Thesis Report

ROYAUME DU MAROC
INSTITUT AGRONOMIQUE
ET VÉTÉRINAIRE HASSAN II
‫المملكة‬
‫المغربية‬
‫معهد‬
‫الحسن‬
‫الثاني‬
‫للزراعة‬
‫والبيطرة‬
‫اململكة‬
‫املغربية‬
ROYAUME DU MAROC
ET VETERINAIRE HASSAN II
‫معهد‬
‫الحسن‬
‫الثاني‬
Adresse : Madinat Al Irfane, B.P. 6202. Rabat – Maroc
Tél : (00 212) 0537 77 17 58/59
Fax : (00 212) 0537 77 58 45
Site web : http://www.iav.ac.ma
‫ب‬ .‫ص‬ :‫العنوان‬
6202
‫الرباط‬ ‫المعاهد‬ ‫الرباط‬
–
‫المغرب‬
:‫الهاتف‬
59
/
58
17
77
0537
(00 212)
:‫الفاكس‬
45
58
77
0537
(00 212)
:‫األنتيرنت‬ ‫موقع‬
http://www.iav.ac.ma
Projet de Fin d’Etudes présenté pour l’obtention
du diplôme d’Ingénieur en Topographie
DEVELOPMENT OF AN INTEGRATED
BIM–IOT–3D GIS APPROACH FOR
RAILROAD INFRASTRUCTURE
MONITORING
Présenté et soutenu publiquement par :
EL FARISSI Salaheddine
Jury :
Pr. EL-AYACHI Moha (Président) IAV HASSAN II
Pr. YAAGOUBI Reda (Rapporteur) IAV HASSAN II
Ing. EL HADDADI Nour-eddine (Rapporteur) SOCOTEC Monitoring France
Pr. ID-RAIS Abderrahim (Examinateur) IAV HASSAN II
Mai 2023

Acknowledgements
I would like to express my sincerest gratitude to Professor YAAGOUBI Reda for their invaluable
guidance and unwavering support throughout the process of my thesis. Their expertise, patience,
and dedication have been instrumental in shaping my research and academic journey. I am truly
grateful for their insightful feedback, constructive criticism, and encouragement, which have
pushed me to strive for excellence. Their mentorship has not only enhanced my knowledge and
skills but has also instilled in me a deep passion for the subject matter. I am honored to have had
the opportunity to work under their supervision, and their invaluable contributions have played a
significant role in the successful completion of my thesis.
I also would like to sincerely thank Engineer EL HADDADI Nour-eddine for their advice and
keen guidance during my internship and thesis at SOCOTEC Monitoring France. Their expertise,
and encouragement have been invaluable in shaping my professional growth. Their dedication and
constructive feedback have inspired me to excel.
I am also grateful to Eng. Thibault Colette, his commitment to quality and attention to detail
have been evident in every project we’ve worked on together.
Many thanks go to my fellow Eng. Guillaume Azema, your positive attitude and collaborative
spirit made a lasting impression on me.
I would like to thank professor Mourad Bouziani for supporting me in doing my internship
abroad.

Abstract
Contemporary cities are increasingly taking on the vocation of connected cities, thanks to
a concordance of several disciplines delivering high mastery of the different life cycles that are
incumbent of the city’s dynamics (architecture, engineering, and construction). To the point of
setting an objective better suited to the multidisciplinary nature of these urban agglomerations,
that of producing sustainable smart cities.
Primo, this work proposes an integration approach of BIM and 3D-GIS with focus on the railway
infrastructure. After a review of IfcTunnel and IfcRail, a 3D model of the railway tunnel was set
to an OpenBIM export through IFC4.3. Then an ETL transformation model, which was tweaked
to better suite the railway infrastructure, was used to convert the IFC model to its counterpart the
CityGML 3.0 standard. This approach allowed us to take advantage of the power of 3D-GIS in terms
of contextualization and situational awareness processes. We also highlighted some challenges in
geometry type conversion and attribute inheritance.
Secondo, we deliver in the context of an IoT-3D GIS approach a proof of concept of an OpenAPI
capable of querying a time-series database that supports an IoT sensing layer. The API took
inspiration from the OGC SensorThings API standard in its conceptual schema and alienated
itself with a technical implementation based on an Apache abstraction layer to the different app
components. The approach pushed a horizontal scaling allowing for near-permanent availability
of data from Internet of Things (IoT) sensors as well as historical archiving of sensors position.
After investigating the API architecture, in addition to looking at previous works on IoT databases,
we proceeded to augment the API with a No-SQL approach to achieve an effective and efficient
database query time. With hyper-tables, TimescaleDB improved insert and query performance by
partitioning time-series data on its time parameter.
Terso, our choice to adopt the SensorThings API, as a primary pool for our semantic web
application, has enabled us to put forward a scalable and powerful spatial querying capabilities for
the underlying time series database. Furthermore, a Dynamo script, connecting SensorThings API
and our BIM-3D GIS model, is used to update in real time a spatially queried IfcSHMSystem rail’s
parameter. We use ArcGIS Data Interoperability Extension to ingest our model using the OGC
Indexed 3D Scene Layers (I3S) container which is then consumed by the ArcGIS Online scene
viewer for monitoring rail activity, Autodesk Platform Services (formerly Forge) (APS) is used for
dash-boarding sensor measurements on corresponding Industry Foundation Classes (IFC) model.
Keywords: BIM, SIG3D, IoT, Time series, SensorThings API, CityGML

Résumé
Les villes contemporaines prennent de plus en plus la vocation de villes connectées, grâce à
une concordance de plusieurs disciplines délivrant une grande maîtrise des différents cycles de vie
qui incombent à la dynamique de la ville (architecture, ingénierie, et construction). Au point de se
fixer un objectif mieux adapté à la nature pluridisciplinaire de ces agglomérations urbaines, celui
de produire des villes intelligentes durables.
Primo, ce travail propose une approche d’intégration du BIM et du SIG 3D en mettant l’accent
sur l’infrastructure ferroviaire. Après un examen des deux standards BuildingSMART IfcTunnel
et IfcRail, un modèle 3D du tunnel ferroviaire a été conçu pour une exportation OpenBIM par le
biais d’IFC. Ensuite, un modèle de transformation Extract, Transform and Load (ETL), adapté à
l’infrastructure ferroviaire, a été utilisé pour convertir le modèle IFC en son équivalent, le standard
CityGML 3.0. Cette approche nous a permis de tirer parti de la puissance du SIG 3D en termes de
contextualisation et de processus de connaissance de la situation. Nous avons également mis en
évidence certains défis liés à la conversion des types géométriques et à l’héritage des attributs.
Secondo, nous présentons, dans le contexte d’une approche SIG IoT-3D, une preuve de concept
d’une API ouverte capable d’interroger une base de données de séries temporelles qui prend en
charge une couche de détection IoT. L’API s’est inspirée de la norme OGC SensorThings API dans
son schéma conceptuel et s’est aliénée avec une implémentation technique basée sur une couche
d’abstraction Apache pour les différents composants de l’application. L’approche favorise une mise
à l’échelle horizontale permettant une disponibilité quasi-permanente des données provenant
des capteurs IoT ainsi qu’un archivage historique de la position des capteurs. Après avoir étudié
l’architecture de l’API et les travaux antérieurs sur les bases de données IoT, nous avons ajouté à
l’API une approche No-SQL afin d’obtenir un temps d’interrogation de la base de données efficace
et efficient. Avec le concept des hypertables, TimescaleDB a amélioré les performances d’insertion
et de requête en partitionnant les données de séries temporelles en fonction de leur paramètre
temporel.
Terso, notre choix d’adopter l’API SensorThings, comme pool principal pour notre application
web sémantique, nous a permis de mettre en avant des capacités d’interrogation spatiale évolutives
et puissantes pour la base de données de séries temporelles sous-jacente. En outre, un script
Dynamo, reliant l’API SensorThings et notre modèle SIG BIM-3D, est utilisé pour mettre à jour en
temps réel un paramètre de santé structurelle relatif à chacune des rails. Nous utilisons ArcGIS
Data Interoperability Extension pour ingérer notre modèle à l’aide du conteneur OGC I3S qui est
ensuite consommé par le visualiseur de scène ArcGIS Online pour surveiller l’activité ferroviaire.
APS est utilisé pour dash-boarder les mesures des capteurs sur le modèle IFC correspondant.
Mots clés : BIM, SIG3D, IoT, Séries temporelles, SensorThingsAPI, CityGML

