rical errors detected in the model and of many unnecessary geometric details e.g. short edges or small faces in a building's geometry, which may lead to an unsatisfactory CFD mesh, resulting in non-converging solutions or unacceptably high computing time.

In the first step, the algorithm iteratively treats building geometries (i.e. CAD models) one by one, by solving and eliminates the following errors and topological problems (some examples are shown in Figure 2):

* self-intersections

* non-manifold geometries

* interior surfaces

* holes

* stray elements

* gaps between neighboring faces

* duplicate vertices and faces

* missing polygons

* multiply used edges

* wrong direction of face normal vectors

<figure><img src="image_2.png" /><figcaption>**Fig. 2.** Example of a building and terrain model with quality deficits a) missing ground surface, b) presence of interior surfaces, c) duplicate faces, d) missing polygons, e) gaps between neighboring faces, f) wrong direction of face normal.</figcaption></figure>

Some errors, among the above list, have been corrected automatically using our algorithm, for example, the elimination of separation surfaces inside buildings, missing polygons of the ground surfaces were automatically created. Other errors still need manual correction, e.g. repair holes and gaps in surfaces.

The second geometric specification that had to be addressed is the complexity of integrating buildings and terrain surface into the same model and identifying the valid intersection between them. This process makes additional requirements on the 3D city
model, such as the fact that terrain and buildings must be watertight and without self-intersection.

Connected terrain and building may form a 2-manifold model when only a building exterior is present. This is a common representation of city models which focus on the external terrain and the shapes of buildings.

<figure><img src="image_3.png" /><figcaption>**Fig. 3.** Result of converting between data types and special union operation</figcaption></figure>

The basic operations of Boolean algebra are conjunction, disjunction and negation. For our purposes we only need conjunction. Rhino union-operator can be used to merge buildings and terrain that are connected (see Figure 3). To do this, the building – terrain shell must be perfectly continuous (no holes) at the building/terrain junctions. There can be no creases, interruptions, folds or dangling edges, non-manifold conditions (redundant “T” surfaces) or faces in such junctures.

## 1.2 Integration of new projects in the data base, IFC to CityGML

CityGML had been chosen as the main format of the “La Defense” BIM database. But most of the building project (new construction or renovation) will be produced by CAD BIM Software (ArchiCAD, Revit, Allplan, ...) and exported in IFC format. Most CAD tools cannot directly export to CityGML format which is why it is very
important to implement a complete process, allowing bi-directional conversion between IFC and CityGML formats.

The GeoBIM project [1] shows how building envelopes in CityGML could be extracted from IFC Format. However, one of the conclusions of this work is that this conversion is very difficult, and that some specific geometric details cannot be handled automatically. For example, extracting glass surfaces was very difficult, whereas it is very important to have them in CityGML model, in order to address comfort simulation in public spaces.

The algorithm we developed is based on a quite simple mathematical assumption: if the IFC model is perfectly structured (with all building elements and spaces perfectly in contact), computing the union of all geometries should result with the exact external envelop of the building. This hypothesis has been checked on small synthetic models and turned out to work quite well (see Figure 4).

<figure><img src="image_4.png" /><figcaption>**Fig. 4.** Synthetic tests of IFC to CityGML conversion.</figcaption></figure>

In order to handle larger and “real life” buildings, specific treatments were used to deal with openings (replacing window or door geometry by the geometry of the opening) and curtain walls (grouping all curtain wall part into a unique façade element). By tagging input geometrical elements with the corresponding IFC class, one can assign resulting geometric faces to corresponding CityGML elements (RoofSurface, WallSurface, Window). Finally, a LOD3 CityGML model containing external envelop geometry and all IFC information needed for exploitation or simulation is obtained (see Figure 5).
<figure><img src="image_5.png" /><figcaption>**Fig. 5.** Example of application of the conversion algorithm from IFC 2x3 to CityGML 2.0</figcaption></figure>

**1.3 Need for new parameters: time dependence and use**

In order to properly address overall comfort in outdoor spaces, it is critical to consider not only the different physical environmental parameters human senses are sensitive to (sound pressure, lighting level, outdoor wind speed, temperature and humidity...) but also their time variability. Indeed, first these parameters undergo strong variations from season to season, day to day, hour to hour and even to smaller time scales: for instance, the pass-by a single car in a quiet environment will last maybe a few seconds but will dramatically change nearby sound levels and could possibly cause discomfort to someone sitting nearby reading a book.

