Mostofi, F; Tokdemir, O B and Toǧan, V (2023) Comprehensive root cause analysis of construction defects using semisupervised graph representation learning. Journal of Construction Engineering and Management, 149(9): 04023079, ISSN 0733-9364
Abstract
Quality is a substantial pillar of construction success, as its failure poses a significant threat to the construction budget and schedule. Effective root cause (RC) analysis allows for the early identification of issues leading to quality failure and proactive defect-prevention measures. This study puts forward a flexible RC analysis method that extracts useful information from construction nonconformance reports (NCRs) to identify the future trend RCs of construction defects by employing a novel graph representation learning (GRL) approach called node2vec. Node2vec was used to connect high-cost impact RC information based on shared construction defects to determine the RCs of the construction defects. Compared with the conventional RC analysis in the literature (i.e., association rule mining), the proposed node2vec offers three advantages: (1) responsiveness to large itemset, allowing its application across multiple projects with different data collection systems. (2) It receives richer semantic information (defect-related features, RC connectivity, and different cost impacts), enabling a more comprehensive understanding of underlying defects. (3) Prediction ability of future connectivity RCs, resulting in more efficient defect-prevention actions. In contrast to unsupervised RC analysis approaches, the incorporated word2vec prediction model allows the measurement of the prediction performance of related RCs (73% accuracy and 2.31% loss), providing a noticeably more accountable RC analysis and holistic defect prevention. This in turn facilitates the integration of the proposed approach with decisions regarding quality improvement in construction projects, thereby accelerating targeted decisions and interventions within related defect-prevention policies.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | association rule mining; construction quality management; graph representation learning; node2vec; root cause analysis |
| Index terms: | construction defect, construction budget, quality improvement, mining, prevention, connectivity, prediction model, accuracy, construction project, integration, root cause analysis, construction quality |
| Subjects: | contract obligations, prediction and forecasting, financial and cost management, project delivery, risk assessment, quality assurance, networking, organizational analysis, geotechnical engineering, production management, financial risk, professional development |
| Topics: | Cost Management, Project Management, Research Practice, Engineering Principles, Information Management, Risk Management, Legal Issues, Digital Applications, Quality Management, Organizational Design |
| Descriptive scope: | 3 PCA |
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