Mechanical, Electrical, and Plumbing (MEP) systems are the most complex and tedious scopes aspects in construction. Besides, it is also error-prone due to the large amount of data that will lead to time and cost overrun. While numerous studies have addressed these issues using Building Information Modeling (BIM) and heuristic methods that focused on designing phase, the application of Artificial Intelligence (AI) remains underexplored. In this paper, a generative AI approach was developed to enhance the quality and efficiency of design review. By leveraging the AI ability to extract and analyze textual information and machine learning to identify clash, we propose a streamlined design process. The purpose of this research is to develop a new approach that combines the ability of generative AI to provide information and BIM to improve and simplify the review process of a design. Furthermore, this research will address the current typical BIM error that occur. BIM is used as an application to model, review, and modify the design. A model was developed to demonstrate and prove the effectiveness of generative AI, machine learning, and BIM to improve and simplify the process of reviewing model. The plumbing section which is prone to redesign due to clash is used as a study case. The study aims to provide a new perspective for reviewing model designs, showcasing the potential of combining AI and BIM to solve complex construction problems.

Ahmadpanah, H., Haidar, A., &Latifi, S. M. (2023). BIM and Machine Learning (ML) Integration in Design Coordination: Using ML to Automate Object Classification for Clash Detection. Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe, 2, 619–628. https://doi.org/10.52842/conf.ecaade.2023.2.619

Beetz, J., vanBerlo, L., deLaat, R., &van denHelm, P. (2010). An Open Source IFC Model Server. Proceedings of the CIB W78 2010: 27th International Conference–Cairo, Egypt, 16–18. http://bimserver.org/wp-content/uploads/2010/11/Beetz_Berlo-CIB-W78_Cairo.pdf

Blundell, S. (2018). BIM and building services: You’re doing it wrong. Planning, Building & Construction Today. https://www.pbctoday.co.uk/news/digital-construction-news/bim-news/bim-and-building-services/41022/

Chauhan, K., Peltokorpi, A., Abou-Ibrahim, H., &Seppänen, O. (2022). Mechanical, Electrical and Plumbing Coordination Practices: Case Finnish Construction Market. 30th Annual Conference of the International Group for Lean Construction, IGLC 2022, 635–644. https://doi.org/10.24928/2022/0169

Cheng, Z., Dong, H., Jia, R., Wu, P., Han, S., Cheng, F., &Zhang, D. (2022). FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 1, 1150–1166. https://doi.org/10.18653/v1/2022.acl-long.82

Chidambaram, S. (2020). The Application of Clash-Detection Processes in Building Information Modelling for Rebars. Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction, 172, 53–69. https://doi.org/10.1680/jsmic.20.00005

Choi, W., Kim, C., Heo, S., &Na, S. (2022). The Modification of A∗Pathfinding Algorithm for Building Mechanical, Electronic and Plumbing (MEP) Path. IEEE Access, 10, 65784–65800. https://doi.org/10.1109/ACCESS.2022.3184106

Christiano, P. F., Leike, J., Brown, T. B., Martic, M., Legg, S., &Amodei, D. (2017). Deep Reinforcement Learning from Human Preferences. Advances in Neural Information Processing Systems, 2017, 4300–4308.

Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., &Ameyaw, E. E. (2020). Artificial Intelligence in the AEC Industry: Scientometric Analysis and Visualization of Research Activities. Automation in Construction, 112. https://doi.org/10.1016/j.autcon.2020.103081

Es, S., James, J., Espinosa-Anke, L., &Schockaert, S. (2024). RAGAS: Automated Evaluation of Retrieval Augmented Generation. EACL 2024 - 18th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of System Demonstrations, 150–158.