Besides, the user of a public space experiences its surrounding environment at a specific time and day, and for a specific duration, from a few seconds to a few hours. Therefore, it is rather meaningless to assess the comfort one might experience based solely on yearly average values, which is nevertheless what is most commonly done in impact studies for instance.

Finally, the sensitivity one has to its environment – and therefore the sense of comfort – depends as well on the type of activity one is doing, and therefore on the use of the public space. As a result, threshold values corresponding to different comfort levels vary as well with the type of activity: reading a book, running, having lunch, etc.

Given those issues (the dependence with time of physical environment descriptors and comfort level, and the dependence with activity of comfort thresholds) and with the objective to more properly assess overall quality of urban spaces, it was found necessary to enrich the physical descriptors of the outdoor environment resulting from numerical simulation:

* include a sufficiently fine time-dependence to provide a meaningful information and at the same time limit data size,

* include a way to take into account the use of the public space.

This has been achieved by acquiring more accurate meteorological data (hourly time series over a year), re-run some simulations and apply specific post-treatment depending on the field.
1.4 Infrared & lighting exposure

Lighting and infrared radiative exposure severely affects visual and thermal comfort.
To achieve accurate analysis of spatial and temporal variations of light exposure,
meteorological data with a time step of less than 30 minutes has been used (see Figure
6).

<figure><img src="image_6.png" /><figcaption>Fig. 6. Example of time history of lighting parameters over a day</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 7. Spectral decomposition of natural light components</figcaption></figure>

Depending on the nature of the available data, various models [2-3] are used to re-
construct illumination values. Other models [4-5] are used as well for spectral distri-
butions (both in the visible and infrared domains) of direct and diffuse illumination as
well as the type of sky (see Figure 7).

Considering temporal variations during a representative year - with a sufficiently
fine time step - corresponds to more than 8000 situations, which raises obvious prob-
lems of calculation time. The use of a parametric sky model considerably reduces this
complexity and allows for finer analyses. This classical approach in lighting simula-
tion [6] breaks down the hemisphere into sectors and carries out the annual assess-
ments in two steps: determination of extended sky factors, and then temporal analy-
sis.
The determination of extended sky factors between the studied areas and the angu-
lar sectors of the sky is of a similar complexity to that of a lighting simulation. Each
portion of the hemisphere can be assimilated to a radiative source. These factors con-
sider direct and indirect contributions (via multiple inter-reflections) and are inde-
pendent of climatic conditions. After this step, each virtual sensor associated with the
areas of interest studied is therefore linked to the radiation conditions through its ex-
tended sky factor vector.

In this work, the Phanie lighting simulation software uses a parametric sky model
with irregular and adaptive decomposition depending on the solar gains of the site in
question (see Figure 8). This sky model also integrates a spectral dimension, for a
more realistic consideration of colored inter-reflections.

<figure><img src="image_8.png" /><figcaption>Fig. 8. Example of an adaptive decomposition of the parametric sky model</figcaption></figure>

This process [7] is very fast and allows for assessments to be carried out over much
longer periods of time in order to obtain more realistic analyses. For each instant in a
typical year, the radiative quantities associated with each virtual sensor are deduced
from simple scalar products between the extended sky factors and the spectral irradi-
ance associated with each sector. These spectral irradiances are deduced from the
climatic conditions at each moment.

In this study, sky coefficients are used to reconstruct global and infrared radiative
inputs.

**1.5 Wind exposure**

In order to assess the influence of wind in thermal comfort, simulated acceleration
factors referenced to the closest meteorological monitoring station have been used to
calculate wind speed and direction at any location (see Figure 9). Meteorological
wind speed has also been tuned to be representative of the site roughness.
<figure><img src="image_9.png" /><figcaption>**Fig. 9.** Methodology to extrapolate wind speed any location and at each time step</figcaption></figure>

## 1.6 Thermo-aeraulic comfort

The overall thermo-aeraulic comfort can be addressed through the Physical Equivalent Temperature (PET) [8] which essentially provides a way to do an energy balance between the human body and the outdoor environment, taking into account conduction (based on temperature difference), convection (based on relative wind speed), radiation (based on infrared exposure) and phase change latency (based on humidity). This calculation can be done at each time step based on meteorological data, simulated wind and infrared exposure results (see Figure 10).

<figure><img src="image_10.png" /><figcaption>**Fig. 10.** Methodology to compute time varying PET based on simulation results</figcaption></figure>
## 1.7 Noise exposure

Time dependence of noise exposure essentially comes from time variability of urban noise sources which are here road and rail traffic, even though one could add that other unmodelled noise sources also induces time variability. On the other hand, usual noise exposure usually considers yearly average noise levels.

