Next-Generation Building Condition Assessment: BIM and Neural Network Integration
Publication: Journal of Performance of Constructed Facilities
Volume 38, Issue 6
Abstract
The inspection of building components plays a crucial role in reducing maintenance costs throughout the building’s life cycle. Many buildings suffer from defects in the building elements, such as surface problems, cracks, and moisture problems. Building Information Modeling (BIM) has become a widely used digital tool for the construction industry, allowing for the virtual visualization, design, and simulation of structures. At the same time, progress in neural networks, a branch of artificial intelligence, presents encouraging potential for classification and evaluation. This article explores the fusion of BIM and neural networks to improve and simplify the building inspection procedure. Neural networks are implemented with BIM to conduct real-time analysis and predictions as part of an inspection framework. A data set consisting of 500 wall samples with defects from different buildings was collected through fieldwork. These data were then classified into five categories, ranging from D1 (No damage) to D5 (Collapse), based on expert opinions. A neural network was used to create a model that can show the severity of degradation of building elements. The BIM platform is utilized in the implementation of the proposed model to facilitate the exchange of information and enhance documentation during inspection. The visualization provided by BIM allows the facility management team and building owners to assess building elements through causality analysis using distinct color codes. A case study of an office building is featured in the paper to showcase the application of the proposed model for asset management. The color-coded system used in this study denotes low-performance conditions as red, medium performance conditions as yellow, and high-performance conditions as green. This article outlines the procedures for categorizing and ranking defects in building elements, specifically focusing on walls.
Practical Applications
This study explores innovative methods for assessing the condition of buildings, aiming to improve accuracy, efficiency, and predictive capabilities. Traditional building inspections are often subjective, time-consuming, and labor-intensive, making it challenging to generate consistent and comprehensive evaluations. Our research addresses these limitations by integrating BIM and neural networks. BIM provides detailed digital models of buildings, which include comprehensive information about each component. This technology enables precise and consistent assessments, reducing the subjectivity inherent in manual inspections. Neural networks, a type of artificial intelligence, can analyze complex data patterns and detect potential issues early, allowing for proactive maintenance. By combining these two technologies, our approach offers a more efficient and objective method for building condition assessment. This integration not only saves time and resources but also enhances the ability to predict and prevent future issues, ultimately extending the lifespan of building components and improving overall building performance. These advancements are particularly relevant for property managers, maintenance teams, and construction professionals seeking to optimize building maintenance and management practices. The findings of this study can significantly benefit the construction and facilities management industries by providing a more reliable and advanced method for maintaining building health and safety, thereby ensuring sustainable and cost-effective building management.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code generated or used during the study are available from the corresponding author by request.
References
Abioye, S. O., L. O. Oyedele, L. Akanbi, A. Ajayi, J. M. D. Delgado, M. Bilal, O. O. Akinade, and A. Ahmed. 2021. “Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges.” J. Build. Eng. 44 (Dec): 103299. https://doi.org/10.1016/j.jobe.2021.103299.
Balaras, C. A., K. Droutsa, E. Dascalaki, and S. Kontoyiannidis. 2005. “Deterioration of European apartment buildings.” Energy Build. 37 (5): 515–527. https://doi.org/10.1016/j.enbuild.2004.09.010.
Borin, P., and F. Cavazzini. 2019. “Condition assessment of RC bridges. Integrating machine learning, photogrammetry and BIM.” Remote Sens. Spatial Inf. Sci. 42 (Aug): 201–208.
Bortolini, R., N. Forcada, and M. Macarulla. 2017. “BIM for the integration of building maintenance management: A case study of a university campus.” In Proc., eWork and eBusiness in Architecture, Engineering and Construction: ECPPM 2016, 427–434. London: CRC Press.
BSI (British Standards Institution). 2013. Specification for information management for the capital/delivery phase of construction projects using building information modelling. PAS 1192-2. London: BSI.
Bukovics, Á., F. Lilik, L. T. Kóczy, and B. Lukács. 2023. “Complex framework for condition assessment of residential buildings.” In Proc., Int. Conf. on Recovery, Maintenance and Rehabilitation of Buildings. Berlin: Springer.
Bulsari, A. 1993. “Some analytical solutions to the general approximation problem for feedforward neural networks.” Neural Netw. 6 (7): 991–996. https://doi.org/10.1016/S0893-6080(09)80008-7.
Chen, F.-C., and M. R. Jahanshahi. 2017. “NB-CNN: Deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion.” IEEE Trans. Ind. Electron. 65 (5): 4392–4400. https://doi.org/10.1109/TIE.2017.2764844.