Contents
Extended abstract (In French) 1
General Introduction 6
1 Introduction 6
2 Problem Framing 7
3 Objectives 8
4 Methodology 8
5 Thesis Outline 9
I Previous Works 10
Chapter 1 — BIM-3D GIS Integration Patterns 11
1 Introduction 11
1.1 Building information modeling (BIM) 12
1.2 Geographical Information System (GIS) 12
1.3 BIM/GIS Data Integration 13
2 Conclusion 14
Chapter 2 — 3D GIS-IoT Integration Patterns 15
1 Introduction 15
1.1 Internet of Things (IoT) 15
1.2 3D GIS-IoT Integration Strategies 18
2 Conclusion 19
II Materials & Methods 21
Chapter 3 — Implementation & Results 22
1 Introduction 22
2 Case study (Tour Triangle Project) 22
2.1 Overview 22
v

3 Hardware & Software 22
3.1 Hardware 22
3.2 Software 23
3.3 Tech Stack 25
4 Scan to BIM (3D Point Cloud to IFC) 26
4.1 Field Laser Scanning 26
4.2 Registration 26
4.3 Preprocessing 27
4.4 Parametric 3D BIM Modeling 29
4.5 Geo-referencing with shared coordinates 31
4.6 OpenBIM Export 31
5 BIM to 3D GIS (IFC to CityGML) 32
5.1 Reading source IFC 32
5.2 Mesh & attribute setting 32
6 Extended SensorThings API 33
6.1 Deployment architecture 33
6.2 Database performance 34
6.3 Authentication & Authorisation 36
6.4 Creating spatial entities 36
6.5 External Joining with BIM and 3D GIS 36
7 IFC-SensorThings-I3S Integration Platform 38
General Conclusion 41
Appendices 43
Dynamo Node to Access Data from SensorThings API Endpoint 44
FROST-Server docker-compose file 45
Tunnel Entity 46
Bibliography 47

List of Figures
1.1 Tunnel isolé de son environnement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Distance entre deux rails mesurée pour préparer la modélisation . . . . . . . . . . . . 4
1.3 Dégagement du contrôle de santé intégrée des rails . . . . . . . . . . . . . . . . . . . . 5
1.4 Plateforme d’intégration basée IFC-SensorThings-I3S . . . . . . . . . . . . . . . . . . 5
2.1 BIM-3D GIS integration pattern distribution (Ma and Ren, 2017) . . . . . . . . . . . . 7
2.2 GIS-BIM-IoT Nodes Integration Pattern (Isikdag, 2015) . . . . . . . . . . . . . . . . . . 8
2.3 Thesis methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.1 Model of a mechanical room developed from lidar data (Oregon State University) . . 12
1.2 Underground Saint Denis Pleyel Station (Esri France) . . . . . . . . . . . . . . . . . . . 13
2.1 Components of an IoT architecture (Isikdag, 2015) . . . . . . . . . . . . . . . . . . . . 16
2.2 Data model of OGC SensorThings API part 1 (Liang et al., 2016) . . . . . . . . . . . . . 17
2.3 Data model of OGC SensorThings API part 2 (Liang et al., 2016) . . . . . . . . . . . . . 18
2.4 Example query of SensorThings API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1 Tour Triangle (tour-triangle.com) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 RTC360 3D Laser Scanner (Leica Geosystems) . . . . . . . . . . . . . . . . . . . . . . . 23
3.3 Cyclone REGISTER - Point cloud registration software (Leica Geosystems) . . . . . . 24
3.4 CloudCompare - Open Source 3D point cloud processing software (danielgm) . . . . 24
3.5 ReCap Pro - Capture detailed models of real-world assets (Autodesk) . . . . . . . . . 24
3.6 Revit - BIM software for designers and builders (Autodesk) . . . . . . . . . . . . . . . 25
3.7 FME Desktop (Safe Software) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.8 TimescaleDB for time-series and analytics (Timescale) . . . . . . . . . . . . . . . . . . 25
3.9 ReactJS for web and native user interfaces (React) . . . . . . . . . . . . . . . . . . . . . 26
3.10 Docker for accelerated and containerized applications (Docker) . . . . . . . . . . . . 26
3.11 Tunnel 3D Point Cloud extent in 2D view (Bing Maps) . . . . . . . . . . . . . . . . . . 27
3.12 E57 Point Cloud after registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.13 Tunnel section that will be rendered for modeling . . . . . . . . . . . . . . . . . . . . . 28
3.14 Annotated tunnel (height = 5m, width = 13m and length = 47m) . . . . . . . . . . 28
3.15 Manual point cloud cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.16 Tunnel separation into railway and tunnel . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.17 Rail and sleeper design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.18 Railway track model integrated into its corresponding point cloud . . . . . . . . . . . 30
vii

3.19 Longitudinal section of tunnel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.20 Tunnel theoretical axis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.21 3D BIM model of tunnel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.22 Bearing of metro line 12 in RGF93 CC49 . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.23 IFC model of tunnel (BIMvision) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.24 FME Transformation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.25 Standard based FROST-Server (FROST-Server) . . . . . . . . . . . . . . . . . . . . . . . 34
3.26 FROST-Server Deployment Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.27 Indexing timestamps using B-Tree index . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.28 Indexing locations using GiST index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.29 Indexing JSONB fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.30 Time bucketing time series data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.31 SensorThings API database diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.32 SensorThings API Docker server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.33 APS DataViz Extensions (Data Visualization Extension) . . . . . . . . . . . . . . . . . . 38
3.34 Updating in real time the Structural Health Monitoring (SHM) of railroad tracks . . . 39
3.35 Automating data integration workflows with FME Server . . . . . . . . . . . . . . . . . 39
3.36 Scene Layers for 3D GIS models (Scene Layers) . . . . . . . . . . . . . . . . . . . . . . 39
3.37 Prism measurements displayed with IFC model . . . . . . . . . . . . . . . . . . . . . . 39
3.38 React plotly.js . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.39 Underground tunnel with its surroundings . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.40 An integrated BIM-IoT-3D GIS platform . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
A.1 Dynamo script to update railway structural health . . . . . . . . . . . . . . . . . . . . 44
B.1 FROST-Server docker-compose file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
C.1 Tunnel Entity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

List of Tables
2.1 Comparison of different 3D GIS-IoT integration strategies . . . . . . . . . . . . . . . . 18
ix

Listings
4.1 Importing synthetic data to FROST-Server . . . . . . . . . . . . . . . . . . . . . . . . . 42
x

Acronyms
ADE Application Domain Extension
AEC Architecture, Engineering and Construction
API Application Programming Interface
APS Autodesk Platform Services (formerly Forge)
BIM Building Information Modeling
DOM Document Object Model
ETL Extract, Transform and Load
GIS Geographical Information System
GML Geography Markup Language
HTTP Hypertext Transfer Protocol
I3S Indexed 3D Scene Layers
IFC Industry Foundation Classes
IoT Internet of Things
JVM Java Virtual Machine
JSON JavaScript Object Notation
LiDAR Light Detection and Ranging
LoD Level of Detail
MQTT MQ Telemetry Transport
OGC Open Geospatial Consortium
rcp ReCap
REST REpresentational State Transfer
RGB Red, Green and Blue
xi

SHM Structural Health Monitoring
UCS User Coordinate System
URI Uniform Resource Identifier

Extended abstract (In French)
Mise en contexte
La ville intelligente (Smart City) est devenue un choix stratégique et éminent pour beaucoup de
pays dans le monde, un paradigme à la fois complexe et multidisciplinaire dont la gestion des
données spatiales alliées est devenue un sujet de recherche brûlant. Les Systèmes d’Information
Géographique 3D (SIG 3D) et le Bâti Immobilier Modélisé (BIM) sont deux disciplines essentielles à
cet égard, et bien que le développement des applications SIG ait une longue histoire et que le BIM
se voit également développé depuis plus de 10 ans, le développement d’une approche intégrée
dans ce sens ouvre une nouvelle voie de recherche et d’application (Ma and Ren, 2017).
Cela dit, une telle intégration se justifie par le fait que le BIM allié au SIG permettra dans le cadre
des quartiers urbains de développer des applications telles que les programmes d’intervention,
l’efficacité énergétique des bâtiments et surtout les plans d’urgence (Emergency response). (Isikdag,
2015) a pu développer une preuve de concept en alliant des informations provenant de sources
multiples ; de la maquette BIM et du système sensoriel IoT, le tout dans un SIG3D, qui a profitait
à de nombreux domaines de gestion urbaine par le biais du Web3D. Dans la mesure où, il est
également possible que chaque objet de la ville soit représenté sous la forme d’un capteur virtuel
ou soit associé à des capteurs virtuels.
(Vilgertshofer et al., 2017), à travers une investigation sur l’établissement d’une connexion
sémantiquement riche entre le BIM et le SIG3D ont conclu qu’une intégration dans ce sens est très
souhaitable. Pourtant comme le BIM et le SIG3D reposent sur différents formats d’échange (IFC et
CityGML), une conversion entre les modèles de données entraînera inévitablement une perte de
données.
Ainsi, comme une ville peut être considérée comme un écosystème vivant, les modèles urbaines
les plus précis utiliseront des informations provenant à la fois i.) des représentations des bâtiments
dans un modèle 3D de la ville, ii.) de la maquette BIM et iii.) des capteurs IoT réels et virtuels
(Isikdag, 2015).
Cependant, les applications d’intégration BIM-SIG3D sont encore à leurs balbutiements, ainsi
l’intérêt d’une telle approche n’a pas été exploité proprement. En effet, l’utilisation des traitements
SIG se vaut d’une utilité basique, notamment que les SIG représentent un vaste domaine couvrant la
prise de décision basée sur la géo-visualisation et la modélisation géospatiale plutôt qu’un système
de visualisation 3D de l’environnement bâti et des villes (Song et al., 2017).
1