To enrich existing noise simulation data and add time variability, it has been decided to define a set of correction factors between yearly average levels and hourly levels, based on the evolution of the two types of traffic, and apply this set of factors to any calculation point in the area of interest. Existing studies on the evolution of road traffic in urban areas [9-10] have shown that the largest variability happens between weekdays and weekends, and from hour to hour. This is also true regarding railway traffic, although the time evolution is specific to each line. Each value of traffic evolutions has then been converted into a correction term on noise level in dB (see Figure 11).

<figure><img src="image_11.png" /><figcaption>**Fig. 11.** Hourly traffic evolution and corresponding correction factor on noise levels in dB (top: road/bottom: railway).</figcaption></figure>

# 2 Implementation and experimentation results

## 2.1 Overall comfort rating

The overall rating is carried out hourly based on physical thermo-aeraulic, radiative and acoustic inputs (see Figure 12). With the climatic database, the time histories are extrapolated over a year and used for the assessment of overall outdoor comfort. The methodology of comfort rating is carried out to combine at each hour all physical input according an approach proposed by Cohen [11]. Each physical time history is coded in a scale 1 to 5 (see Table 1), from worst to best comfort level, according to pedestrian activities (such as standing, seating, walking, etc.). Each activity translates into a different metabolism value when calculating the PET.

After this step, the hourly summation function over all coded time history is carried out. Finally, an occurrence analysis according to two thresholds is carried out to classify environmental quality of each time step (see Table 2), which can be good, mod-
erate or poor, corresponding respectively to a cumulative value over 10, between 10 and 5 and below 5. Finally, this occurrence analysis of environmental quality (comfort rating) time history provide urban planners important decisional information to manage urban space for example with a specific scenario focused on a user's activity, a time period, specific days of the week and for different seasons.

<figure><img src="image_12.png" /><figcaption>**Fig. 12.** Time history input for the overall comfort rating</figcaption></figure>

