Harode, A; Thabet, W and Leite, F (2024) Formulation of feature and label space using modified Delphi in support of developing a machine-learning algorithm to automate clash resolution. Journal of Construction Engineering and Management, 150(3): 04023173, ISSN 0733-9364
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 x-y-z 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.
| Item Type: | Article |
|---|---|
| Index terms: | qualification, resolution, insulation, option, literature review, accuracy, learning algorithm, slope, interview, preference, machine learning, construction project, code compliance |
| Subjects: | data collection methods, building materials, data analysis and analytics, artificial intelligence, educational resources, algorithms, decision-making and reasoning, professional development, health safety and environment, production management, geotechnical engineering, decision analysis, conflict resolution |
| Topics: | Design Practice, Digital Applications, Education, Research Practice, Project Management, Engineering Principles, Information Management, Health and Safety, Risk Management, Stakeholder Management |
| Descriptive scope: | 4 PCEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here