Ghaffarianhoseini, A., Tookey, J., Ghaffarianhoseini, A., Naismith, N., Azhar, S., Efimova, O., &Raahemifar, K. (2017). Building Information Modelling (BIM) Uptake: Clear Benefits, Understanding its Implementation, Risks and Challenges. Renewable and Sustainable Energy Reviews, 75, 1046–1053. https://doi.org/10.1016/j.rser.2016.11.083

Guo, Z., Li, Q., Wang, Q., Qiao, S., Mei, T., &Zuo, W. (2024). Reducing redundancy in large MEP building information models for facility management using oriented bounding boxes and hash sets. Automation in Construction, 165. https://doi.org/10.1016/j.autcon.2024.105514

Hassanain, M. A., Adewale, B., Al-Hammad, A. M., &Sanni-Anibire, M. O. (2018). Factors Affecting Building Services’ Coordination During the Design Development and Review Stages. Built Environment Project and Asset Management, 8, 64–77. https://doi.org/10.1108/BEPAM-06-2017-0040

International Council Code. (2021). 2021 International Plumbing Code.

Korman, T. M., &Tatum, C. B. (2006). Prototype Tool for Mechanical, Electrical, and Plumbing Coordination. Journal of Computing in Civil Engineering, 20, 38–48. https://doi.org/10.1061/(asce)0887-3801(2006)20:1(38)

Koubaa, A. (2023). GPT-4 vs. GPT-3.5: A Concise Showdown. TechRxiv, 0–5. https://doi.org/10.20944/preprints202303.0422.v1

LangChain. (2024). Conceptual Guide LangChain. https://python.langchain.com/v0.2/docs/concepts/

Leng, S., Hu, Z.-Z., Luo, Z., Zhang, J.-P., &Lin, J.-R. (2019). Automatic MEP Knowledge Acquisition Based on Documents and Natural Language Processing. 0, 800–809.

Lin, W. Y., &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 (Switzerland), 9. https://doi.org/10.3390/app9245324

Luo, S., Yao, J., Wang, S., Wang, Y., &Lu, G. (2022). A Sustainable BIM-based Multidisciplinary Framework for Underground Pipeline Clash Detection and Analysis. Journal of Cleaner Production, 374. https://doi.org/10.1016/j.jclepro.2022.133900

Mahmoud, M., Chen, W., Yang, Y., &Li, Y. (2024). Automated BIM Generation for Large-scale Indoor Complex Environments Based on Deep Learning. Automation in Construction, 162. https://doi.org/10.1016/j.autcon.2024.105376

Nasuha, A. N., Rahman, R. A., &Haron, A. T. (2023). Design Coordination in BIM: Clash Resolution Process and Affecting Factors. AIP Conference Proceedings, 2688. https://doi.org/10.1063/5.0111616

Park, J. H., &Storch, R. L. (2002). Pipe-routing Algorithm Development: Case Study of a Ship Engine Room Design. Expert Systems with Applications, 23, 299–309. https://doi.org/10.1016/S0957-4174(02)00049-0

Penmetsa, C. (2024). Machine learning using Scikit-Learn (sklearn) — Evaluating Regression model using metrics. Medium. https://medium.com/codenx/machine-learning-using-scikit-learn-sklearn-evaluating-regression-model-using-metrics-0414107a7e22

Pestana, E., Paice, A., &West, S. (2024). Optimizing MEP Design in Early AEC Projects Through Generative Design. Automation in Construction, 165. https://doi.org/10.1016/j.autcon.2024.105566

RAGAS. (2023). Ragas Metrics. Ragas. https://docs.ragas.io/en/stable/concepts/metrics/index.html

Rajabi, M., Bigga, T., &Bartl, M. A. (2015). Optimization of the quantity take-off (QTO) process for mechanical, electrical and plumbing (MEP) trades in tender estimation phase of the construction projects. 32nd International Symposium on Automation and Robotics in Construction and Mining: Connected to the Future, Proceedings. https://doi.org/10.22260/isarc2015/0022