Chen, W., G. Chen, L. Zhao, and C. Y.-C. Chen. 2021. “Predicting drug–target interactions with deep-embedding learning of graphs and sequences.” J. Phys. Chem. A 125 (25): 5633–5642. https://doi.org/10.1021/acs.jpca.1c02419.
Cheng, D., X. Wang, Y. Zhang, and L. Zhang. 2020a. “Graph neural network for fraud detection via spatial-temporal attention.” IEEE Trans. Knowl. Data Eng. 34 (8): 3800–3813. https://doi.org/10.1109/TKDE.2020.3025588.
Cheng, J. C., W. Chen, K. Chen, and Q. Wang. 2020b. “Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms.” Autom. Constr. 112 (Apr): 103087. https://doi.org/10.1016/j.autcon.2020.103087.
Chew, L. W., X.-X. Li, and M. Y. Chew. 2023. “Climate change projection and its impacts on building façades in Singapore.” Sustainability 15 (4): 3156. https://doi.org/10.3390/su15043156.
Degtyarev, G. V., K. A. Belokur, and I. V. Sokolova. 2018. “Modeling of the building by numerical methods at assessment of the technical condition of structures.” In Proc., Materials Science Forum. Melbourne, VIC, Australia: Trans Tech Publ.
Dejaco, M. C., F. R. Cecconi, and S. Maltese. 2017. “Key performance indicators for building condition assessment.” J. Build. Eng. 9 (Jan): 17–28. https://doi.org/10.1016/j.jobe.2016.11.004.
Dong, J., and S.-X. Hu. 1997. “The progress and prospects of neural network research.” Inf. Control 26 (5): 360–368.
Dykes, C., and G. Baird. 2014. “Performance benchmarks for non-domestic buildings: Towards user perception benchmarks.” Build. Res. Inf. 42 (1): 62–71. https://doi.org/10.1080/09613218.2014.832103.
Faqih, F., and T. Zayed. 2021. “Defect-based building condition assessment.” Build. Environ. 191 (Mar): 107575. https://doi.org/10.1016/j.buildenv.2020.107575.
Farahani, A., H. Wallbaum, and J.-O. Dalenbäck. 2019. “Optimized maintenance and renovation scheduling in multifamily buildings—A systematic approach based on condition state and life cycle cost of building components.” Construct. Manage. Econ. 37 (3): 139–155. https://doi.org/10.1080/01446193.2018.1512750.
Fox, M., D. Coley, S. Goodhew, and P. De Wilde. 2014. “Thermography methodologies for detecting energy related building defects.” Renewable Sustainable Energy Rev. 40 (Dec): 296–310. https://doi.org/10.1016/j.rser.2014.07.188.
Gaspar, K., M. Casals, and M. Gangolells. 2016. “Classifying system for façades and anomalies.” J. Perform. Constr. Facil. 30 (1): 04014187. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000693.
Guo, S., B. Zhang, T. Yang, D. Lyu, and W. Gao. 2019. “Multitask convolutional neural network with information fusion for bearing fault diagnosis and localization.” IEEE Trans. Ind. Electron. 67 (9): 8005–8015. https://doi.org/10.1109/TIE.2019.2942548.
Hammad, A., S. Setayeshgar, C. Zhang, and Y. Asen. 2012. “Automatic generation of dynamic virtual fences as part of BIM-based prevention program for construction safety.” In Proc., 2012 Winter Simulation Conf. New York: IEEE.
He, X., T. Wang, K. Wu, and H. Liu. 2021. “Automatic defects detection and classification of low carbon steel WAAM products using improved remanence/magneto-optical imaging and cost-sensitive convolutional neural network.” Measurement 173 (Mar): 108633. https://doi.org/10.1016/j.measurement.2020.108633.
Huang, M., Y. Liu, X. Ao, K. Li, J. Chi, J. Feng, H. Yang, and Q. He. 2022. “AUC-oriented graph neural network for fraud detection.” In Proc., ACM Web Conf. 2022. New York: Association for Computing Machinery.
Jacobsen, A., T. Hitaka, and M. Nakashima. 2010. “Online test of building frame with slit-wall dampers capable of condition assessment.” J. Constr. Steel Res. 66 (11): 1320–1329. https://doi.org/10.1016/j.jcsr.2010.04.011.
Johnston, D., S. L. McFallan, and P. Tilley. 2002. “Implementation of a property standard index.” Facilities 20 (3–4): 136–144. https://doi.org/10.1108/02632770210697431.
Koch, C., K. Georgieva, V. Kasireddy, B. Akinci, and P. Fieguth. 2015. “A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure.” Adv. Eng. Inf. 29 (2): 196–210. https://doi.org/10.1016/j.aei.2015.01.008.
