Formulation of Feature and Label Space Using Modified Delphi in Support of Developing a Machine-Learning Algorithm to Automate Clash Resolution
Publication: Journal of Construction Engineering and Management
Volume 150, Issue 3
Abstract
To improve the current manual and iterative nature of clash resolution on construction projects, current research efforts continue to explore and test the utilization of machine-learning algorithms to automate the process. Though current research shows significant accuracy in automating clash resolution, many have failed to provide clear explanation and justification for the selection of their feature and label space. Since this is critical in developing an effective and explainable solution in machine learning, it is crucial to address this research gap. In this paper, the authors utilize an in-depth literature review and industry interviews to capture domain knowledge on how design clashes are resolved by industry experts. From analysis of the knowledge captured, we identified 23 factors considered by experts when resolving clashes and five alternative solutions/options to resolve a clash. Using a pool of industry experts, a modified Delphi approach was conducted to validate the factors and options and to determine a priority ranking. The authors identified 94 industry experts based on a predetermined qualification matrix to take part in the modified Delphi. Twelve participants responded and took part in the first round, and 11 completed the second round. A consensus was reached on all clash factors and resolution options. Factors including “clashing elements type,” “constrained slope,” “critical element in the clash,” “location of the clash,” “code compliance,” and “project stage clashing element is in” were ranked as the most important factors, while “clashing element material” and “insulation type” were considered the least important. Participants also showed more preference to the “moving the clashing element with low priority in/along directions” option to resolve clashes. These identified factors and options will be utilized to collect specific clash data to train and test effective and explainable machine-learning algorithms toward automating clash resolution.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. This includes interviews and intercoder reliability analysis results.
Acknowledgments
The authors would like to acknowledge and thank the construction industry partners who took part in this work and provided their valuable time, experience, and 3D models to support the research. The views and findings expressed in this paper are those of the authors and do not reflect those of the industry partners.
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© 2023 American Society of Civil Engineers.
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Received: Jun 19, 2023
Accepted: Oct 26, 2023
Published online: Dec 30, 2023
Published in print: Mar 1, 2024
Discussion open until: May 30, 2024
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