Méthodologie
Problématique
Les capteurs de constrôle de santé intégré sont largement utilisés pour surveiller et monitorer les
infrastructures génie civil, tels que les ponts, les barrages, les tours, les tunnels etc. Ces capteurs
topographiques et géotechniques sont considérés comme une couche de détection d’un système
IoT qui alimente des bases de données dédiées, par des séries temporelles en temps réel.
Par ailleurs, il est indispensable d’avoir une approche efficace de visualisation et d’analyse de
ces données pour pouvoir les comprendre rapidement et facilement ainsi que prendre des bonnes
décisions.
Dans ce cadre, plusieurs études encouragent l’intégration du jumeau numérique allié aux
données IoT dans un SIG3D pour mieux appréhender l’enjeu du monitoring urbain durable.
En effet, (Isikdag, 2015) a souligné que les applications de monitoring des villes basées sur
le SIG3D nécessitent la mise en commun d’une panoplie de données provenant de ressources
multiples ; de la maquette BIM, du modèle 3D de la ville et des capteurs IoT.
L’OpenBIM avec son standard d’échange IFC est utilisé pour permettre l’échange et le partage
des informations de conception en temps réel. Les modèles 3D de la ville utilisent les informations
transférées du Building Information Modeling (BIM) pour représenter la sémantique, tandis que
les informations en temps réel concernant l’intérieur doivent être fournies par les capteurs dans
des noeuds IoT (Isikdag, 2015).
Ce projet de fin d’études se veut proposer une méthode d’intégration basée sur le SIG3D des
informations résidant dans le jumeau numérique et des informations acquises à partir du système
sensoriel.
Une fois cette intégration réalisée, de nombreuses applications allant de la gestion des seuilles
d’alerte au monitoring des bâtiments en danger en bénéficieront.
Par conséquent, ce travail de fin d’études vise à répondre à la problématique suivante : Com-
ment peut-on améliorer les processus des plans d’urgence en se basant sur les données d’auscultation
des ouvrages d’art ?
Objectifs
Afin de répondre à la problématique soulevée, l’objectif principal de ce projet de fin d’études est de
développer une approche d’intégration BIM-IoT-SIG3D pour le monitoring des ouvrages d’art.
Auscultation d’une infrastructure ferroviaire.
Afin de rendre concret notre objectif, il nous a été judicieux de passer par un enchainement
d’objectifs spécifiques :
1. Réaliser une revue de littérature sur les approches d’intégration BIM-IoT-SIG3D ;
2. Proposer une approche d’intégration BIM-SIG3D en se basant sur les deux standards IFC &
CityGML ;
3. Intégration des données capteurs dans la maquette BIM-SIG3D en adoptant SensorThings
API.
2

Approches & Outils
Traitement du nuage 3D
Dans le cadre du projet de la Tour Triangle à Paris. Un relevé LiDAR a été effectué afin de contrôler
la stabilité du tunnel en-dessous. Les données ont été acquise par un scanner terrestre de Leica
RTC360. Le résultat obtenu est un nuage 3D en format eps. La figure 1.1 montre le nuage 3D du
tunnel isolé de son environnement qui sera assujeti au traitement et à la modélisation.
Figure 1.1: Tunnel isolé de son environnement
Le nuage de points contient plus de 92 millions de points coloriés codés en 16 bits. Et en prenant
en considération la capacité technique de la station de travail, nous nous sommes concentrés sur
un tronçon du tunnel dont les caractéristiques géométriques sont bien apparentes.
Le nettoyage du nuage de points a été effectué avec la solution Open-source CloudCompare.
Le nettoyage consiste en une élimination des parties qui n’entre pas dans la partie assujettie de la
modélisation.
Création de la maquette BIM
Le processus scan-to-BIM consiste à suivre les étapes suivantes :
• Acquisition du nuage de points ;
• Nettoyage du nuage ;
• Importation du nuage dans une solution BIM ;
• Modélisation des objets ;
• Ajout de la topologie et de la sémantisation à la maquette.
Nous avons adopté Autodesk Revit comme solution BIM.
3

Afin de pouvoir importer le nuage sur Revit, la conversion du nuage en format rcp (Point Cloud
File) a été nécessaire. Et dans le but de mieux maîtriser la géométrie des rails et du tunnel. Une
séparation du nuage a été adopté pour mieux nettoyer les structures et les modéliser.
La modélisation concernera les traverses et les deux rails de la voie. La stratégie adoptée est
d’avoir un lien topologique entre les traverses et les rails de manière que les traverses supportent
les rails.
Ainsi nous utiliserons la notion des familles imbriquées supportées par Revit ; cette démarche
a été testé dans notre étude d’interopérabilité avec le SIG 3D. Nous soulignons aussi que la modéli-
sation utilisera des primitives simples afin d’éviter d’éventuelles erreurs au niveau de la traduction
des géométries vers notre environnement de SIG 3D. La figure 1.2 montre la disposition des rails
sur le nuage 3D associé.
Figure 1.2: Distance entre deux rails mesurée pour préparer la modélisation
Synchronisation de SensorThings API avec IFC et CityGML
L’API SensorThings de l’OGC se vaut une approche ouverte, géospatiale et unifiée qui a pour but
d’interconnecter une couche de détection IoT avec les applications Web qui les utilisent (Liang
et al., 2016).
La partie Sensing permet aux dispositifs et applications IoT de CRÉER, LIRE, METTRE À JOUR
et SUPPRIMER (c’est-à-dire HTTP POST, GET, PATCH et DELETE) des données et métadonnées
IoT dans un service SensorThings. L’API SensorThings donne accès à des informations de mesure
actualisées qui sont :
• RESTful
• Encodés (Geo)JSON
• Disponible via les modèles d’URL OASIS Odata et les options d’interrogation
• Capable d’inclure directement les données des capteurs via le protocole ISO MQTT
Cela signifie que les données de l’API peuvent être facilement visualisées à l’aide d’un navigateur
Web normal. On peut simplement naviguer d’un objet à l’autre en cliquant sur les URL fournies
dans les données.
Pour profiter de telle approche, nous avons utilisé une implémentation technique préexistante
de ce standard : Frost-Server.
Résultats
Pour mettre en oeuvre tous ces composants, nous avons développé une application sous React
avec un back-end sous forme d’un conteneur Docker combinant notre serveur SensorThings avec
une base de données PostgreSQL.
4

Le serveur SensorThings, développé grâce à une implémentation technique de Frost-Server
certifiée OGC, et mis dans un contenaire sous Docker pour un CI (intégration continue)/CD (dé-
ploiement continu) efficient, permet d’afficher les observations en temps réel parvenant de notre
couche IoT sous en forme de graphes grâce à la librairie React Plotly.js. En parallèle une extension
APS DataViz vient augmenté le viewer des maquettes BIM d’autodesk avec des fonctionnalité
d’interfacage et de visualisation.
Finalement, grâce au viewer d’ArcGIS Online, nous avons pu axploité la maquette CityGML 3.0
à travers le format I3S qui sert à partager sur le Web des modèles 3D d’une taille considérable, et ce
pour situer l’ouvrage souterrain dans son environnement ainsi que pour afficher l’état de santé
structurelle des rails. La figure 1.3 montre un dégagement du contrôle de sonté intégrée de l’un
des rails, et la figure 1.4 montre la mise à disposition des différents éléments de la plateforme Web
développée sous React.js.
Figure 1.3: Dégagement du contrôle de santé intégrée des rails
Figure 1.4: Plateforme d’intégration basée IFC-SensorThings-I3S
5

