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Advances in Informatics and Computing in Civil and Construction Engineering
Ivan Mutis · Timo Hartmann Editors
Proceedings of the 35th CIB W78 2018 Conference: IT in Design, Construction, and Management
Ivan Mutis • Timo HartmannEditors
Advances in Informaticsand Computing in Civiland Construction EngineeringProceedings of the 35th CIB W78 2018Conference: IT in Design, Construction,and Management
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EditorsIvan MutisCAEEIllinois Institute of TechnologyChicago, IL, USA
Timo HartmannInstitute of Civil EngineeringTU BerlinBerlin, Germany
ISBN 978-3-030-00219-0 ISBN 978-3-030-00220-6 (eBook)https://doi.org/10.1007/978-3-030-00220-6
Library of Congress Control Number: 2018953319
© Springer Nature Switzerland AG 2019This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material isconcerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproductionon microfilms or in any other physical way, and transmission or information storage and retrieval, electronicadaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does notimply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws andregulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in this book are believedto be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty,express or implied, with respect to the material contained herein or for any errors or omissions that may have beenmade. The publisher remains neutral with regard to jurisdictional claims in published maps and institutionalaffiliations.
This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Letter from the Editors
The 35th CIB W78 conference took place in Chicago in 2018, with a theme focused onfostering, encouraging, and promoting research and development in the application ofintegrated information technology (IT) throughout the life cycle of the design, construction,and occupancy of buildings and related facilities. Organized by Professors David Arditi andIvan Mutis (Illinois Institute of Technology, Chicago), Timo Hartmann (TechnischeUniversität Berlin), Robert Amor (University of Auckland), and with special and valuablesupport from Bill East (Prairie Sky Consulting, USA), it brought together more than 200scholars from 40 countries, who presented the innovative and unique concepts and methodsfeatured in this collection of papers.
With the publication of these contributions, we expect to scaffold scholars’ motivations toinspire and discover the pressing research questions that need to be answered in the comingdecade. Framed under topic clusters as described in the introductory section, the Editorsorganized the responses of the 2018 worldwide, open call for submissions. Taking the numberof submissions in each focus area as an indicator of research potential, the open call elicitedthe lowest response in the area of Systems of Integrated Computer and Physical Components(Cyber-Physical-Systems), which suggests underdevelopment of initiatives for scientificquestions in this area. We look forward to seeing greater response to this area in the future.
Ultimately, the success of this event and its contribution to the field of informatics andcomputing in civil and construction engineering is the result of countless hours ofinvestigation, development, and work from scholars across the globe. The Editors andorganizing committee thank all who have supported the effort. We thank in particular the paperreviewers.
The research and approaches that have been developed and presented at this conference canimmediately deliver extraordinary innovations to construction practices with benefitsattributable to individuals, organizations, and the industry, as a whole. Looking forward,the legacy of this conference will be carried not only through its influence on the constructionpractice but also on research for years to come.
Ivan MutisTimo Hartmann
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About CIB and CIB W78
CIB, officially named International Council for Research and Innovation in BuildingConstruction, was established in 1953 under the name Conseil International du Bâtiment. Thefoundational objectives of CIB were to stimulate and facilitate the international cooperationand exchange of information between governmental research institutes in the building andconstruction sector, with an emphasis on those engaged in technical fields of research. Sinceits inception, the association has developed into a worldwide network that connects more than5000 experts. These specialists represent the research institutes, university, and industry- andgovernment-related entities that constitute the approximate 500-member organizations of CIB.Though the size and strength of the organization today has grown compared to the past, thefocus of CIB and its members remains the same: the active collection of research andinnovation information for all aspects of building and construction.
CIB W78, or work group 78, is one of the largest and most active working commissions ofCIB. The scope of W78’s work is broad, but its primary mission is to proactively encouragethe integration of Information and Communication Technologies (ICT) into a facility’s lifecycle. It achieves this goal by disseminating research and knowledge among an internationalcommunity of scholars and practitioners in a variety of means, most notably the annualinternational conference.