<table><caption>**Table 1.** PET coding table for all activities</caption><thead><tr><td>PET</td><td>Sensation</td><td>Code</td></tr></thead><tbody><tr><td>&lt; 4°C</td><td>Extremely cold</td><td>1</td></tr><tr><td>4°C à 8°C</td><td>Very cold</td><td>2</td></tr><tr><td>8°C à 13°C</td><td>old</td><td>3</td></tr><tr><td>13°C à 18°C</td><td>Slightly froid</td><td>4</td></tr><tr><td>18°C à 23°C</td><td>Neutral</td><td>5</td></tr><tr><td>23°C à 29°C</td><td>Slightly warm</td><td>4</td></tr><tr><td>29°C à 35°C</td><td>Warm</td><td>3</td></tr><tr><td>35°C à 41°C</td><td>Very warm (health risk)</td><td>2</td></tr><tr><td>41°C</td><td>Extremely warm (danger)</td><td>1</td></tr></tbody></table>
<table><caption>**Table 2.** Time history of multi-component comfort indexes, coding indexes, cumulative comfort and coding of environmental quality.</caption><thead><tr><th>Year</th><th>month</th><th>day</th><th>hour</th><th>minute</th><th>PET time history °C</th><th>Coding PET</th><th>Laeq time history dBA</th><th>Coding Laeq</th><th>wind (m/s)</th><th>Coding wind comfort</th><th>Cumulative comfort</th><th>Environmental quality level</th></tr></thead><tbody><tr><td>1996</td><td>6</td><td>8</td><td>14</td><td>22</td><td>29,5</td><td>2</td><td>67,6</td><td>1</td><td>4,2</td><td>0</td><td>3</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>14</td><td>52</td><td>29,1</td><td>2</td><td>67,6</td><td>1</td><td>4,2</td><td>0</td><td>3</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>15</td><td>22</td><td>28,9</td><td>4</td><td>67,7</td><td>1</td><td>4,2</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>15</td><td>52</td><td>25,6</td><td>4</td><td>67,7</td><td>1</td><td>3,8</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>16</td><td>22</td><td>24,7</td><td>4</td><td>67,6</td><td>1</td><td>3,8</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>16</td><td>52</td><td>24,1</td><td>4</td><td>67,6</td><td>1</td><td>3,8</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>17</td><td>22</td><td>23,0</td><td>5</td><td>67,4</td><td>1</td><td>3,8</td><td>0</td><td>6</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>17</td><td>52</td><td>21,8</td><td>5</td><td>67,4</td><td>1</td><td>3,8</td><td>0</td><td>6</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>18</td><td>22</td><td>21,0</td><td>5</td><td>67,1</td><td>1</td><td>3,8</td><td>0</td><td>6</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>18</td><td>52</td><td>20,2</td><td>5</td><td>67,1</td><td>1</td><td>3,8</td><td>0</td><td>6</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>19</td><td>22</td><td>19,2</td><td>5</td><td>66,6</td><td>1</td><td>3,8</td><td>0</td><td>6</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>19</td><td>52</td><td>17,8</td><td>4</td><td>66,6</td><td>1</td><td>3,8</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>20</td><td>22</td><td>17,1</td><td>4</td><td>66</td><td>1</td><td>3,8</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>8</td><td>20</td><td>52</td><td>16,9</td><td>4</td><td>66</td><td>1</td><td>3,8</td><td>0</td><td>5</td><td>1</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>5</td><td>52</td><td>12,9</td><td>2</td><td>60,6</td><td>2</td><td>0,6</td><td>5</td><td>9</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>6</td><td>22</td><td>13,2</td><td>4</td><td>62,3</td><td>2</td><td>0,6</td><td>5</td><td>11</td><td>3</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>6</td><td>52</td><td>13,1</td><td>4</td><td>62,3</td><td>2</td><td>0,6</td><td>5</td><td>11</td><td>3</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>7</td><td>22</td><td>16,6</td><td>4</td><td>63,4</td><td>2</td><td>0,6</td><td>5</td><td>11</td><td>3</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>7</td><td>52</td><td>18,5</td><td>5</td><td>63,4</td><td>2</td><td>0,6</td><td>5</td><td>12</td><td>3</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>8</td><td>22</td><td>20,3</td><td>5</td><td>64,3</td><td>2</td><td>0,6</td><td>5</td><td>12</td><td>3</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>8</td><td>52</td><td>18,7</td><td>5</td><td>64,3</td><td>2</td><td>0,6</td><td>5</td><td>12</td><td>3</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>9</td><td>22</td><td>22,0</td><td>5</td><td>65,4</td><td>1</td><td>1,0</td><td>5</td><td>11</td><td>3</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>9</td><td>52</td><td>25,4</td><td>4</td><td>65,4</td><td>1</td><td>1,0</td><td>5</td><td>10</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>10</td><td>22</td><td>29,0</td><td>4</td><td>66,2</td><td>1</td><td>1,0</td><td>5</td><td>10</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>10</td><td>52</td><td>30,5</td><td>2</td><td>66,2</td><td>1</td><td>1,0</td><td>5</td><td>8</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>11</td><td>22</td><td>32,0</td><td>2</td><td>66,7</td><td>1</td><td>1,0</td><td>5</td><td>8</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>11</td><td>52</td><td>32,5</td><td>2</td><td>66,7</td><td>1</td><td>1,0</td><td>5</td><td>8</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>12</td><td>22</td><td>33,9</td><td>2</td><td>67</td><td>1</td><td>1,4</td><td>5</td><td>8</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>12</td><td>52</td><td>34,0</td><td>2</td><td>67</td><td>1</td><td>1,4</td><td>5</td><td>8</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>13</td><td>22</td><td>34,3</td><td>2</td><td>67,4</td><td>1</td><td>1,4</td><td>5</td><td>8</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>13</td><td>52</td><td>34,2</td><td>2</td><td>67,4</td><td>1</td><td>1,4</td><td>5</td><td>8</td><td>2</td></tr><tr><td>1996</td><td>6</td><td>9</td><td>14</td><td>22</td><td>33,9</td><td>2</td><td>67,6</td><td>1</td><td>1,4</td><td>5</td><td>8</td><td>2</td></tr></tbody></table>

## 2.2 How to build indicators to assess the quality of public spaces from a user's perspective?

Previous sections of this work focused on physical criteria in order to assess comfort in public spaces. However, when one is interested in urban quality, it is straightforward to realize that this is a notion that is simple to grasp but complex to analyze. If, with the blink of an eye, anyone can tell how good she/he feels in a public space, it is far more complicated to detail and objectivize what this quality is made of: parameters are numerous and there is a part of subjectivity. Thus, the first part of our approach has focused on the definition of indicators relevant to grasp the reality of urban quality in a broad sense. In order to do so, we built on existing methodologies on the assessment of public spaces and definition of urban quality. We then worked with Paris La Defense urban experts to adapt our methodology to the specificities of the territory: we asked them to confirm (or infirm) the relevance of indicators and define ponderation. We ended up with more than 200 indicators grouped in five categories of analysis as following:

* uses and activities,

* accessibility and integration in the urban environment,

* connection to the living,

* comfort and image,

* social interactions.