General Introduction
1 Introduction
With the concepts of Smart City attracting the infrastructure industry, the methods of managing
spatial objects has become a hot research topic. Relating to this topic, 3D Geographical Information
System (GIS) and BIM are two critical technologies. Although the application of GIS has a long
history and BIM has seen a steep development over the last 10 years, their integrated use starts a
new direction and is still at the early exploration stage (Ma and Ren, 2017).
Smart City is a vision to integrate IoT solutions in a secure fashion to manage a city’s assets. In
order to know what is happening in the city, smart city applications need mass data, both static
and dynamic, current and historical, geometrical and semantic, microscopic and macroscopic etc.
Once collected, management and application of these data often use technologies such as BIM and
GIS.
BIM can be used to manage the lifecycle data of vertical facilities such as infrastructures while
GIS can be used to store, manage and analyze data describing the urban environment, which is
horizontally distributed. Hence, integrated application of BIM and GIS is essential in Smart City
applications where data of both facilities and urban environment are required (Ma and Ren, 2017).
From the charts in figure 2.1 we can grasp the major researches on integrated application of BIM
and GIS.
(Isikdag, 2015) stated that GIS based city monitoring / city management applications require
the fusion of information acquired from multiple resources, BIM, City Models and Sensors. He
provided a method for facilitating the GIS based fusion of information residing in digital building
Models and information acquired from the city objects i.e. Things. Once this information fusion
is accomplished, many fields ranging from Emergency Response, Urban Surveillance, Urban
Monitoring to Smart Buildings will have potential benefits. The figure 2.2 proposes an integration
pattern for the BIM-IoT-3D GIS approach as developed in the aforementioned study.
To this regard, BIM and GIS methodologies have been integrated to create a multi-scale vision
of the territory to the advantage of different applications with needs that could not be satisfied
with the use of only BIM or GIS. This integration is complex due to the differences between the
two methodologies in terms of detail levels, geometric representation methods, archiving methods
and semantic contents. The differences between software, standards and data types used by both
methodologies pose some problems for their integration, problems related to the geometry and
semantics used. Nevertheless, the integration of BIM data and spatial data related to the context in
6

Figure 2.1: BIM-3D GIS integration pattern distribution (Ma and Ren, 2017)
which the building is located offers the possibility of maintaining clear situational awareness on a
large number of spatial objects. All this makes the planning of interventions more informed, aware
and therefore more effective (Vacca and Quaquero, 2020).
2 Problem Framing
SHM sensors are used to monitor civil engineering infrastructures, such as bridges, dams, towers
and railroad. These IoT nodes are considered as a sensing layer coupled with a tasking system that
feeds dedicated databases with real-time time series.
To this regard, it is extremely quintessential to adopt an effective integration ontology combining
the visualization and the spatial querying of such geometric and semantic data, in order to
understand it quickly and to easily inform a viable metric depicting the monitored entities.
In this context, several studies encourage the integration of the digital twin model combined
with IoT data in a 3D GIS environment to better grasp the issue of sustainable urban monitoring.
(Isikdag, 2015) clearly stated that the loosely coupled nature of an architecture based on BIM-3D
GIS would bridge the key technologies for acquiring and presenting real-time building information
and information acquired from sensors.
With such directive, infrastructure information gathered from the virtual sensors such as settle-
ment will be available for the interested parties regardless of their hardware, operating system and
software they use. Similarly, information gathered from city objects such as air pollution, sound
levels, will be available in real time. Hence a detailed description offered by BIM, a geospatial
context supported by 3D GIS and monitoring capabilities offered by the IoT layer pushes an overall
integral framework for grasping structural health of the civil engineering infrastructure.
Our current work proposes an integration pattern based on the 3D GIS environment, the
information residing in the digital twin model and the information acquired from the sensory
system. Once this integration is achieved, many applications ranging from the management of
7

Figure 2.2: GIS-BIM-IoT Nodes Integration Pattern (Isikdag, 2015)
alert thresholds to the monitoring of urban infrastructure will benefit from it.
Therefore, the problem framing of this thesis is as follows: How can we improve the processes
of monitoring railroad infrastructure which is equipped with multiple IoT sensory nodes ?
3 Objectives
Following the problem framing the we just exposed, the main objective of this thesis is to develop
a BIM-IoT-3D GIS integration pattern for monitoring civil engineering infrastructures (Case
study—Monitoring underground railroad).
In order to make our goal concrete, it made sense for us to go through a sequence of specific
objectives:
1. Conduct a literature review on BIM-IoT-3D GIS integration approaches;
2. Propose a BIM-3D GIS integration approach based on the two standards IFC and CityGML;
3. Integration of sensor data into the BIM-3D GIS model by adopting SensorThings API.
4 Methodology
Hereafter, we describe the methods and techniques used to conduct the study. We explain how the
research questions and hypotheses were addressed and how the data was collected and analyzed.
Our methodology (figure 2.3) ensures the validity, reliability, and reproducibility of the research
findings.
8

Figure 2.3: Thesis methodology
5 Thesis Outline
To achieve our underlying goals, the current thesis is structured into three chapters.
The first chapter details different BIM-3D GIS integration patterns and specifically it exposes
key bottlenecks related to format exchange notably those related to geometry conversion and
attribute inheritance.
The second chapter tackles the 3D GIS-IoT integration paradigm through discussing different
concepts relating to ingesting, querying and historical archiving of time series data as well as
choosing SensorThings API as the underlying database supporting our BIM-IoT-3D GIS integration.
The last chapter has a role of implementing a multi-scale solution using multiple tools and
technical implementations that we justify its use and discuss its inherent limitations.
9

Chapter 1 — BIM-3D GIS Integration
Patterns
1 Introduction
BIM and 3D GIS are two powerful tools that can be integrated to provide a comprehensive under-
standing of the built environment. The integration of BIM and GIS can offer a range of benefits
such as increased collaboration, improved decision-making, and enhanced data management.
However, there are various ways in which BIM and GIS can be integrated, and each method has
its own advantages and disadvantages. (Zhu and Wu, 2021) stated that the development of smart
cities applications coupled with its corresponding digital twins requires the integration of Building
Information Modeling (BIM) and Geographic Information Systems (GIS), where BIM models are to
be integrated into GIS for visualization and/or analysis.
Some common BIM 3D-GIS integration patterns include:
• Federated Integration: In this pattern, BIM and GIS data are maintained in separate systems
and are federated when needed. This allows each system to maintain its own data model and
schema, but requires additional effort to ensure that the data is kept synchronized.
• Aggregated Integration: In this pattern, BIM and GIS data are aggregated into a single system,
allowing for a unified view of the built environment. This can simplify data management and
reduce duplication, but may require significant effort to reconcile differences in data models
and schemes.
• Linked Integration: In this pattern, BIM and GIS data are linked through a common identifier,
such as a location or asset tag. This allows for easy navigation between the two systems and
can improve data quality by leveraging data validation and workflows in each system.
• Service-based Integration: In this pattern, BIM and GIS data are exposed as web services
that can be consumed by other systems. This allows for easy integration with other systems
and can reduce the need for data duplication, but requires additional effort to develop and
maintain the web services.
Each integration pattern has its own strengths and weaknesses and the choice of pattern will
depend on the specific requirements of the application. It is important to carefully consider the
11

integration pattern before starting the integration process to ensure that the chosen approach
meets the application’s needs and goals.
1.1 Building information modeling (BIM)
BIM can be defined as a set of interacting processes, roles, policies, and technologies, creating
virtual information-based models to manage data in the digital format used within the AEC industry
(Alizadehsalehi et al., 2020).
The effort involved could include new and existing projects such as residential construction,
commercial construction, industrial construction (figure 1.1), heavy construction, infrastructure
construction, and heritage construction.
OpenBIM
OpenBIM extends the benefits of BIM by improving the accessibility, usability, management and
sustainability of digital data in the built asset industry.
At its core, OpenBIM is a collaborative process that is vendor-neutral. OpenBIM processes
can be defined as sharable project information that supports seamless collaboration for all project
participants. OpenBIM facilitates interoperability to benefit projects and assets throughout their
life cycle.
Figure 1.1: Model of a mechanical room developed from lidar data (Oregon State University)
1.2 Geographical Information System (GIS)
A 3D GIS is a software system that enables the visualization, management, and analysis of geospatial
data in a three-dimensional (3D) space. It allows users to represent and analyze spatial data, such as
buildings, terrain, and infrastructure, in a more realistic and accurate way by incorporating height
or elevation information. This type of GIS can be used for a variety of applications, including urban
planning, environmental management, emergency response, and asset management. In (figure
1.2) we can see a real application of an underground metro line that is used to manage the project
lifecycle.
In recent years, the world of representation and the world of semantic content are increasingly
intertwining; in this perspective, (Pepe et al., 2021) stated that GIS has represented the first step
12