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Contents
Part I Information Integration and Informatics
1 Barriers of Automated BIM Use: Examining Factorsof Project Delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Jason Lucas and Sai Sri Neeharika Vijayarao
2 Simulation of Construction Processes as a Link Between BIM Modelsand Construction Progression On-site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Ector Oliveira, Cláudio Ferreira Júnior, and Fabiano Correa
3 In Search of Sustainable Design Patterns: Combining Data Miningand Semantic Data Modelling on Disparate Building Data . . . . . . . . . . . . . 19Ekaterina Petrova, Pieter Pauwels, Kjeld Svidt, and Rasmus Lund Jensen
4 The Role of Knowledge-Based Information on BIMfor Built Heritage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27C. K. Cogima, P. V. V. Paiva, E. Dezen-Kempter, M. A. G. Carvalho,and Lucio Soibelman
5 Heritage Building Information Modelling (HBIM): A Reviewof Published Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Ian J. Ewart and Valentina Zuecco
6 Next Generation of Transportation Infrastructure Management:Fusion of Intelligent Transportation Systems (ITS) and BridgeInformation Modeling (BrIM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Alireza Adibfar and Aaron Costin
7 Blockchain in the Construction Sector: A Socio-technical SystemsFramework for the Construction Industry. . . . . . . . . . . . . . . . . . . . . . . . . . . 51Jennifer Li, David Greenwood, and Mohamad Kassem
8 Formalized Knowledge Representation to Support Integrated Planningof Highway Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Jojo France-Mensah and William J. O’Brien
9 An Automated Layer Classification Method for Converting CADDrawings to 3D BIM Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Mengtian Yin, Zihao Ye, Llewellyn Tang, and Shuhong Li
10 Defining Levels of Development for 4D Simulation of Major CapitalConstruction Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Michel Guevremont and Amin Hammad
11 Modularized BIM Data Validation Framework Integrating VisualProgramming Language with LegalRuleML . . . . . . . . . . . . . . . . . . . . . . . . . 85Pedram Ghannad, Yong-Cheol Lee, Johannes Dimyadi, and Wawan Solihin
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12 Coupling Between a Building Spatial Design Optimisation Toolboxand BouwConnect BIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95Sjonnie Boonstra, Koen van der Blom, Hèrm Hofmeyer,Joost van den Buijs, and Michael T. M. Emmerich
13 Reusability and Its Limitations of the Modules of Existing BIM DataExchange Requirements for New MVDs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Yong-Cheol Lee, Pedram Ghannad, and Jin-Kook Lee
14 Employment of Semantic Web Technologies for CapturingComprehensive Parametric Building Models . . . . . . . . . . . . . . . . . . . . . . . . . 111Farhad Sadeghineko, Bimal Kumar, and Warren Chan
15 BIM Coordination Oriented to Facility Management . . . . . . . . . . . . . . . . . . 123Mónica Viviana Sierra-Aparicio, JoséLuis Ponz-Tienda,and Juan Pablo Romero-Cortés
16 OpenBIM Based IVE Ontology: An Ontological Approach to ImproveInteroperability for Virtual Reality Applications. . . . . . . . . . . . . . . . . . . . . . 129Anne-Solene Dris, Francois Lehericey, Valerie Gouranton,and Bruno Arnaldi
17 BIM and Through-Life Information Management: A SystemsEngineering Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137Yu Chen and Julie Jupp
18 A Lean Design Management Process Based on Planning the Levelof Detail in BIM-Based Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147Petteri Uusitalo, Olli Seppänen, Antti Peltokorpi, and Hylton Olivieri
Part II Cyber-Human-Systems
19 The BIMbot: A Cognitive Assistant in the BIM Room . . . . . . . . . . . . . . . . . 155Ivan Mutis, Adithya Ramachandran, and Marc Gil Martinez
20 Perceived Productivity Effects of Mobile ICTin Construction Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165Abid Hasan, Kumar Neeraj Jha, Raufdeen Rameezdeen, SeungJun Ahn,and Bassam Baroudi
21 Mobile EEG-Based Workers’ Stress Recognition by Applying DeepNeural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Houtan Jebelli, Mohammad Mahdi Khalili, and SangHyun Lee
22 Feasibility of Wearable Electromyography (EMG) to AssessConstruction Workers’ Muscle Fatigue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181Houtan Jebelli and SangHyun Lee
23 Tacit Knowledge: How Can We Capture It?. . . . . . . . . . . . . . . . . . . . . . . . . 189Jacqueline Jepson, Konstantinos Kirytopoulos, and Nicholas Chileshe
24 Inside the Collective Mind: Features Extraction to Support AutomatedDesign Space Explorations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199Lucian-Constantin Ungureanu and Timo Hartmann
25 Detecting Falls-from-Height with Wearable Sensors and ReducingConsequences of Occupational Fall Accidents Leveraging IoT . . . . . . . . . . 207Onur Dogan and Asli Akcamete
26 Using Augmented Reality to Facilitate Construction Site Activities . . . . . . . 215Serkan Kivrak and Gokhan Arslan
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27 Semantic Frame-Based Information Extraction from Utility RegulatoryDocuments to Support Compliance Checking . . . . . . . . . . . . . . . . . . . . . . . . 223Xin Xu and Hubo Cai
28 Ontology-Based Semantic Retrieval Method of Energy ConsumptionManagement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Ya-Qi Xiao, Zhen-Zhong Hu, and Jia-Rui Lin
29 Visualisation of Risk Information in BIM to Support Risk Mitigationand Communication: Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239Yang Zou, Lari Tuominen, Olli Seppänen, and Brian H. W. Guo
30 Team Interactions in Digitally-Mediated Design Meetings . . . . . . . . . . . . . . 247Jacob Ofori-Darko, Dragana Nikolic, and Chris Harty