Koch, D., M. Despotovic, M. Sakeena, M. Döller, and M. Zeppelzauer. 2018. “Visual estimation of building condition with patch-level ConvNets.” In Proc., 2018 ACM Workshop on Multimedia for Real Estate Tech. New York: Association for Computing Machinery.
Lavy, S., J. A. Garcia, and M. K. Dixit. 2014. “KPIs for facility’s performance assessment, Part I: Identification and categorization of core indicators.” Facilities 32 (5–6): 256–274. https://doi.org/10.1108/F-09-2012-0066.
Lin, Y.-C., and Y.-C. Su. 2013. “Developing mobile-and BIM-based integrated visual facility maintenance management system.” Sci. World J. 2013 (1): 124249. https://doi.org/10.1155/2013/124249.
Linggar, S., A. Aminullah, and A. Triwiyono. 2019. “Analysis of building and its components condition assessment case study of dormitory buildings.” In Proc., MATEC Web of Conf. Les Ulis, France: EDP Sciences.
Locatelli, M., E. Seghezzi, L. Pellegrini, L. C. Tagliabue, and G. M. Di Giuda. 2021. “Exploring natural language processing in construction and integration with building information modeling: A scientometric analysis.” Buildings 11 (12): 583. https://doi.org/10.3390/buildings11120583.
López, F. J., P. M. Lerones, J. Llamas, J. Gómez-García-Bermejo, and E. Zalama. 2018. “A review of heritage building information modeling (H-BIM).” Multimodal Technol. Interact. 2 (2): 21. https://doi.org/10.3390/mti2020021.
Lu, W., A. Fung, Y. Peng, C. Liang, and S. Rowlinson. 2015. “Demystifying construction project time–effort distribution curves: BIM and non-BIM comparison.” J. Manage. Eng. 31 (6): 04015010. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000356.
Lupăşteanu, V., R. Lupăşteanu, and C. Chingălată. 2022. “Condition assessment of buildings in Romania: A proposed method and case study.” J. Build. Eng. 47 (Apr): 103814. https://doi.org/10.1016/j.jobe.2021.103814.
Marmo, R., M. Nicolella, F. Polverino, and A. Tibaut. 2019. “A methodology for a performance information model to support facility management.” Sustainability 11 (24): 7007. https://doi.org/10.3390/su11247007.
Maslesa, E., P. A. Jensen, and M. Birkved. 2018. “Indicators for quantifying environmental building performance: A systematic literature review.” J. Build. Eng. 19 (Aug): 552–560. https://doi.org/10.1016/j.jobe.2018.06.006.
Matos, R., et al. 2022. “Building condition indicators analysis for BIM-FM integration.” Arch. Comput. Methods Eng. 29 (6): 3919–3942.
Motawa, I., and A. Almarshad. 2013. “A knowledge-based BIM system for building maintenance.” Autom. Constr. 29 (Jan): 173–182. https://doi.org/10.1016/j.autcon.2012.09.008.
Pan, Y., and L. Zhang. 2021. “Roles of artificial intelligence in construction engineering and management: A critical review and future trends.” Autom. Constr. 122 (Feb): 103517. https://doi.org/10.1016/j.autcon.2020.103517.
Pan, Y., and L. Zhang. 2023. “Integrating BIM and AI for smart construction management: Current status and future directions.” Arch. Comput. Methods Eng. 30 (2): 1081–1110. https://doi.org/10.1007/s11831-022-09830-8.
Pärn, E. A., and D. J. Edwards. 2017. “Conceptualising the FinDD API plug-in: A study of BIM-FM integration.” Autom. Constr. 80 (Aug): 11–21. https://doi.org/10.1016/j.autcon.2017.03.015.
Rakha, T., Y. El Masri, K. Chen, E. Panagoulia, and P. De Wilde. 2022. “Building envelope anomaly characterization and simulation using drone time-lapse thermography.” Energy Build. 259 (Mar): 111754. https://doi.org/10.1016/j.enbuild.2021.111754.
Sajid, S., and L. Chouinard. 2019. “Impulse response test for condition assessment of concrete: A review.” Constr. Build. Mater. 211 (Jun): 317–328. https://doi.org/10.1016/j.conbuildmat.2019.03.174.
Setiono, R., and W. K. Leow. 2000. “FERNN: An algorithm for fast extraction of rules from neural networks.” Appl. Intell. 12 (Jan): 15–25. https://doi.org/10.1023/A:1008307919726.
