Shafaat, A; Marbouti, F and Mahfouz, T (2023) Early warning system for highway construction projects using GA-SVM. International Journal of Construction Management, 23(14), pp. 2348-2357. ISSN 1562-3599
Abstract
Construction project changes vis-a-vice their impact on the cost and time performance has been the main concern of practitioners and scholars for decades; hence, many attempts have been made to develop early warning mechanisms for decision-makers. Since many factors drive project changes, various strategies can be adopted to mitigate further alterations after awarding the contract. This study aimed at developing multiple Support Vector Machine (SVM) classifiers to enhance change prediction accuracy. 5,628 completed construction-oriented projects datasets comprising preconstruction information, obtained from the Indiana Department of Transportation (INDOT), were used to develop the models. All free kernel parameters were determined using Genetic Algorithm (GA) optimization. The results show that the models built using SVM classifiers with Radial Basis Function (RBF) kernel function were successful in decreasing the uncertainty associated with change order occurrence in the early phases of the projects. Therefore, the study recommends that agencies, developers, and contractors store historical project data to build early warning systems using machine learning techniques.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | change; data mining; early warning; genetic algorithm; prediction; support vector machine |
| Index terms: | project data, highway construction, data mining, construction project, accuracy, dataset, time performance, practitioner, genetic algorithm, strategy, machine learning, change order, agency, early warning |
| Subjects: | data science, practitioner, algorithms, project controls, sociology, contractual condition, data management, civil engineering, management, data collection methods, production management, professional development, financial risk, artificial intelligence |
| Topics: | Contract Administration, Information Management, Engineering Principles, Digital Applications, Research Practice, Business Strategy, Time Control, Roles and Professions, Project Management, Cost Management |
| Descriptive scope: | 5 PCTEA |
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