31 User Perceptions of and Needs for Smart Home Technologyin South Africa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255Kelvin Bradfield and Chris Allen
32 Seamless Integration of Multi-touch Table and Immersive VRfor Collaborative Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263Mattias Roupé, Mikael Johansson, Laura Maftei, Rikard Lundstedt,and Mikael Viklund-Tallgren
33 Development and Usability Testing of a Panoramic Augmented RealityEnvironment for Fall Hazard Safety Training . . . . . . . . . . . . . . . . . . . . . . . 271R. Eiris Pereira, H. F. Moore, M. Gheisari, and B. Esmaeili
34 The Negative Effects of Mobile ICT on Productivity in IndianConstruction Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281Abid Hasan, Kumar Neeraj Jha, Raufdeen Rameezdeen, SeungJun Ahn,and Bassam Baroudi
35 Augmented Reality Combined with Location-Based ManagementSystem to Improve the Construction Process, Quality Controland Information Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289Julia Ratajczak, Alice Schweigkofler, Michael Riedl, and Dominik T. Matt
36 Workflow in Virtual Reality Tool Development for AEC Industry . . . . . . . 297Lucky Agung Pratama and Carrie Sturts Dossick
37 Implementation of Augmented Reality Throughout the Lifecycleof Construction Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Fopefoluwa Bademosi and Raja R. A. Issa
38 Challenges Around Integrating Collaborative Immersive Technologiesinto a Large Infrastructure Engineering Project . . . . . . . . . . . . . . . . . . . . . . 315Laura Maftei, Dragana Nikolic, and Jennifer Whyte
Part III Computer Support in Design and Construction
39 Cybersecurity Management Framework for a Cloud-Based BIMModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325Ivan Mutis and Anitha Paramashivam
40 A System for Early Detection of Maintainability Issues Using BIM . . . . . . 335Bahadir Veli Barbarosoglu and David Arditi
41 Towards Automated Analysis of Ambiguity in Modular ConstructionContract Documents (A Qualitative & Quantitative Study) . . . . . . . . . . . . . 343Ali Azghandi Roshnavand, Mazdak Nik-Bakht, and Sang H. Han
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42 Adopting Parametric Construction Analysis in IntegratedDesign Teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351Alireza Borhani, Carrie Sturts Dossick, Christopher Meek, Devin Kleiner,and John Haymaker
43 Integrating BIM, Optimization and a Multi-criteria Decision-MakingMethod in Building Design Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359Elaheh Jalilzadehazhari and Peter Johansson
44 A BIM-Based Decision Support System for Building Maintenance . . . . . . . 371Fulvio Re Cecconi, Nicola Moretti, Sebastiano Maltese,and Lavinia Chiara Tagliabue
45 Structural Behavior Analysis and Optimization, Integrating MATLABwith Autodesk Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379Giulia Cerè, Wanqing Zhao, and Yacine Rezgui
46 An Assessment of BIM-CAREM Against the Selected BIM CapabilityAssessment Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387Gokcen Yilmaz, Asli Akcamete, and Onur Demirors
47 Towards a BIM-Agile Method in Architectural Design Assessmentof a Pedagogical Experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397Henri-Jean Gless, Gilles Halin, and Damien Hanser
48 A Generalized Adaptive Framework for Automating Design ReviewProcess: Technical Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405Nawari O. Nawari
49 An Integrated Simulation-Based Methodology for Considering WeatherEffects on Formwork Removal Times . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415Robert Larsson
50 Exploring Future Stakeholder Feedback on Performance-Based DesignAcross the Virtuality Continuum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423Sooji Ha, Neda Mohammadi, L. Sena Soysal, John E. Taylor,Abigail Francisco, Sean Flanagan, and Semra Çomu Yapıcı
51 A BIM Based Simulation Framework for Fire Evacuation Planning. . . . . . 431Qi Sun and Yelda Turkan
52 Where Do We Look? An Eye-Tracking Study of ArchitecturalFeatures in Building Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439Zhengbo Zou and Semiha Ergan
53 Developing a Framework of a Multi-objective and Multi-criteria BasedApproach for Integration of LCA-LCC and Dynamic Analysisin Industrialized Multi-storey Timber Construction . . . . . . . . . . . . . . . . . . . 447Hamid Movaffaghi and Ibrahim Yitmen
54 Collective Decision-Making with 4D BIM: Collaboration GroupPersona Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455Veronika Bolshakova, Annie Guerriero, Hugo Carvalho, and Gilles Halin
55 Post-occupancy Evaluation Parameters in Multi-objectiveOptimization–Based Design Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463Elie Daher, Sylvain Kubicki, and Annie Guerriero
56 Social Paradigms in Contemporary Airport Design . . . . . . . . . . . . . . . . . . . 471Filippo Bosi, Maria Antonietta Esposito, and Arto Kiviniemi
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57 A Method for Facilitating 4D Modeling by Automating TaskInformation Generation and Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 479Murat Altun and Asli Akcamete
Part IV Intelligent Autonomous Systems
58 An Autonomous Thermal Scanning System with Which to Obtain 3DThermal Models of Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489Antonio Adán, Samuel A. Prieto, Blanca Quintana, Tomás Prado,and Juan García
59 Productivity Improvement in the Construction Industry: A Case Studyof Mechanization in Singapore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497Chea Zhiqiang, Gurumurthy Balasubramaniam, and Ruwini Edirisinghe
60 Automated Building Information Models Reconstruction Using 2DMechanical Drawings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505Chi Yon Cho, Xuesong Liu, and Burcu Akinci