So that this methodology was user-centered, we elaborated a questionnaire submitted to users of the public space where we conducted the test. The results to this questionnaire on perceptions and representations of the public space were used to define how important the five categories would be. We also integrated perception as a key indicator (maximum ponderation) for each category of analysis.
## 2.3 Methodology to assess urban quality from a user's perspective

We wanted our methodology to assess the urban quality of public spaces to reflect a user-centered approach. Our idea was that the assessment of the urban quality of public spaces should take in account users' perception and representation at first. In order to do so, we built a methodology where the categories of analysis' importance would depend on the results of questionnaires conducted in the studied area on the perception and representation of the public space and where for each category of analysis, perception would be a key indicator.

In complement, we considered that indicators had to be weighed according to their relevance to the territory and asked professionals to do so.

So, our methodology is based on a broad definition of urban quality from a user's perspective. These two principles reflect in the large number of used indicators, the importance of users' perceptions as an indicator for every category of analyses and the ponderation's system based on the results of questionnaires. Our methodology also relies on professionals' expertise for the ponderation of indicators. Finally, the methodology allows to assess the consistency of analysis according to the availability of data.

The methodology has been applied to the studied area, Place Mandela in the area of interest of Paris La Defense. The results shown in Figure 13 is a radar chart where 80 informed indicators are automatically evaluated according to the data collected in the BIM model. The user can select the categories of analysis as well as the parameters needed to evaluate the key indicator he wants to consider.

<figure><img src="image_13.png" /><figcaption>Fig. 13. Urban quality analysis on the area of interest (place Mandela)</figcaption></figure>

## 2.4 Integrating simulation results into urban quality analysis

The last step of the research was to build a common environment for both methodology urban comfort and quality of use. A specific implementation into the OpenBIM
viewer eveBIM has been achieved in order to integrate the results of the different simulation needed. The program enables the user to draw specific zones on the simulation maps so as to define the most interesting area to study comfort results. The calculated comfort criteria are integrated into the corresponding use zones.

<figure><img src="image_14.png" /><figcaption>**Fig. 14.** Example of comfort rating analysis on the simulated area in the eveBIM viewer</figcaption></figure>

The analysis of the quality of use then integrates a temporal dimension that the user must select to visualize the different results on the area and to grasp the overall comfort. Figure 14 shows the area of interest with comfort maps and the result of the overall comfort rating here at 3.375. Calculation details are shown as well, in particular the acoustic comfort and wind comfort corresponding to the given use and the selected period. It will be thus possible to understand why the comfort has obtained a certain rating, and to reconsider urban strategy on the selected area.

# 3 Future works

## 3.1 Storing simulation data

This work has contributed to generate multi-physics, spatial and temporal data which can be merged to data coming from other databases such as environmental sensors, open data (air quality, real time traffic information, quality dimension and availability of available services etc.), IOT, meteorological stations or field investigation (see Figure 15).

This new large database can then be used to gain more accurate knowledge of urban spaces and therefore to manage urban spaces more efficiently. It would then be possible to use new disruptive tools such as machine learning in order to predict short-term environmental quality assessments (health and comfort) and identify new designs, services (for example pedestrian alert or recommendation to use urban space) or management strategies to adapt the urban space to daily life needs.
<figure><img src="image_15.png" /><figcaption>Fig. 15. IOT strategy for data management and storage</figcaption></figure>

## 3.2 Developing a tool to codesign public spaces with users

Another ongoing work concerns the development of a co-design tool based on the research conducted to assess urban quality of public spaces (see Figure 16). The requirements of this tool are based on the needs of Paris La Defense public consultation team. It aims at revealing to end users the complexity of making the right choices when designing public spaces with a high level of quality of use. The tool allows to define zones with different purposes (zones to relax, play, walk...), to implement urban furniture (benches, lights...) and vegetation. It also allows to insert text and images to complement the design. Each added object adds up to the database, and all information can be saved. The tool will allow users to understand how a design choice can impact positively on one aspect of quality of use but negatively on another one.

<figure><img src="image_16.png" /></figure>
<caption>**Fig. 16.** View of the co-design tool under development (eveBIM viewer)</caption>

The next phase will be to test it with users to improve its ergonomics. In a first phase, this tool will be displayed during workshops with a CSTB professional using it according to participants' wishes. This first phase will be used to bring improvements to the overall functioning of our tool aiming at allowing autonomous use in a second phase.

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