for the integration of geographical information connected to an object with semantics or data
information.
Figure 1.2: Underground Saint Denis Pleyel Station (Esri France)
1.3 BIM/GIS Data Integration
The integration between these two domains and the complementary nature of the information
provided by each technology lead in most cases to have a new data flow and a highly detailed and
holistic picture of a project (Colucci et al., 2020).
IFC-to-shapefile Conversion
(Zhu and Wu, 2021) stated that The IFC-to-shapefile path is more workable for BIM-to-GIS data
conversion, for four reasons.
1. There are fewer and easier conversion tasks in IFC-to-shapefile conversion. For example,
the challenging solid-to-surface conversion and class mapping, which are mandatory for
the IFC-to-CityGML conversion, are not required by the IFC-to-shapefile conversion. In this
sense, the data conversion from BIM to GIS can be completed in an easier manner;
2. Behind shapefile are mature GIS systems, such as the prevalent ArcGIS. These systems have
strong data management and analysis capacity that get shapefile ready for practical use, while
CityGML models have to first be converted before they can be used in ArcGIS;
3. In terms of shapefile itself, shapefile supports both solid models and surface models, which
makes shapefile capable of accommodating 3D IFC geometry, and the relational database
technique behind shapefile enables it to store, extend, and query IFC semantic information;
4. shapefile is an open format widely used for geospatial data exchange. It has been adopted
by researchers, industry, and governments, such as the Landgate of Western Australia and
13

Data.gov.au, which provides open government data in Australia. All of these advantages make
the IFC-to-shapefile path more realistic for the use of building models in GIS.
IFC-to-CityGML Conversion
Moreover (Zhu and Wu, 2021) also stated that the IFC-to-CityGML conversion has to deal with
more conversion tasks, and some of them are quite challenging in both geometry conversion and
semantics transfer.
The geometry conversion for the IFC-to-CityGML path is more difficult than the IFC-to-
shapefile path, as it involves the change of the modeling paradigm (from solid modeling to surface
modeling) and the conversion of Level of Detail (LoD).
In terms of semantics transfer, class mapping is a unique task that is mandatory for the IFC-
to-CityGML conversion. A large amount of work has been carried out to address this problem by
developing new data schemas or modifying current data schemas.
In addition, Application Domain Extension (ADE) can be developed for CityGML to receive
additional semantic information from IFC. The IFC-to-CityGML path has potential to be the
standardized way for accommodating BIM information but is more difficult to realize. Despite the
efforts mentioned above, it is still problematic in both geometry conversion and semantics transfer.
An easy-to-do and efficient approach for geometry conversion is still absent, not to mention
that ADEs developed by various projects were project-specific and may not be recognized by
some visualization tools. This is probably the reason that CityGML was rarely used in studies on
application-level BIM/GIS integration.
2 Conclusion
BIM and GIS are two important technologies, and in recent years, there has been growing interest
in integrating these two to improve the management and analysis of spatial data related to con-
struction projects. This research has investigated the integration pattern of BIM and 3D-GIS to
understand its benefits, challenges, and potential solutions.
The research findings suggest that the integration of BIM and 3D-GIS can provide significant
benefits to monitoring projects. However, the integration of BIM and 3D-GIS also presents several
challenges such as data interoperability, data quality, and data security.
To overcome these challenges, various approaches have been proposed, including the use of
common data models, data mapping techniques, and the adoption of open standards such as
the Industry Foundation Classes (IFC) and Open Geospatial Consortium (OGC) standards. The
adoption of these approaches can improve data interoperability and quality while ensuring data
security.
14

Chapter 2 — 3D GIS-IoT Integration
Patterns
1 Introduction
The management and monitoring of railroad infrastructures demand the use of a tremendous
amount of data about their maintenance history and current status. One very characteristic of this
information is its geographic nature, which suggests that Geographic Information Systems (GIS) are
appropriate to facilitate the way we handle it. However, the access to such information nowadays
is challenging because of numerous reasons: some data is still mainly stored on paper; databases
are out of date; managing scarcely existing records and creating new ones is quite a laborious and
time-consuming task; field monitoring require human resources and are expensive; and so forth.
Thus, we need to make smarter the way management and monitoring of railroad infrastructures
are performed. Some promising technologies appeared in the last few years to overcome a number
of the identified issues.
1.1 Internet of Things (IoT)
Today the role of Internet is evolving from being a communication medium for people to being a
communication medium between people, between people and devices, and between devices.
IoT approach defines a global network structure based on standards and set of communication
protocols where physical and virtual devices (i.e. known as Things) communicate with each other,
publish, consume, exchange and share information in real time.
IoT implementation enables “Things” (i.e. devices, city objects, building elements...) to com-
municate with each other and this provides unique opportunities for the development of smart
buildings and smart cities. In smart buildings which implement the IoT approach, a door would
have the ability to connect with the fire alarm, or a chair would communicate with indoor lights. In
smart cities, a car would communicate with the parking space, a train can communicate with other
trains, a bus can communicate with a bus stop, the list can be extended. (Isikdag, 2015) in figure 2.1
has illustrated an IoT architecture as being consisted of several software layers.
OGC SensorThings API
OGC SensorThings API is an IoT open-standard Web service. A comprehensive model for IoT re-
sources that contains numerous classes and attributes, including tasking and sensing capabilities,
15

Figure 2.1: Components of an IoT architecture (Isikdag, 2015)
is defined by SensorThings API. Currently, the SensorThings API standard comprises two parts:
part 1, which is related to sensing capabilities (figure 2.2), and part 2, which is related to tasking
capabilities (figure 2.3). Each class in the SensorThings API standard is described in the following
text.
1. (T hing) The entities of the Thing class are objects in the information domain (virtual things)
or physical domain (physical things) that can be identified and integrated into communica-
tion networks;
2. (Location) The entities of this class record the last known location of the entities of the Thing
class;
3. (Histor icalLocation) The entities of this class record the time period or time points of
previous locations of the entities of the Thing class. For example, if a "thing" is mobile, several
entities of the Location class are linked to entities of the HistoricalLocation class;
4. (Datastream) This class comprises entities of the Observation class that measure the same
entity of the ObservedProperty class and are produced by the same entity of the Sensor class.
For instance, if an entity of the Thing class is capable of observing three properties, such as
illumination, relative humidity, and air temperature, then this entity may correspond to three
Datastream entities, each of which groups the Observation entities for one feature;
5. (Sensor) The entities of this class represent the instruments used to monitor a phenomenon
or property;
16

6. (Obser vedProper t y) The entities of this class represent the monitored properties, including
illumination, relative humidity, and air temperature;
7. (Obser vation) The entities of this class represent the determined or measured value of a
property represented by an entity of the Observed Property class that is measured by an entity
of the sta:Sensor class;
8. (FeatureO f Interest) This class comprises the features corresponding to the entities of the
Observation class.
Figure 2.2: Data model of OGC SensorThings API part 1 (Liang et al., 2016)
1. (TaskingCapabilit y) This class comprises the controllable capabilities supported by enti-
ties of the sta:Thing class;
2. (Task) This class contains user commands for controlling entities of the Tasking Capability
class. Device control should be performed on the basis of the input values contained in the
Task class;
3. (Actuator) This class contains metadata of the instrument used for obtaining the entities of
the TaskingCapability class.
SensorThings API hosts IoT resources in the RESTful Web service style and JavaScript Object
Notation (JSON) format. An example of a query result is displayed in figure 2.4. An entity of
the T hing class contains numerous attributes, including properties, a description, and a name.
17