61 Architectural Symmetry Detection from 3D Urban Point Clouds:A Derivative-Free Optimization (DFO) Approach. . . . . . . . . . . . . . . . . . . . . 513Fan Xue, Ke Chen, and Weisheng Lu
62 Sequential Pattern Analyses of Damages on Bridge Elementsfor Preventive Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 521Kowoon Chang, Soram Lim, Seokho Chi, and Bon-Gang Hwang
63 Sound Event Recognition-Based Classification Model for AutomatedEmergency Detection in Indoor Environment . . . . . . . . . . . . . . . . . . . . . . . . 529Kyungjun Min, Minhyuk Jung, Jinwoo Kim, and Seokho Chi
64 Improved Window Detection in Facade Images . . . . . . . . . . . . . . . . . . . . . . 537Marcel Neuhausen and Markus König
65 Path Planning of LiDAR-Equipped UAV for Bridge InspectionConsidering Potential Locations of Defects . . . . . . . . . . . . . . . . . . . . . . . . . . 545Neshat Bolourian and Amin Hammad
66 Automatic Annotation of Web Images for Domain-SpecificCrack Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553Peter Cheng-Yang Liu and Nora El-Gohary
67 A Machine Learning Approach for Compliance Checking-SpecificSemantic Role Labeling of Building Code Sentences . . . . . . . . . . . . . . . . . . 561Ruichuan Zhang and Nora El-Gohary
68 Requirement Text Detection from Contract Packages to SupportProject Definition Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569Tuyen Le, Chau Le, H. David Jeong, Stephen B. Gilbert,and Evgeny Chukharev-Hudilainen
69 In Search of Open and Practical Language-Driven BIM-BasedAutomated Rule Checking Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577Wawan Solihin, Johannes Dimyadi, and Yong-Cheol Lee
70 Image-Based Localization for Facilitating Construction Field Reportingon Mobile Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585Youyi Feng and Mani Golparvar-Fard
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71 Towards an Automated Asphalt Paving ConstructionInspection Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593Clyde Newcomer, Joshua Withrow, Roy E. Sturgill Jr., and Gabriel B. Dadi
72 Computer Vision and Deep Learning for Real-Time PavementDistress Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601Kristina Doycheva, Christian Koch, and Markus König
73 A Flight Simulator for Unmanned Aerial Vehicle Flights OverConstruction Job Sites. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 609Hashem Izadi Moud, Mohamad A. Razkenari, Ian Flood, and Charles Kibert
74 Bridge Inspection Using Bridge Information Modeling (BrIM)and Unmanned Aerial System (UAS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617Yiye Xu and Yelda Turkan
Part V Cyber-Physical-Systems
75 Comparison Between Current Methods of Indoor Network Analysisfor Emergency Response Through BIM/CAD-GIS Integration . . . . . . . . . . 627Akram Mahdaviparsa and Tamera McCuen
76 Instrumentation and Data Collection Methodology to EnhanceProductivity in Construction Sites Using Embedded Systemsand IoT Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637Alejandra M. Carmona, Ana I. Chaparro, Ricardo Velásquez,Juan Botero-Valencia, Luis Castano-Londono, David Marquez-Viloria,and Ana M. Mesa
77 A Cyber-Physical Middleware Platform for Buildingsin Smart Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645Balaji Kalluri, Clayton Miller, Bharath Seshadri, and Arno Schlueter
78 A Framework for CPS-Based Real-Time Mobile Crane Operations . . . . . . 653Congwen Kan, Chimay J. Anumba, and John I. Messner
79 Drive Towards Real-Time Reasoning of Building Performance:Development of a Live, Cloud-Based System. . . . . . . . . . . . . . . . . . . . . . . . . 661Ruwini Edirisinghe and Jin Woo
80 Bayesian Network Modeling of Airport Runway Incursion OccurringProcesses for Predictive Accident Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 669Zhe Sun, Cheng Zhang, Pingbo Tang, Yuhao Wang, and Yongming Liu
81 A Low-Cost System for Monitoring Tower Crane ProductivityCycles Combining Inertial Measurement Units, Load Cellsand Lora Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677Alejandra M. Carmona, Ana I. Chaparro, Susana Pardo, Ricardo Velásquez,Juan Botero-Valencia, Luis Castano-Londono, David Marquez-Viloria,Cristóbal Botero, and Ana M. Mesa
82 The Interface Layer of a BIM-IoT Prototype for Energy ConsumptionMonitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685Fernanda Almeida Machado, Cassio Gião Dezotti, and Regina Coeli Ruschel
83 Predicting Energy Consumption of Office Buildings: A Hybrid MachineLearning-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695Kadir Amasyali and Nora El-Gohary
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Part VI Computing and Innovations for Design Sustainable Buildingsand Infrastructure
84 Thermal Performance Assessment of Curtain Walls of FullyOperational Buildings Using Infrared Thermography and UnmannedAerial Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703Ivan Mutis and Albert Ficapal Romero
85 BIM and Lean-Business Process Reengineering for EnergyManagement Optimization of Existing Building Stock . . . . . . . . . . . . . . . . . 711Athanasios Chassiakos, Stylianos Karatzas, and Panagiotis Farmakis
86 Geographic Information Systems (GIS) Based Visual AnalyticsFramework for Highway Project Performance Evaluation. . . . . . . . . . . . . . 719Chau Le, Tuyen Le, and H. David Jeong
87 Usage of Interface Management in Adaptive Reuse of Buildings . . . . . . . . . 725Ekin Eray, Benjamin Sanchez, Seokyoung Kang, and Carl Haas
88 Semantic Enrichment of As-is BIMs for Building Energy Simulation . . . . . 733Huaquan Ying, Hui Zhou, Qiuchen Lu, Sanghoon Lee, and Ying Hong
89 Proof of Concept for a BIM-Based Material Passport . . . . . . . . . . . . . . . . . 741Iva Kovacic, Meliha Honic, and Helmut Rechberger