Rapson, C. J., Seet, B. C., Naeem, M. A., Lee, J. E., Al-Sarayreh, M., &Klette, R. (2018). Reducing the Pain: A Novel Tool for Efficient Ground-Truth Labelling in Images. International Conference Image and Vision Computing New Zealand, 2018, 1–9. https://doi.org/10.1109/IVCNZ.2018.8634750

Revitapidocs. (2023). Revit API 2023. (n.d.). https://www.revitapidocs.com/2023/

S, R., &George, D. G. (2022). Relative Study on Clash Detection and its Effects on BIM Modelling. International Journal for Research in Applied Science and Engineering Technology, 10, 1519–1525. https://doi.org/10.22214/ijraset.2022.46881

Sato, K. (2023). Application of Geospatial Data to Architectural Design Education. Engineering Proceedings, 55. https://doi.org/10.3390/engproc2023055038
Savitri, D. M., Juliastuti, &Pramudya, A. A. (2020). Clash Detection Analysis with BIM-based Software on Midrise Building Construction Project. IOP Conference Series: Earth and Environmental Science, 426. https://doi.org/10.1088/1755-1315/426/1/012002

Singh, J., Deng, M., &Cheng, J. C. P. (2018). Implementation of Mass Customization for MEP Layout Design to Reduce Manufacturing Cost in One-off Projects. IGLC 2018 - Proceedings of the 26th Annual Conference of the International Group for Lean Construction: Evolving Lean Construction Towards Mature Production Management Across Cultures and Frontiers, 1, 625–635. https://doi.org/10.24928/2018/0519

Teo, Y. H., Yap, J. H., An, H., Yu, S. C. M., Zhang, L., Chang, J., &Cheong, K. H. (2022). Enhancing the MEP Coordination Process with BIM Technology and Management Strategies. Sensors, 22. https://doi.org/10.3390/s22134936

Walch, K. (2020). Data Preparation & Labeling for AI 2020. Cognilytica - Medium. https://medium.com/cognilytica/data-preparation-labeling-for-ai-2020-b512a5ed777c

Wang, J., Wang, X., Shou, W., Chong, H. Y., &Guo, J. (2016). Building Information Modeling-based Integration of MEP Layout Designs and Constructability. Automation in Construction, 61, 134–146. https://doi.org/10.1016/j.autcon.2015.10.003

Wang, L., Qian, B., Cui, X., &Chao, L. (2018). Application of BIM Technology in Interference Check in Construction Work. 170, 116–123. https://doi.org/10.2991/iceep-18.2018.20

Wiik, L. (2024). GPT-4o vs. GPT-4 vs. Gemini 1.5 — Performance Analysis. Medium. https://medium.com/@lars.chr.wiik/gpt-4o-vs-gpt-4-vs-gemini-1-5-performance-analysis-6bd207a2c580

Williams, B. (2023). Building Information Twinning: Investigating the Software Architecture of a Building’s Digital Twin.

Xie, X., Zhou, J., Fu, X., Zhang, R., Zhu, H., &Bao, Q. (2022). Automated Rule Checking for MEP Systems Based on BIM and KBMS. Buildings, 12. https://doi.org/10.3390/buildings12070934

Yuvita, R. L., &Budiwirawan, A. (2022). Analysis of the Advantages and Disadvantages of Using Autodesk Revit for the Dean Building of the Faculty of Education, Universitas Negeri Semarang. Jurnal Teknik Sipil Dan Perencanaan, 24, 91–98. https://doi.org/10.15294/jtsp.v24i2.36613

Zeynep Onur, A., &Nouban, F. (2019). Software in the Architectural Presentation and Design of Buildings: State-of-the-art. International Journal of Innovative Technology and Exploring Engineering, 8, 2723–2729. https://doi.org/10.35940/ijitee.J9486.0881019

Zhang, F., Chan, A. P. C., Darko, A., Chen, Z., &Li, D. (2022). Integrated Applications of Building Information Modeling and Artificial Intelligence Techniques in the AEC/FM Industry. Automation in Construction, 139. https://doi.org/10.1016/j.autcon.2022.104289