Machine Learning and Artificial Intelligence Applications in Building Construction: Present Status and Future Trends
Publication: Construction Research Congress 2022
ABSTRACT
The use of Machine Learning (ML), Deep Learning, and Artificial Intelligence (AI) in building construction has been gaining traction since the mid 2000s. The ability to process the ever-increasing construction data, identify patterns, and predict future values has encouraged researchers to develop informed decision-making applications. This provides solutions to the challenges faced in the different areas of construction. This paper provides a literature review to identify, review, and categorize the existing body of knowledge involving the research and implementation of AI and ML in building construction. Related papers from journals were searched and identified using Scopus and Web of Science databases. Domain areas reviewed included clash detection, construction contract, cost, documents, equipment, labor, material, monitoring, planning, and scheduling productivity, risk, safety, and waste. Research studies reviewed are summarized and analyzed to identify gaps, describe the utilized algorithms and their applications, and aid in subsequent work by the research team.
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REFERENCES
Akhavian, R., and A. H. Behzadan. 2016. “Smartphone-Based Construction Workers’ Activity Recognition and Classification.” Automation in Construction 71: 198–209.
Akinosho, T. D., L. O. Oyedele, M. Bilal, A. O. Ajayi, M. D. Delgado, O. O Akinade, and A. A. Ahmed. 2020. “Deep Learning in the Construction Industry: A Review of Present Status and Future Innovations.” Journal of Building Engineering, 101827.
Antwi-Afari, M. F., H. Li, J. Seo, and A. Y. L. Wong. 2018. “Automated Detection and Classification of Construction Workers’ Loss of Balance Events Using Wearable Insole Pressure Sensors.” Automation in Construction 96: 189–99.
Arabi, S., A. Haghighat, and A. Sharma. 2020. “A Deep‐learning‐based Computer Vision Solution for Construction Vehicle Detection.” Computer‐Aided Civil and Infrastructure Engineering 35 (7): 753–67.
Awada, M., F. J. Srour, and I. M. Srour. 2021. “Data-Driven Machine Learning Approach to Integrate Field Submittals in Project Scheduling.” Journal of Management in Engineering 37 (1): 4020104.
Cheng, M.-Y., H.-C. Tsai, and W.-S. Hsieh. 2009. “Web-Based Conceptual Cost Estimates for Construction Projects Using Evolutionary Fuzzy Neural Inference Model.” Automation in Construction 18 (2): 164–72.
Chou, J.-S., M.-Y. Cheng, Y.-W. Wu, and A.-D. Pham. 2014. “Optimizing Parameters of Support Vector Machine Using Fast Messy Genetic Algorithm for Dispute Classification.” Expert Systems with Applications 41 (8): 3955–64.
Darko, A., A. P. C. Chan, M. A. Adabre, D. J. Edwards, M. R. Hosseini, and E. E. Ameyaw. 2020. “Artificial Intelligence in the AEC Industry: Scientometric Analysis and Visualization of Research Activities.” Automation in Construction 112: 103081.
Gondia, A., A. Siam, W. El-Dakhakhni, and A. H. Nassar. 2020. “Machine Learning Algorithms for Construction Projects Delay Risk Prediction.” Journal of Construction Engineering and Management 146 (1): 4019085.
Hajdasz, M. 2014. “Flexible Management of Repetitive Construction Processes by an Intelligent Support System.” Expert Systems with Applications 41 (4): 962–73.
Ul Hassan, F., and T. Le. 2021. “Computer-Assisted Separation of Design-Build Contract Requirements to Support Subcontract Drafting.” Automation in Construction 122: 103479.
Hong, T., Z. Wang, X. Luo, and W. Zhang. 2020. “State-of-the-Art on Research and Applications of Machine Learning in the Building Life Cycle.” Energy and Buildings 212: 109831.
Hsu, H.-C., S. Chang, C.-C. Chen, and I.-C. Wu. 2020. “Knowledge-Based System for Resolving Design Clashes in Building Information Models.” Automation in Construction 110: 103001.
Hu, Y., and D. Castro-Lacouture. 2019. “Clash Relevance Prediction Based on Machine Learning.” Journal of Computing in Civil Engineering 33 (2): 4018060.
Kifokeris, D., and Y. Xenidis. 2019. “Risk Source-Based Constructability Appraisal Using Supervised Machine Learning.” Automation in Construction 104: 341–59.
Kim, H., L. Soibelman, and F. Grobler. 2008. “Factor Selection for Delay Analysis Using Knowledge Discovery in Databases.” Automation in Construction 17 (5): 550–60.
Kim, K., and Y. K. Cho. 2020. “Effective Inertial Sensor Quantity and Locations on a Body for Deep Learning-Based Worker’s Motion Recognition.” Automation in Construction 113: 103126.