90 Learning from Class-Imbalanced Bridge and Weather Datafor Supporting Bridge Deterioration Prediction . . . . . . . . . . . . . . . . . . . . . . 749Kaijian Liu and Nora El-Gohary
91 Machine-Learning-Based Model for Supporting Energy PerformanceBenchmarking for Office Buildings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757Lufan Wang and Nora M. El-Gohary
92 Occupants Behavior-Based Design Study Using BIM-GIS Integration:An Alternative Design Approach for Architects . . . . . . . . . . . . . . . . . . . . . . 765Mahdi Afkhamiaghda, Akram Mahdaviparsa, Kereshmeh Afsari,and Tamera McCuen
93 Standardization of Whole Life Cost Estimation for Early DesignDecision-Making Utilizing BIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773Mariangela Zanni, Tim Sharpe, Philipp Lammers, Leo Arnold,and James Pickard
94 Data Model Centered Road Maintenance Support System UsingMobile Device. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 781Satoshi Kubota
95 Ontology-Based Semantic Modeling of Disaster Resilient ConstructionOperations: Towards a Knowledge-Based Decision Support System . . . . . . 789Sunil Dhakal and Lu Zhang
96 A Methodology for Real-Time 3D Visualization of Asphalt ThermalBehaviour During Road Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 797D. S. Makarov, F. Vahdatikhaki, S. R. Miller, and A. G. Dorée
97 Eliminating Building and Construction Waste with Computer-AidedManufacturing and Prefabrication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805Gerard Finch and Guy Marriage
Contents xv
98 A Methodological Proposal for Risk Analysis in the Constructionof Tunnels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815Luis Guillermo Garzón Ospina, Astrid Johanna Bernal Rueda,Andrés Felipe Moggio Bessolo, and Jose Luis Ponz Tienda
99 Technology Alternatives for Workplace Safety Risk Mitigationin Construction: Exploratory Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 823Ali Karakhan, Yiye Xu, Chukwuma Nnaji, and Ola Alsaffar
Part VII Education, Training, and Learning with Technologies
100 BIM4VET, Towards BIM Training Recommendationfor AEC Professionals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 833Annie Guerriero, Sylvain Kubicki, V. Maquil, N. Mack, Yacine Rezgui, H. Li,S. Lamb, A. Bradley, and J.-P. Poli
101 Teaching Effective Collaborative Information Deliveryand Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 841Dragana Nikolic and Robert M. Leicht
102 A Story of Online Construction Masters’ Project: Is an Active OnlineIndependent Study Course Possible? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 849Gulbin Ozcan-Deniz
103 Lessons Learned from a Multi-year Initiative to Integrate Data-DrivenDesign Using BIM into Undergraduate Architectural Education . . . . . . . . . 857J. Benner and J. J. McArthur
104 Integrated and Collaborative Architectural Design: 10 Yearsof Experience Teaching BIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865João Alberto da Motta Gaspar, Regina Coeli Ruschel,and Evandro Ziggiatti Monteiro
105 Toward a Roadmap for BIM Adoption and Implementationby Small-Sized Construction Companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873Wylie Ferron and Yelda Turkan
106 BIM Implementation in Mega Projects: Challenges and Enablersin the Istanbul Grand Airport (IGA) Project . . . . . . . . . . . . . . . . . . . . . . . . 881Basak Keskin, Beliz Ozorhon, and Ozan Koseoglu
107 Virtual Learning for Workers in Robot DeployedConstruction Sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 889Soyoung Moon, Burcin Becerik-Gerber, and Lucio Soibelman
108 Building Energy Modeling in Airport Architecture Design . . . . . . . . . . . . . 897Maria Antonietta Esposito and Alessandra Donato
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 905
Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 909
xvi Contents
Introduction
A Vision for Research and Innovation in Informatics and Computing inCivil and Construction Engineering
While we move into the first quarter of the twenty-first century, the practice of civil,construction, and building engineering embraces an incommensurable transformation in theway we deliver products, process data, and interact with agents and technology. Newparadigms focused on sustainable practices, and the effective use of data and information andcomputing technologies, and automation have framed the trends we see in research initiativesand fundamental problems in civil and construction engineering disciplines. The continuousexpansion of interdisciplinary work among computing, informatics, and construction and civilengineering merges perspectives to create integrated or hybrid methods of observing,dissecting and solving central problems and of integrating relevant theories. The 2018conference and this related publication is an effort to register diversity of thinking tounderstand a phenomenon, problem, dataset, or methods that enable value creation in practiceand expand the frontiers of new, integrated knowledge.
We view the worldwide, open call for research initiatives as a survey of innovations andnovel approaches to phenomena and problems in computing and informatics in civil andconstruction engineering. The compilation is organized under seven concept clusters to alignthe contributions to the forefront of trends on investment for scientific research. The selectionin clusters was decided to better capture new advancements of knowledge within the focusareas. The conceptualization and focus were based mainly on reflections from visionarydocuments [1–3]. The focus areas cover the spectrum of aims of scientific questions and thefundamental aspects that advance understanding or solve problems. Within each area,evolving technology may transform activities and subsequently shape research practices in thecoming decade (Fig. 1).