Shen, Z., and R. R. Issa. 2010. Quantitative evaluation of the BIM-assisted construction detailed cost estimates. Rotterdam, Netherlands: International Council for Research and Innovation in Building and Construction.
Shohet, I. M. 2003. “Building evaluation methodology for setting maintenance priorities in hospital buildings.” Construct. Manage. Econ. 21 (7): 681–692. https://doi.org/10.1080/0144619032000115562.
Singh Ahluwalia, S. 2008. A framework for efficient condition assessment of the building infrastructure. Waterloo, ON, Canada: Univ. of Waterloo.
Sony, S., S. Laventure, and A. Sadhu. 2019. “A literature review of next-generation smart sensing technology in structural health monitoring.” Struct. Control Health Monit. 26 (3): e2321. https://doi.org/10.1002/stc.2321.
Sun, H., H. V. Burton, and H. Huang. 2021. “Machine learning applications for building structural design and performance assessment: State-of-the-art review.” J. Build. Eng. 33 (Jul): 101816. https://doi.org/10.1016/j.jobe.2020.101816.
Tan, Y., S. Li, H. Liu, P. Chen, and Z. Zhou. 2021. “Automatic inspection data collection of building surface based on BIM and UAV.” Autom. Constr. 131 (Nov): 103881. https://doi.org/10.1016/j.autcon.2021.103881.
Tsai, Y.-H., S.-H. Hsieh, and S.-C. Kang. 2014. “A BIM-enabled approach for construction inspection.” Comput. Civ. Build. Eng. 2014 (1): 721–728. https://doi.org/10.1061/9780784413616.090.
Valero, L. R., V. F. Sasso, and E. P. Vicioso. 2019. “In situ assessment of superficial moisture condition in façades of historic building using non-destructive techniques.” Case Stud. Constr. Mater. 10 (Apr): e00228. https://doi.org/10.1016/j.cscm.2019.e00228.
Wen, T., and M. Mydin. 2013. “Distinctive structural and non-structural building defects and failures in educational buildings.” Analele Universităţii Eftimie Murgu Reşiţa. Fascicula de Inginerie 20 (3): 67–76.
Wetzel, E. M., and W. Y. Thabet. 2016. “Utilizing Six Sigma to develop standard attributes for a Safety for Facilities Management (SFFM) framework.” Saf. Sci. 89 (Nov): 355–368. https://doi.org/10.1016/j.ssci.2016.07.010.
Xie, X., Q. Lu, D. Rodenas-Herraiz, A. K. Parlikad, and J. M. Schooling. 2020. “Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance.” Eng. Constr. Archit. Manage. 27 (8): 1835–1852. https://doi.org/10.1108/ECAM-11-2019-0640.
Xu, B., H. Shen, B. Sun, R. An, Q. Cao, and X. Cheng. 2021. “Towards consumer loan fraud detection: Graph neural networks with role-constrained conditional random field.” In Proc., AAAI Conf. on Artificial Intelligence. Washington, DC: Association for the Advancement of Artificial Intelligence.
Zhang, F., A. P. Chan, A. Darko, Z. Chen, and D. Li. 2022. “Integrated applications of building information modeling and artificial intelligence techniques in the AEC/FM industry.” Autom. Constr. 139 (Jul): 104289. https://doi.org/10.1016/j.autcon.2022.104289.
Zhu, J., N. Chen, and W. Peng. 2018. “Estimation of bearing remaining useful life based on multiscale convolutional neural network.” IEEE Trans. Ind. Electron. 66 (4): 3208–3216. https://doi.org/10.1109/TIE.2018.2844856.
Zhuang, Y., X. Ji, L. Sun, K. Kajiwara, J.-D. Kang, and T. Nagae. 2024. “Damage inference and residual capacity assessment for an E-Defense 2018 ten-story RC test structure.” Eng. Struct. 302 (Mar): 117317. https://doi.org/10.1016/j.engstruct.2023.117317.
Information & Authors
Information
Published In
Journal of Performance of Constructed Facilities
Volume 38 • Issue 6 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 28, 2024
Accepted: Jun 28, 2024
Published online: Sep 27, 2024
Published in print: Dec 1, 2024
Discussion open until: Feb 27, 2025
ASCE Technical Topics:
- Architectural engineering
- Artificial intelligence (AI)
- Artificial intelligence and machine learning
- Building information modeling
- Building management
- Buildings
- Case studies
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Defects and imperfections
- Engineering fundamentals
- Inspection
- Materials characterization
- Materials engineering
- Methodology (by type)
- Models (by type)
- Neural networks
- Research methods (by type)
- Structural engineering
- Structures (by type)
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.