Figure 2.3: Data model of OGC SensorThings API part 2 (Liang et al., 2016)
SensorThings API contains the aforementioned attributes as well as navigation links that connect
related entities.
Generally speaking, SensorThings API is a comprehensive solution for an IoT Web service. A
general and complete data model is defined in the aforementioned standard for IoT tasking and
sensing. Moreover, to enable users to query targeted IoT resources, SensorThings API uses flexible
query functions and the RESTful Web service style.
1.2 3D GIS-IoT Integration Strategies
Most relevant studies have integrated city models and IoT resources at the application level without
following any open standards. Only a few studies have adopted open standard based data sets.
However, the integration methods of these studies are usually customized according to the
targeted applications. These methods are insufficiently general for adoption in other applications.
Before designing a general solution for integrating city models and IoT resources, we analyzed and
categorized the integration strategies adopted in previous studies.
From a literature review, three types of integration strategies were identified 2.1: the 1) embed-
ding; 2) external referencing; and 3) external joining strategies.
Strategy Advantages Disadvantages
Embedding Atomic Large data size
External referencing Lightweight Data are not self-contained
External joining All resources are independent May be unable to find suitable linkages
Table 2.1: Comparison of different 3D GIS-IoT integration strategies
These strategies are described as follows.
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Figure 2.4: Example query of SensorThings API
1. In the embedding strategy, one resource is embedded directly into another resource; for
example, a time series of sensor observations are embedded into city model data, such as the
CityGML 3.0 Dynamizer. In this case, all the data are atomic. Nevertheless, the data size is
large and inconvenient for updating dynamic information;
2. In the external referencing strategy, the relative or absolute path in one resource is used to
retrieve another resource. The external referencing strategy is more lightweight than the
embedding strategy because every piece of information need not be embedded into one
resource in the external referencing strategy. Moreover, dynamic information can be obtained
through reference links in the external referencing strategy, such as the design in the Open
Geospatial Consortium (OGC) 3-D IoT platform for smart cities pilot. This strategy also allows
the referenced resources to be independent; thus, the referenced resources can be created and
maintained as a single data set because they do not depend on any other resource. However,
one minor drawback of the aforementioned strategy is that the data are not self-contained
and may require connections to retrieve all the pieces of information;
3. In the external joining strategy, external data are created to describe and record the rela-
tionships between two resources. This strategy allows all resources to be independent. The
mapping relationships of these resources are relatively easy to update. The aforementioned
strategy also enables the flexible processing of many-to-many mappings. However, one
drawback of the aforementioned strategy is that it may be unable to find suitable linkages
between resources because they are created independently.
2 Conclusion
The integration of 3D-GIS and the Internet of Things (IoT) is a promising approach to improving
the management and analysis of spatial data related to smart city applications. This research has
investigated the integration pattern of 3D-GIS and IoT to understand its benefits, challenges, and
potential solutions.
19

The research findings suggest that the integration of 3D-GIS and IoT can provide significant
benefits to smart city applications, including improved real-time monitoring and analysis of urban
environments, enhanced decision-making, and increased efficiency. The integration can also help
to reduce costs and improve resource allocation by allowing stakeholders to access and analyze
data in real-time. However, the integration of 3D-GIS and IoT also presents several challenges such
as data interoperability and data quality.
To overcome these challenges, various approaches have been proposed, including the use of
open standards such as the OGC SensorThings API and the development of common data models.
The adoption of these approaches can improve data interoperability and quality while ensuring
data security.
In conclusion, the integration of 3D-GIS and IoT presents significant benefits to smart city
applications, and the challenges associated with it can be addressed through the adoption of open
standards and the development of common data models. As the use of IoT continues to grow in
smart city applications, the integration of 3D-GIS and IoT will become increasingly important, and
further research is needed to explore its potential in different smart city settings.
20

Part II
Materials & Methods
21

Chapter 3 — Implementation & Results
1 Introduction
This study integrated the open standards of BuildingSMART IFC, OGC SensorThings API, and I3S
and utilized the advantages of these standards to construct a smart monitoring framework. This
section describes the tools used as well as the adopted integration strategy.
2 Case study (Tour Triangle Project)
2.1 Overview
As part of the Tour Triangle project in Paris. A LiDAR survey was performed to check the stability of
the tunnel underneath. The figure 3.1 shows a rendering of the Tour Triangle future installation in
Paris.
For monitoring underground railroad during the construction of the project, a new approach
based on 3D geospatial integration approach was proposed to better comprehend the SHM of the
underlying railway infrastructure.
The underground tunnel is part of the metro lines that flow into the metropolitan Paris and
is located near "Porte de Versailles". The metro line in question is the L12 and has four separate
rail-tracks that support the daily travel of the Parisian residents.
3 Hardware & Software
In order to put into practice the proposed approach and to reach our objectives, we have called
upon a series of software and hardware that we summarize as follows.
3.1 Hardware
RTC360 3D Laser Scanner
Leica RTC360 3D reality capture solution enables users to document and capture their environments
in 3D, improving efficiency and productivity in the field and in the office through fast, simple-to-use,
accurate, and portable hardware and software. The RTC360 3D laser scanner is the solution for
professionals to manage project complexities with accurate and reliable 3D representations and
discover the possibilities of any site.
22

Figure 3.1: Tour Triangle (tour-triangle.com)
Figure 3.2: RTC360 3D Laser Scanner (Leica Geosystems)
3.2 Software
Cyclone REGISTER 360
Leica Cyclone REGISTER 360 has many capabilities from simple, guided workflows to automated
registration and client-ready deliverables.
CloudCompare
CloudCompare is a 3D point cloud (and triangular mesh) processing software. It has been originally
designed to perform comparison between two dense 3D points clouds (such as the ones acquired
with a laser scanner) or between a point cloud and a triangular mesh. It relies on a specific octree
structure dedicated to this task.
Afterwards, it has been extended to a more generic point cloud processing software, including
many advanced algorithms (registration, resampling, color/normal/scalar fields handling, statistics
23

Figure 3.3: Cyclone REGISTER - Point cloud registration software (Leica Geosystems)
computation, sensor management, interactive or automatic segmentation, display enhancement,
etc.).
Figure 3.4: CloudCompare - Open Source 3D point cloud processing software (danielgm)
ReCap Pro
ReCap Pro software helps designers and engineers capture high quality, detailed models of real-
world assets. ReCap Pro can be used to:
• Understand and verify existing conditions and as-built assets to gain insights and make better
decisions;
• Deliver a point cloud or mesh in support of BIM (Building Information Modeling) processes
and collaborate across teams with real-world context;
• Survey, plan, construct, and renovate building and infrastructure projects.
Figure 3.5: ReCap Pro - Capture detailed models of real-world assets (Autodesk)
24

Revit
Revit BIM software helps architecture, engineering, and construction Architecture, Engineering and
Construction (AEC) teams create high-quality buildings and infrastructure. Revit can be used to:
• Model shapes, structures, and systems in 3D with parametric accuracy, precision, and ease;
• Streamline documentation work, with instant revisions to plans, elevations, schedules, and
sections as projects change;
• Empower multidisciplinary teams with specialty toolsets and a unified project environment.
Figure 3.6: Revit - BIM software for designers and builders (Autodesk)
FME Desktop
FME, also known as Feature Manipulation Engine, is a geospatial extract, transformation and load
software platform developed and maintained by Safe Software of British Columbia, Canada.
Figure 3.7: FME Desktop (Safe Software)
3.3 Tech Stack
PostgreSQL / TimescaleDB
TimescaleDB is an open-source time series database developed by Timescale Inc. It is written in C
and extends PostgreSQL. TimescaleDB supports standard SQL queries and is a relational database.
Additional SQL functions and table structures provide support for time series data oriented towards
storage, performance, and analysis facilities for data-at-scale. Time-based data partitioning pro-
vides for improved query execution and performance when used for time oriented applications.
More granular partition definition is achieved through the use of user defined attributes.
Figure 3.8: TimescaleDB for time-series and analytics (Timescale)
25

React
React (also known as React.js or ReactJS) is a free and open-source front-end JavaScript library for
building user interfaces based on components. It is maintained by Meta (formerly Facebook) and a
community of individual developers and companies.
React can be used as a base in the development of single-page, mobile, or server-rendered
applications with frameworks like Next.js. However, React is only concerned with the user interface
and rendering components to the Document Object Model (DOM), so creating React applications
usually requires the use of additional libraries for routing, as well as certain client-side functionality.
Figure 3.9: ReactJS for web and native user interfaces (React)
Docker
Docker is a set of platform as a service products that use OS-level virtualization to deliver software
in packages called containers. The service has both free and premium tiers. The software that hosts
the containers is called Docker Engine. It was first started in 2013 and is developed by Docker, Inc.
Figure 3.10: Docker for accelerated and containerized applications (Docker)
4 Scan to BIM (3D Point Cloud to IFC)
4.1 Field Laser Scanning
As part of monitoring the underground railway infrastructure during the construction of the project,
a 3D Laser Scan was done covering the metro line 12 close to the " Porte de Versailles " station.
4.2 Registration
We relied both on terrain registration using Leica Cyclone Field 360 solution installed on a tablet
and coupled with RTC360’s Visual Inertial System technology, and a precise registration under Leica
Cyclone Register 360, which was prepared for by using terrain marks between each two consecutive
setups.
We relied on Cyclone Register 360 to do some prior cleaning of the external environment of
the tunnel, we also carried out a down sampling process in which we minimized the point cloud
density because of the limiting processing power that we have.
26