Ivan MutisTimo Hartmann
xvii
References
1. National Science Foundation (NSF).: NSF’s 10 Big Ideas. (2018) Available: https://www.nsf.gov/news/special_reports/big_ideas/
2. National Science Foundation (NSF).: Building the future investing in discovery and innovation. In: NationalScience Board, National Science Foundation (NSF), vol. NSF-18-45, Arlington, Virginia (2018)
3. Rajkumar, R., Lee, I., Sha, L., Stankovic, J.: Cyber-physical systems: The next computing revolution. In:Design Automation Conference, pp. 731–736. (2010)
Fig. 1 Conference topics clustered in focus areas
xviii Introduction
44A BIM-Based Decision Support Systemfor Building Maintenance
Fulvio Re Cecconi , Nicola Moretti , Sebastiano Maltese ,and Lavinia Chiara Tagliabue
AbstractAvailable data about asset condition and performances can be conveyed into different Key Performance Indicators (KPIs).Many KPIs measuring technical, functional and economic/financial asset performances can be found in literature.Nevertheless, they are often strictly related to a specific scope, thus they provide an incomplete depiction of the wholeassets performances. The objective of this research is to provide facility managers and asset owners with an easyinstrument to prioritize maintenance. In order to reduce costs related to its use, the instrument, developed in the form of aDecision Support System (DSS), is based on existing and reliable performances metrics and leverages new technologieslike Building Information Modelling (BIM). Accordingly, the Facility Condition Index (FCI) is combined with theD index, a KPI related to the age of building components, developed by the authors. The joint use of the FCI and theD index, allows facility managers to make more conscious decisions. The proposed DSS helps in the definition of the bestmaintenance plan, providing a ranking of building components which require more urgent maintenance interventions.Although the DSS should be tested measuring its ability to preserve buildings and their performances on a long term, thefirst results are positive, as confirmed by the application to a case study on an office building in Italy. Moreover, theusability of the instrument has been appreciated by the users in a medium size Italian company.
KeywordsDSS ! Maintenance ! FCI ! Building condition assessment
44.1 Introduction
Management of building maintenance is one of the biggest challenges in Facility Management (FM) since in this sector,nowadays, most of the economic resources related to the construction industry are spent. Additionally, a more sustainablebuilt environment can be reached only by improving energy and environmental performances of physical assets optimizing,as instance, the maintenance choices [1].
Despite the huge amount of data produced and recorded by new technologies during the whole lifecycle of a building,facility managers make decisions on maintenance with limited knowledge concerning buildings condition [1]. Available data
F. Re Cecconi ! N. MorettiPolitecnico di Milano, 20133 Milan, Italye-mail: [email protected]
N. Morettie-mail: [email protected]
S. MalteseUniversity of Applied Sciences and Arts of Southern Switzerland, 6952 Canobbio, Switzerlande-mail: [email protected]
L. C. Tagliabue (&)University of Brescia, Brescia, Italye-mail: [email protected]
© Springer Nature Switzerland AG 2019I. Mutis and T. Hartmann (eds.), Advances in Informatics and Computing in Civil and Construction Engineering,https://doi.org/10.1007/978-3-030-00220-6_44
371
about asset condition and performances can be conveyed into different Key Performance Indicators (KPIs) [2]. Nevertheless,KPIs are often strictly related to a specific field, thus they provide an incomplete picture of the whole assets performances.
The objective of this research is to provide facility managers and asset owners with an easy instrument to prioritizemaintenance, based on existing and reliable performance metrics and leveraging new technologies like Building InformationModelling (BIM). For this purpose, a Decision Support System (DSS) based on the Facility Condition Index (FCI) andservice life of building components has been developed. The DSS has been integrated within the Building InformationModelling (BIM) approach and a has been tested through a case study concerning an office building located in Erba, Italy.
44.2 Background of the Research
In this paragraph the background of the research is presented, stressing on the possibilities and advantages provided by theuse of an effective Decision Support System (DSS) for Operations Maintenance and Repair (OM&R) management.Moreover, potentialities and criticalities of the Facility Condition Index (FCI), one of the most acknowledged KPIs formaintenance management are presented.
44.2.1 Decision Support Systems for Management of Buildings
Decision Support Systems (DSS) are frequently used when dealing with the built environment [3, 4]. Their importance liesin the possibility of comparing multiple features, computed both through subjective and objective parameters and calculationmethods. DSS weaknesses generally include: the high influence of the weights used, the reliability of the parameterscalculated and their need for large amount of data to be gathered and elaborated in order to produce reliable results. Ingeneral, they should be used in the early project phases, although their precision is higher when more data are available(and less changes are possible).
DSS can be based on several data processing techniques though, usually, they are all based on Multi-Criteria AnalysisTechniques, which allow data normalization, elaboration, comparison and eventually, alternatives’ ranking and selection.
A robust method, frequently used in association with a DSS [5], is the Analytical Hierarchy Process (AHP), which allowsfor parameters weights calculation. The method was first proposed by the mathematician Saaty in the 80s [6] and isfrequently and successfully coupled with the Delphi method, no matter the topic, in order to enhance the quality of theweights calculated and achieving more precise results [7].
Another issue to be faced in the development of a DSS concerns the connection with the data to be gathered, the less workneeds to be done, the faster will be the analysis, thus the connection with the BIM processes [8], can enhance a betterinformation flow.
44.2.2 Facility Condition Index
The Facility Condition Index (FCI) is a metric widely exploited for assessment of assets [3], thanks to its scalability from thesingle component to the whole real property [9, 10]. Therefore, it is a powerful tool which makes assets comparable inmeasures of maintenance performances. In its basic form it is used for evaluation of costs of deferred maintenance (DM),over the Current Replacement Value (CRV) of the component [11]. The FCI allows to quantify in a scale from 0 to 100(where 0 represents the best value) the condition of an asset based on the expense dedicated to maintenance operations [11].The assessment phase is a crucial issue for the calculation of the metric. Nevertheless, the calculation methodology can varyaccording to the objective to be achieved [12]. Moreover, a standardized definition of algorithms to be used cannot be foundin literature [13].
The FCI is affected by some other issues. For instance, the ratio calculated through the indicator does not represent themagnitude of the DM interventions, since the output value is mainly influenced by the CRV. For this reason, the FCIcalculated for two similar components characterized by similar deferred maintenance interventions (with analogous cost),could be affected by substantial differences according to the value of the CRV. Accordingly, the FCI should be employedalong with other indicators and coefficient, able to balance the effect of the CRV and to grasp the strategic importance of acomponent [14, 15].
