Clash Relevance Prediction in BIM Model Coordination Using Artificial Neural Network
Publication: Construction Research Congress 2024
ABSTRACT
As construction projects become more sophisticated, model coordination is critical to mitigating risk. Even though clash detection is highly automated in existing software systems, reviewing clashes and making corrections are still manual and repetitive workflows. Previous researchers leveraged machine learning and data mining techniques to analyze model coordination data and streamline decision-making. Nonetheless, gaps still remain in the fact that existing studies used limited datasets and mostly focused on MEP systems; additionally, no previous study identified which clash attribute combination is necessary to accurately predict clash relevance. By applying an Artificial Neural Network multilayer perceptron algorithm with different combinations of clashes’ attributes in the dataset, the authors achieved a precision of over 80% in predicting clash relevance. Notably, this study contributes to the body of knowledge by identifying the BIM object attributes necessary to predicting clash relevance with high precision using all major disciplines of a construction project.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Agatonovic-Kustrin, S., and Beresford, R. 2000. “Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.” Journal of pharmaceutical and biomedical analysis, 22(5), 717–727.
Aryadoust, V., and Goh, C. C. 2014. “Predicting listening item difficulty with language complexity measures: A comparative data mining study.”.
Autodesk. n.d. “What are the benefits of BIM?” Accessed January 4, 2021. https://www.autodesk.com/solutions/bim/benefits-of-bim.
Ghaffarianhoseini, A., Tookey, J., Ghaffarianhoseini, A., Naismith, N., Azhar, S., Efimova, O., and Raahemifar, K. 2017. “Building information modelling (BIM) uptake: Clear benefits, understanding its implementation, risks and challenges.” Renewable Sustainable Energy Rev. 75 (Aug): 1046–1053. https://doi.org/10.1016/j.rser.2016.11.083.
Bantan, R. A., Zeineldin, R. A., Jamal, F., and Chesneau, C. 2020. “Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network.” Mathematics, 8(5), 766.
Canakci, A., Ozsahin, S., and Varol, T. 2012. “Modeling the influence of a process control agent on the properties of metal matrix composite powders using artificial neural networks.” Powder Technology, 228, 26–35.
Hu, Y., and Castro-Lacouture, D. 2018. “Clash Relevance Prediction Based on Machine Learning.” Journal of Computing in Civil Engineering, 33(2), 04018060.
Hu, Y., Castro-Lacouture, D., Eastman, C. M., and Navathe, S. B. 2021. “Component Change List Prediction for BIM-Based Clash Resolution from a Graph Perspective.” Journal of Construction Engineering and Management, 147(8), 04021085.
Korman, T. M., Fischer, M. A., and Tatum, C. B. 2003. “Knowledge and Reasoning for MEP Coordination.” Journal of Construction Engineering and Management. 129(6), 627–634.
Korman, T. M., and Tatum, C. 2001. “Development of a Knowledge-Based System to Improve Mechanical, Electrical, and Plumbing Coordination.”. Stanford, CA: Center for Integrated Facility Engineering (CIFE), Stanford University.
Koo, H. J., and O’Connor, J. T. 2021. “Building information modeling as a tool for prevention of design defects.” Construction Innovation, 22(4), 870–890.
Koo, H. J., and O’Connor, J. T. 2022. “A Strategy for Building Design Quality Improvement through BIM Capability Analysis.” Journal of Construction Engineering and Management, 148(8), 04022066.
Leite, F. L. 2019. BIM for design coordination: A virtual design and construction guide for designers, general contractors, and MEP subcontractors. John Wiley & Sons.
Lin, Y.-C., Chen, Y.-P., Yien, H.-W., Huang, C.-Y., and Su, Y.-C. 2018. “Integrated BIM, game engine and VR technologies for health- care design: a case study in cancer hospital.” Advanced Engineering Informatics, 36, 130–145.
Lin, W. Y., and Huang, Y.-H. 2019. “Filtering of Irrelevant Clashes Detected by BIM Software Using a Hybrid Method of Rule-Based Reasoning and Supervised Machine Learning.” Applied Sciences, 9, 5324.
Mansour, M., Alsulamy, S., and Dawood, S. 2021. “Prediction of implementing ISO 14031 guidelines using a multilayer perceptron neural network approach.” Plos one, 16(1), e0244029.
Mehrbod, S., Staub-French, S., Mahyar, N., and Tory, M. 2019. “Beyond the clash: investigating BIM-based building design coordination issue representation and resolution.” Journal of Information Technology in Construction (ITcon). 24, 33–57.
NIBS (National Institute of Building Sciences). 2007. United States national building information modeling standard, version 1—Part 1: Overview, principles, and methodologies. Washington, DC: NIBS.
Olawumi, T. O., and Chan, D. W. M. 2018. “Beneficial factors of integrating building information modelling (BIM) and sustainability practices in construction projects.” In Proc., Hong Kong Int. Conf. on Engineering and Applied Science, 141–152. Hong Kong: Higher Education Forum.
Park, Y. S., and Lek, S. 2016. “Artificial neural networks: multilayer perceptron for ecological modeling.” In Developments in environmental modelling. 28, 123–140. Elsevier.
Wang, L., and Leite, F. 2015. “Process Knowledge Capture in BIM-Based Mechanical, Electrical, Plumbing Design Coordination Meetings.” Journal of Computing in Civil Engineering. 30(2), 04015017.
Yan, H., Yang, N., Peng, Y., and Ren, Y. 2020. “Data mining in the construction industry: Present status, opportunities, and future trends.” Automation in Construction. 119, 103331.
Yilmaz, I. 2009. “Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat—Turkey). Computers & Geosciences, 35(6), 1125–1138.
Zaker, R., and Coloma, E. 2018. “Virtual reality-integrated workflow in BIM-enabled projects collaboration and design review: a case study.” Visualization in Engineering. 6(1), 1–5.
Vaferi, B., Samimi, F., Pakgohar, E., and Mowla, D. 2014. “Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes.” Powder Technology. 267, 1–10.
Information & Authors
Information
Published In
History
Published online: Mar 18, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Data analysis
- Data collection
- Engineering fundamentals
- Hydrologic data
- Hydrologic engineering
- Hydrology
- Methodology (by type)
- Mitigation and remediation
- Neural networks
- Practice and Profession
- Project management
- Research methods (by type)
- Water and water resources
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.