Kim, M.-K., J. P. P. Thedja, H.-L. Chi, and D.-E. Lee. 2021. “Automated Rebar Diameter Classification Using Point Cloud Data Based Machine Learning.” Automation in Construction 122: 103476.
Liu, X., Y. Song, W. Yi, X. Wang, and J. Zhu. 2018. “Comparing the Random Forest with the Generalized Additive Model to Evaluate the Impacts of Outdoor Ambient Environmental Factors on Scaffolding Construction Productivity.” Journal of Construction Engineering and Management 144 (6): 4018037.
Martínez‐Rojas, M., J. M. Soto‐Hidalgo, N. Marín, and M. A. Vila. 2018. “Using Classification Techniques for Assigning Work Descriptions to Task Groups on the Basis of Construction Vocabulary.” Computer‐Aided Civil and Infrastructure Engineering 33 (11): 966–81.
Mir, M., H. M. D. Kabir, F. Nasirzadeh, and A. Khosravi. 2021. “Neural Network-Based Interval Forecasting of Construction Material Prices.” Journal of Building Engineering 39: 102288.
Nath, N. D., A. H. Behzadan, and S. G. Paal. 2020. “Deep Learning for Site Safety: Real-Time Detection of Personal Protective Equipment.” Automation in Construction 112: 103085.
Oliveira, B. A. S., A. P. De Faria Neto, R. M. A. Fernandino, R. F. Carvalho, A. L. Fernandes, and F. G. Guimarães. 2021. “Automated Monitoring of Construction Sites of Electric Power Substations Using Deep Learning.” IEEE Access 9: 19195–207.
Poh, C. Q. X., C. U. Ubeynarayana, and Y. M. Goh. 2018. “Safety Leading Indicators for Construction Sites: A Machine Learning Approach.” Automation in Construction 93: 375–86.
Pulket, T., and D. Arditi. 2009. “Construction Litigation Prediction System Using Ant Colony Optimization.” Construction Management and Economics 27 (3): 241–51.
Rahimian, F. P., S. Seyedzadeh, S. Oliver, S. Rodriguez, and N. Dawood. 2020. “On-Demand Monitoring of Construction Projects through a Game-like Hybrid Application of BIM and Machine Learning.” Automation in Construction 110: 103012.
Sabillon, C., A. Rashidi, B. Samanta, M. A. Davenport, and D. V. Anderson. 2020. “Audio-Based Bayesian Model for Productivity Estimation of Cyclic Construction Activities.” Journal of Computing in Civil Engineering 34 (1): 4019048.
Son, H., C. Kim, and C. Kim. 2012. “Automated Color Model–Based Concrete Detection in Construction-Site Images by Using Machine Learning Algorithms.” Journal of Computing in Civil Engineering 26 (3): 421–33.
Song, Y., Y. Wang, F. Liu, and Y. Zhang. 2017. “Development of a Hybrid Model to Predict Construction and Demolition Waste: China as a Case Study.” Waste Management 59: 350–61.
Tang, L. C. M., A. Y. T. Leung, and C. W. Y. Wong. 2010. “Entropic Risk Analysis by a High Level Decision Support System for Construction SMEs.” Journal of Computing in Civil Engineering 24 (1): 81–94.
Tatiya, A., D. Zhao, M. Syal, G. H. Berghorn, and R. LaMore. 2018. “Cost Prediction Model for Building Deconstruction in Urban Areas.” Journal of Cleaner Production 195: 1572–80.
Tixier, A. J.-P., M. R. Hallowell, B. Rajagopalan, and D. Bowman. 2017. “Construction Safety Clash Detection: Identifying Safety Incompatibilities among Fundamental Attributes Using Data Mining.” Automation in Construction 74: 39–54.
Xiao, W., J. Yang, H. Fang, J. Zhuang, and Y. Ku. 2019. “A Robust Classification Algorithm for Separation of Construction Waste Using NIR Hyperspectral System.” Waste Management 90: 1–9.
Yuan, L., J. Guo, and Q. Wang. 2020. “Automatic Classification of Common Building Materials from 3D Terrestrial Laser Scan Data.” Automation in Construction 110: 103017.
Zhang, J., and N. M. El-Gohary. 2016. “Extending Building Information Models Semiautomatically Using Semantic Natural Language Processing Techniques.” Journal of Computing in Civil Engineering 30 (5): C4016004.
Zhu, R., X. Hu, J. Hou, and X. Li. 2021. “Application of Machine Learning Techniques for Predicting the Consequences of Construction Accidents in China.” Process Safety and Environmental Protection 145: 293–302.
Zhu, Z., and I. Brilakis. 2010. “Parameter Optimization for Automated Concrete Detection in Image Data.” Automation in Construction 19 (7): 944–53.
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Published online: Mar 7, 2022
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