372 F. Re Cecconi et al.
44.3 Research Methodology
Building Information Modeling (BIM) can be considered the cornerstone of research in construction field in recent years [16,17] and it is widely acknowledged that asset owners will gain considerable advantages from a comprehensive AssetInformation Model (AIM) furthering the strategic framework for asset management [18]. The AIM, as defined in BSI PAS1192-3:2014 [19], is the foundation of the proposed research methodology that can be described by two main steps:
1. the transition from the physical asset to the digital asset, namely the AIM;2. the maintenance operations prioritization through the DSS, reading data from the AIM.
The first phase of the research (Fig. 44.1) consists in the creation of a template for the development of BIM models ofexisting buildings. The survey and the digitization of the building and its surroundings can be expensive issues [20], whichfrequently prevent the adoption of BIM processes for management of the built environment. Therefore, a BIM modelcharacterized by a low level of detail, which can be incrementally upgraded during operation, has been developed. Thischoice allowed to create a model with approximately one day of work, starting from as-built CAD drawings and carrying outa streamlined survey of the asset and its parts. The BIM template must be constantly updated, to automate some activitiesand to improve the quality of the output. The BIM model has been developed, using Autodesk Revit, connecting the buildingobjects to a specific phase of the management process, so to create a depiction of the asset status at a specific date. Themodel, intended as geometrical objects and information associated to them, can be considered the digital twin of the physicalasset and is used, in this case, for maintenance prioritization. This approach can ease the building maintenance operation,reaching higher levels of automation in maintenance management process [21].
The data contained in the BIM model are associated with data coming from an external source, namely the maintenancecosts database (DB). The maintenance cost DB is based on the Uniclass II standard [22]. This classification system has beenused to define the minimum level of breakdown of the building, that would allow an effective association with maintenanceinterventions and costs. Costs of maintenance interventions and components’ replacement values have been definedemploying the Milan’s Municipal pricelist [23]. From a methodological point of view, this allows to define a standard set ofbuilding components associated with maintenance interventions and cost, though a case-by-case analysis has to be carriedout when the methodology is implemented.
Once the standard maintenance cost DB is defined, the Building Condition Assessment (BCA) survey phase can start.Through the BCA campaign, deferred maintenance interventions as well as the related costs are identified. Survey results arestored in the BIM model created following the bespoke BIM template that allows an easy data extraction by the DSSDynamo script (phase 2).
BIM Template
Survey
Digital asset
Physical asset
1 2 Dynamo script
Thematic plans
Prioritized maintenance
list
DSS
BCAOptimization
Algorithm
Maintenance costs DB
Fig. 44.1 Research schema
44 A BIM-Based Decision Support System for Building Maintenance 373
Among the information stored, the one related to building components’ service life are of primary importance for theprioritization algorithm. Reference Service Life data are stored in the IFC2X4 property set named Pset_ServiceLife. Thisproperty set was introduced with the version 4 of the (Industry Foundation Classes) IFC standard [24] as a replacement ofIfcServiceLife and is applicable to every IfcElement. For the purpose of this research, two are the property of thePset_ServiceLife used: ServiceLifeType and ServiceLifeDuration. The first one describes the type of value stored in theServiceLifeDuration property, it allows for 5 types of IfcPropertyEnumeratedValue and the only one used in this research is“REFERENCESERVICELIFE”, i.e. the typical service life that is quoted for an artefact under reference operating conditions[25]. The ServiceLifeDuration property contains the IfcDurationMeasure, namely the length or duration of a service life[25]. The actual service life is not stored in the model since it is calculated by the Dynamo script from the InstallationDatevalue provided by Construction Operations Building Information Exchange (COBie) [26] information.
Once the data are available, it is possible to run the algorithm, obtaining two types of output: the list of prioritizedmaintenance operations and graphical representations, e.g. thematic plans and 3D views.
44.4 DSS Development
As stated in paragraph 44.2.2, FCI gives better results when combined with other KPIs for maintenance operationsprioritization. The developed DSS is based on a combination of two KPIs for each component, the FCI and the D index: ametric that measures the service life of the component. The D index is derived from D+ and D− indexes developed by Dejacoet al. [27]. Since only two parameters are used in the DSS, no weighting system has been used, therefore the AHP methodhas not been applied.
The D+ and D− indexes cannot be used in their original form because the authors used respectively two differentcalculation methodologies, according to the age of the components, compared to the Reference Service Life (RSL).Moreover, although the two indexes are limited between 0 and 1, they work exactly the opposite way the FCI works, namelyhigher values indicate a higher performance. Thus, the D index employed in the DSS is computed with the same parametersdefined by Dejaco et al. [27] but in a slight different way: starting from 0 when the component is newly installed, the D indexincreases when the component gets old (see Eq. 44.1).
D ¼aslrsl#
12 if asl$ rsl
12 # 1 þ asl& rsl
asl
! "if asl[ rsl
#ð44:1Þ
where
• asl is the actual service life of the component, i.e. the time span from when the component has been installed or built,until now;
• rsl is the reference service life of the component [28].