Figure 3.11: Tunnel 3D Point Cloud extent in 2D view (Bing Maps)
The results were then exported to two open formats: .las and .e57. We used CloudCompare
to carry out the remaining processing steps. The figure 3.12 displays the E57 point cloud under
CloudCompare.
Figure 3.12: E57 Point Cloud after registration
4.3 Preprocessing
The point cloud contains more than 92 M RGB points coded in 16 bits. And taking into consideration
the technical capacity of the workstation, we focused on a section of the tunnel (figure 3.13) whose
geometric features are well apparent.
27

In order to fast forward the cleaning and the segmentation processes, we adopted a local User
Coordinate System (UCS) that is parallel to the y axis. We used a 4X4 rotational matrix that we
saved its factors in order to apply a reverse transformation once prepossessing operations finished.
This orthographic view allowed us to better identify the underground tunnel salient components
as well as better prepare the cleaning of our point cloud and its modeling.
Figure 3.13: Tunnel section that will be rendered for modeling
The result which will be subjected to cleaning processes is depicted in figure 3.14.
Figure 3.14: Annotated tunnel (height = 5m, width = 13m and length = 47m)
Point Cloud Cleaning
We adopted the visual cloud cleaning process, which is based on the registered point cloud. The
cleaning was done inside CloudCompare and consisted of removing accessory items such us
overhead lines, etc as shown in the following figure figure 3.15. Point cloud cleaning can be further
enhanced by segmenting the point cloud using RANSAC or DBSCAN clustering algorithms.
Figure 3.15: Manual point cloud cleaning
28

4.4 Parametric 3D BIM Modeling
With parametric modeling information is linked via algorithms in a digital parametric structured
model so that when a change is made, components are updated automatically in line with specified
parameters.
The parametric modeling was done in the following order:
1. Point cloud import into a BIM solution: First in Recap Pro then imported into Revit with
coordinates aligned with the origin of the project;
2. Parametric modeling of railways: This was achieved by making the railing distance as well as
the sleepers distance parametric;
3. Extrusion modeling of tunnel: This approach eased out considerably the conversion process
that came afterwards;
4. Topology and semantic description.
To import the point cloud to Revit, we need first to convert our E57 to rcp format. Also to better
grasp the rails and tunnel geometry, figure 3.16 depicts a vertical separation that was used to handle
measurements and ease out the modeling process.
Figure 3.16: Tunnel separation into railway and tunnel
Parametric modeling of railway track
We focused on modeling the sleepers and the two rails composing a single rail track. The adopted
strategy was to have a topological relation between the sleepers and the rails so that the sleepers
support the rails (figure 3.17).
Thus we used Revit nested families; this approach was tested in our BIM-3D GIS integration
pattern. We used simple primitives in order to avoid possible degradation in the translation process
from IFC to CityGML.
As for measurements, they were taken completely from ReCap (rcp) point cloud which was
imported into our Revit project.
These nested families can be integrated as follows (figure 3.18), each railway track follows
terrain topography and can change direction and altitude respectively. Also, we made each railway
track component as an instance parameter which can be modified to align itself with each country
judiciary railroad system.
29

Figure 3.17: Rail and sleeper design
Nesting in Revit allowed us to place families within other families in order to display their
combined geometries and make them behave like a single unit within a project.
Figure 3.18: Railway track model integrated into its corresponding point cloud
Extrusion modeling of underground tunnel
The tunnel modeling followed an extrusion principal based on longitudinal section. The chosen
profile (figure 3.19) was further simplified to mimic real algorithmic profiling of such underground
infrastructures.
Figure 3.19: Longitudinal section of tunnel
We also needed to define the tunnel theoretical axis that will support the tunnel extrusion (fig-
ure 3.20). We also applied the reverse rotation matrix to switch from the UCS to terrain coordinates.
An origin related to site coordinates was also marked on the model.
30

Figure 3.20: Tunnel theoretical axis
Scan to BIM Model
The result of the Scan to BIM process is a two part BIM (figure 3.21) model each pertaining semantic
and topological characteristics corresponding to tunnel and railroad tracks.
Figure 3.21: 3D BIM model of tunnel
4.5 Geo-referencing with shared coordinates
We used the metro lines dataset from data.gouv.fr to position and orient our model. Both bear-
ing and 3D coordinates were integrated into our BIM model by using Revit shared coordinates
functionality.
4.6 OpenBIM Export
To prepare an openBIM export, we used IfcRail and IfcTunnel documentation to enhance Revit IFC4
export with related infrastructure type parameters. This process was achieved by using custom
IFC Property Sets. To test our IFC export, we used BIMvision to check compliance with IFC
specification.
31

Figure 3.22: Bearing of metro line 12 in RGF93 CC49
Figure 3.23: IFC model of tunnel (BIMvision)
5 BIM to 3D GIS (IFC to CityGML)
With FME, we converted and transformed our IFC model to suit our research study. By reading data
from common BIM formats like IFC we succeeded at extracting geometric and semantic details
incumbent to our case study.
5.1 Reading source IFC
We used an Industry Foundation Class STEP Files (IFC) reader to open our OpenBIM model. We
will want to read in the IfcRail and IfcTunnel geometry types separately for ETL transformation.
5.2 Mesh & attribute setting
The IFC features need to be merged into a single feature that represents the underground tunnel.
To do so, the Triangulator transformer ensures such process. This transformer breaks the input
geometry into a mesh for each of the flattened components.
Also, CityGML has specific standards for attribute naming in order for the file to be readable.
32

Hence we ensured such alignment by using FME CityGMLGeometrySetter. In (figure 3.24), we can
see part of the ETL model that we worked on.
Figure 3.24: FME Transformation Model
6 Extended SensorThings API
FROST-Server is a standard-based server implementation for the OGC SensorThings API, which
enabled us to manage and query IoT sensor data. The benefits of using FROST-Server in our study
include:
1. Easy implementation, FROST-Server provides an easy-to-implement interface for managing
and querying IoT sensor data, which can reduce development time and costs;
2. Standard-based, FROST-Server is based on open standards such as OGC SensorThings API
(figure 3.25), which ensures compatibility and interoperability with other systems that also
support these standards;
3. Scalability, FROST-Server is designed to be highly scalable, enabling it to handle large
amounts of sensor data and users;
4. Flexibility, FROST-Server allows for flexibility in data storage and retrieval, supporting various
databases and file systems;
5. Security, FROST-Server provides security features such as authentication and authorization,
ensuring that only authorized users can access and modify sensor data;
6. Real-time data processing, FROST-Server can handle real-time sensor data streams and
provide near-real-time processing capabilities, enabling real-time decision-making.
Overall, FROST-Server provides a reliable and efficient way to manage and query IoT sensor
data, enabling organizations to make informed decisions based on the insights derived from their
sensor data.
6.1 Deployment architecture
The all-in-one implementation contains both the Hypertext Transfer Protocol (HTTP) (Sensing)
and the MQ Telemetry Transport (MQTT) (tasking) parts of the FROST-Server. Because everything
33

Figure 3.25: Standard based FROST-Server (FROST-Server)
runs in the same Java Virtual Machine (JVM), the HTTP and MQTT parts can directly communicate,
and there is only minimal delay between entities being updated, and MQTT messages being sent
out.
To make it possible to have multiple HTTP and MQTT instances, a message bus is introduced in
the architecture and the HTTP and MQTT parts of the server are used separately (figure 3.26).
Since there is the possibility to run FROST-Server and the needed database inside one or
multiple Docker containers. We decided to use a non-dockerised database with a dockerised
FROST-Server (Check B appendix).
Figure 3.26: FROST-Server Deployment Architecture
6.2 Database performance
Indexing timestamps, geometry and JSON fields
Indices play a crucial role in the performance of a database by providing a fast and efficient way to
access data.
By default, only primary and foreign keys have indices on them. A very common index is for
34