Equation (44.1) shows how the D index is computed with two different equations according to the age of the component.When the actual service life of the component is lower than its reference service life, D ranges from 0 to 0.5 and is calculatedlikewise D+ by Dejaco et al. When component actual service life is bigger than the reference one, i.e. the component is tooold, D ranges from 0.5 to 1 and is computed similarly to D− by Dejaco et al. Noteworthy, in D index the value 1 is anasymptote, like it is 0 for D−. Figure 44.2 shows how the D index varies with the actual service life of a component that has areference service life of 25 years.
The proposed DSS contributes in easing informed decision-making through a graph where on the X axes is plotted theFCI of the component and on the Y axes the respective D index. This kind of graph allows to identify three areas delimitedby two ellipses (Fig. 44.3):
• the critical condition ellipse: is an ellipse with the major axis parallel to the Y axis and passing through two pointsC1(0.10, 0) and C2 (0, 0.50). Noteworthy, 0.10 is the FCI threshold for critical component (C1) and D = 0.50 means thatthe actual service life of the component is equal to its reference service life;
• the poor condition ellipse: is an ellipse with the major axis parallel to the Y axis and passing through two points P1(0.05,0) and P2 (0, 0.25). It may be highlighted that 0.05 is the FCI threshold for poor component (P1) and D = 0.25 means thatthe actual service life of the component is half of its reference service life.
374 F. Re Cecconi et al.
Other important information given by the DSS is the distance of each point (representing a component’s condition) fromthe critical or poor ellipse. The bigger the distance the greater the need of maintenance of the component. Components incritical or poor condition are classified in 5 severity classes (Fig. 44.5), according to the distance from the boundary ellipse,either critical or poor. Decision makers can then focus maintenance budget for components that actually need more urgentlymaintenance.
44.5 Application of the DSS
To validate the proposed approach, a demonstration on an office building near Como (Italy) was carried out. The asset, builtin 2006, is currently occupied by a construction company and a notary firm. It consists of one underground and three storiesabove ground (around 1000 m2 of gross surface per story). According to the proposed methodology, the BCA has beencarried out and the maintenance cost DB has been developed and adapted to the building under analysis. Figure 44.3 showsthe condition of each component analyzed, highlighting that there are objects that may need maintenance even if their FCI islower than 5%, since they are close to their reference service life (D = 50%) or even above. Among these latter, some partsof the mechanical system are very well maintained (FCI = 0%) but too old (D > 50%).
Structural components surveyed are characterized by a very good FCI (an average of 4.29%) and a very low D (around8%), since the building is quite new. Although, most of the components are in critical conditions, as can be seen in Fig. 44.4,and 30% of the analyzed elements are in poor condition. If the budget for maintenance is not enough to intervene on allcritical and poor components, the one on which resources should be spent can be identified by measuring the distance ofeach point from the boundary ellipse (Fig. 44.3).
To help decision makers, the distances from the boundary ellipse of critical and poor components have been classifiedaccording to five severity classes. Figure 44.5a shows that most of the critical components are in the first severity class (theless critical) and only one component among the critical ones is in the most severe condition (Severity 5). Figure 44.5bshows that most of the poor components are in the intermediate class of severity (Severity 3) and none is in the most severeone (Severity 5).
In Fig. 44.6 the BIM model of the case study is presented. Results computed through the DSS are stored in a customproperty set called “MaintPrior”. In Fig. 44.7 the property set filled with the values computed for the roof of the building isshown.
0 10 20 30 40 50 60 700.0
0.2
0.4
0.6
0.8
Service life
DIn
dex
Fig. 44.2 Diagram of the D index for a component with RSL = 25 years
44 A BIM-Based Decision Support System for Building Maintenance 375
44.6 Discussion and Conclusions
Asset managers must often make decisions about maintenance and renewal alternatives based on sparse data concerning theactual assets’ condition [29] wasting, as a consequence, a huge amount of economic resources. Thus, a DSS easing theprioritization of maintenance operations has a great importance for asset owners. The proposed DSS, based on two KPIs,the FCI and the D index, classifies building components according to their need for maintenance interventions. The use ofthe D index helps in overcoming the limitations of the FCI. The proposed DSS has led to good results in the case study,although some drawbacks still have to be addressed. For instance, the method does not take into account the cultural orhistoric value of the asset, the asset owner’s peculiar requirements or necessities and the intended use of the building.Despite the limitations, the usability of the tools has been appreciated by the firms appointed for maintenance management of
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
0% 5% 10% 15% 20% 25% 30% 35%
D
FCIComponents Poor conditions limit Critical conditions limit
Fig. 44.3 Components condition
52%
30%
18%
Critical Components Poor Components Good components
Fig. 44.4 Percentage ofcomponents in critical, poor andgood condition
376 F. Re Cecconi et al.
the office building in Erba, Italy, on which the case study has been carried out, mainly because the DSS leveragessemi-automated BIM processes to save and retrieve data. The future research work will be focused on widening the scope ofthe DSS, through the integration of further KPIs according to specific clients’ needs and according to specific building types.Moreover, the DSS will be further integrated and automated within the BIM approach.
(a) Critical Components (b) Poor Components
29
3
0
5
10
5
10
15
20
25
30
35
Severity 1 Severity 2 Severity 3 Severity 4 Severity 5
2
5
11
3
00
2
4
6
8
10
12
Severity 1 Severity 2 Severity 3 Severity 4 Severity 5
Fig. 44.5 Severity of the condition of components in critical (a) and poor (b) condition
Fig. 44.6 BIM model of the case study
Fig. 44.7 Custom property set
44 A BIM-Based Decision Support System for Building Maintenance 377
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