Datastreams(x)/observations?$orderby=phenomenonTime asc using B-Tree index.
Figure 3.27: Indexing timestamps using B-Tree index
We also added indices to geometry columns using the PostGIS GiST index.
Figure 3.28: Indexing locations using GiST index
Indices can be added to fields within JSONB columns to speed up queries;
Figure 3.29: Indexing JSONB fields
TimescaleDB (open-source time series database)
Time series data is an essential component of real-time monitoring applications, and managing
this data efficiently can be challenging. TimescaleDB is a relational database management system
that is designed to handle time-series data efficiently. It provides several features that make it an
ideal choice for real-time monitoring applications, including the following:
• Fast ingestion and retrieval of time-series data, TimescaleDB uses a time-series optimized
data storage format that allows for faster ingestion and retrieval of time-series data. It also
supports parallelized queries, which can significantly improve query performance;
• Advanced indexing capabilities, TimescaleDB provides several indexing options that are
optimized for time-series data, including the ability to create multi-dimensional indexes,
which can improve query performance;
• Data retention policies, TimescaleDB allows for the automatic deletion of old data based on
predefined retention policies. This feature can help to manage the database’s size and reduce
the storage requirements for time-series data.
In real-time monitoring applications, TimescaleDB can efficiently store and manage large
volumes of time-series data, allowing for faster data retrieval, analysis, and visualization. Its
advanced indexing capabilities and data retention policies also help to improve the efficiency of
the database, while its scalability ensures that it can handle increased data volumes and traffic over
time.
35

Figure 3.30: Time bucketing time series data
6.3 Authentication & Authorisation
FROST-Server ensures that the user has the following roles:
• READ, can the user read, both on HTTP and MQTT;
• CREATE, can the user create new entities, both on HTTP and MQTT;
• UPDATE, can the user update entities. This is only possible over HTTP.
6.4 Creating spatial entities
The OGC SensorThings API does not just allow you to read data, it is possible to create, update and
delete all data too. For our solution, we will create the data model for the underground tunnel that
we will be monitoring, see appendix C for further detail.
Once a new entity is established, we can proceed to query our database.
6.5 External Joining with BIM and 3D GIS
API management for IoT
To prepare our SensorThings API for integration with other components, we needed to properly
manage our existing Application Programming Interface (API) with our newly extended IoT pool.
This involved controlling access to the API, ensuring its security, and monitoring its performance
and availability. For this reason, we chained our logic as following:
• Designing the API, We started by designing the API. This involved defining the endpoints,
methods, and data formats that will be used by our IoT application to communicate with
devices;
• Securing the API, Security is critical for our IoT applications. We implemented security mea-
sures such as authentication, authorization, and encryption to protect against unauthorized
access and data breaches;
• Publishing the API, Once we completed the API design and secured it properly, we published
it by exposing Docker container ports so that it can be accessed by devices and applications
in the IoT ecosystem.
Overall, we used FROST-Server as our underlying API to manage internal and external endpoints
and a PostgreSQL / PostGIS / TimescaleDB database as a central hub for the different IoT devices.
36

Figure 3.31: SensorThings API database diagram
SensorThings into BIM
To interact with a railway infrastructure model over the web, we used Autodesk Platform Ser-
vices API (Formally Forge) which includes include support for multiple programming languages,
comprehensive documentation, and developer support.
We relied upon APS DataViz Extensions which we contributed to during this study to add
functionality that is related to our study, a contribution that consisted of developing a data wrapper
for incoming SensorThings measurements.
SensorThings into 3D GIS
To test the hypothesis of an IoT-3D GIS integration pattern between SensorThings and CityGML
model. We started by scripting a Dynamo BIM node capable of handling GET requests (check
appendix A for more detail). This script update the SHM of railroad tracks based on SensorThings
real time measurements (figure 3.34).
Once the SHM of railroad structure has been updated, FME Server can be used to automate
data and application integration workflows in a no-code environment on a schedule or in response
to events. For our case study, we tested a manual data integration between IFC and CityGML which
37

Figure 3.32: SensorThings API Docker server
Figure 3.33: APS DataViz Extensions (Data Visualization Extension)
yielded remarkable results in term of geometry conversion and attributes persistence.
Once a data integration workflow has been completed, and in order to have the results accessible
over the web. We used the I3S OGC standard which is encoded using JSON and binary array buffers.
7 IFC-SensorThings-I3S Integration Platform
The integration platform consists of APS for interacting with the IFC model as well as a Data
Visualization Extension for displaying SensorThings API measurements. Raw sensor data which
related to each SensorThings Data Stream is linked to each railing sensor with time framing of
the measurements. Users are able to comprehend the interactions of the tunnel with its close
environments by relying on I3S to build a 3D geospatially enabled environment through ArcGIS
Online web engine.
38

Figure 3.34: Updating in real time the SHM of railroad tracks
Figure 3.35: Automating data integration workflows with FME Server
Figure 3.36: Scene Layers for 3D GIS models (Scene Layers)
Figure 3.37: Prism measurements displayed with IFC model
39

Figure 3.38: React plotly.js
Figure 3.39: Underground tunnel with its surroundings
Figure 3.40: An integrated BIM-IoT-3D GIS platform
40

General Conclusion
In conclusion, the integration of BIM, 3D GIS, and IoT technologies can greatly enhance the effi-
ciency and effectiveness of railway monitoring and operation. By integrating these technologies,
it is possible to create a comprehensive and dynamic digital model of an infrastructure, which can
be used to improve the decision-making process throughout its entire life cycle.
The BIM technology provides a detailed and comprehensive digital representation of an infras-
tructure’s physical and functional characteristics. Our study showed that developing a centralized
web hub for fully inter-operable IFC-CityGML models, relying completely on open interface stan-
dards, utilizing locally unified ETL processes as well as remotely 3D Web sharing technologies
for complete end-to-end seamless monitoring, has benefited the construction life cycle of these
infrastructures with possibility of inspecting and updating its underlying mechanical structure in
concordance with an efficient integration of IoT monitoring sensors capable of maximum situa-
tional awareness.
As for the 3D GIS technology, it has pushed a global landscape for such worldly projects, since
monitoring operations not only mark the project under construction, but in a huge extent the
surroundings that should be monitored too in case of emergencies firing up. Such integration
of spatially aware sensors using SensorThings API, pushed a global scene where intelligence
mark a propellant inter-disciplinary scenery, where multiple collaborators can read and conquer
conclusions specific to their field of intervention in a single inter-operable web platform.
Although combining such technologies in a coherent, unified, and geospatially enabled envi-
ronment still in its early days of development, especially for use cases such as railway infrastructure.
Our approach not only allowed for such concordance but has pushed development through an
honest use of open-source approach which reduces development cost and can adhere to use cases
that are only specific to AEC collaborators.
41

1 import pandas as pd
2 import numpy as np
3 import requests
4
5 # Create a dataframe
6 df = pd.DataFrame(columns =["result"])
7
8 # Add random values to the dataframe
9 num_rows = 100
10 for i in range (0, num_rows):
11 data = pd.DataFrame ({"result": [np.random.randint (1, 6, 1)[0] / 1000]})
12 df = pd.concat ([df , data], ignore_index=True)
13
14 # Post the dataframe to the database
15 url = "http :// localhost :8080/ FROST -Server/v1.1/ Datastreams (1)/Observations"
16 headers = {"Content -Type": "application/json"}
17 for i in range (0, len(df)):
18 data = df.iloc[i]. to_json ()
19 r = requests.post(url , headers=headers , json=df.to_dict("records")[i])
Listing 4.1: Importing synthetic data to FROST-Server
42

Dynamo Node to Access Data from
SensorThings API Endpoint
Dynamo is a powerful tool that allowed us in the context of this research to create custom scripts to
perform tasks such as creating geometry, managing data, and automating workflows. Dynamo was
used to automate data views updates between SensorThings and our BIM model inside the Revit
environment. It provided us a flexible and customizable way to work with BIM data, allowing us to
adapt and optimize our workflows to the application needs.
Figure A.1: Dynamo script to update railway structural health
44

FROST-Server docker-compose file
Figure B.1: FROST-Server docker-compose file
45

Tunnel Entity
Figure C.1: Tunnel Entity
46

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[9] Vacca, G. and Quaquero, E. (2020). Bim-3d gis: An integrated system for the knowledge process
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47

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48

ROYAUME DU MAROC
ET VÉTÉRINAIRE HASSAN II
‫المملكة‬
‫المغربية‬
‫معهد‬
‫الحسن‬
‫الثاني‬
‫اململكة‬
‫املغربية‬
ROYAUME DU MAROC
ET VETERINAIRE HASSAN II
‫معهد‬
‫الحسن‬
‫الثاني‬
Adresse : Madinat Al Irfane, B.P. 6202. Rabat – Maroc
Tél : (00 212) 0537 77 17 58/59
Fax : (00 212) 0537 77 58 45
Site web : http://www.iav.ac.ma
‫ب‬ .‫ص‬ :‫العنوان‬
6202
‫الرباط‬ ‫المعاهد‬ ‫الرباط‬
–
‫المغرب‬
:‫الهاتف‬
59
/
58
17
77
0537
(00 212)
:‫الفاكس‬
45
58
77
0537
(00 212)
:‫األنتيرنت‬ ‫موقع‬
http://www.iav.ac.ma

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2023 øAÓ

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