Adedokun, O; Egbelakin, T and Omotayo, T (2024) Random forest and path diagram taxonomies of risks influencing higher education construction projects. International Journal of Construction Management, 24(1), pp. 66-74. ISSN 1562-3599
Abstract
While risk factors are sine qua non for construction projects' non-performance, the research efforts are directed toward the likelihood of risks at the detriment of their level of influence on higher education building projects. This study assessed the perceptions of construction key stakeholders about the influence of risk factors on higher education building projects using machine learning-based random forest classification. A questionnaire survey was administered to four hundred and sixty-five (465) respondents comprising clients' representatives, consultants, and contractors across five (5) higher education institutions in Nigeria. Of 465 questionnaires, 295 retrieved were suitable for the analysis implying a 63.44% response rate. The Random Forest (RF) classification used 295 samples, out of which 189 (64%) formed the training dataset, while the validation and testing data sets are 47 (16%) and 59 (20%), respectively. The RF model accuracy conducted shows the optimized model with the test accuracy and out-of-bag accuracy (OOB). The study clustered 58 risk factors into four comprising (i) security, access, health, and safety risks, (ii) construction dispute resolution risks, (iii) construction planning and contract documentation risks, and (iv) construction cost and management risks. Further, the proposed recommendations could help enhance the performance of higher education building projects.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction projects; Nigeria; performance; random forest classification; risk factors |
| Index terms: | validation, model accuracy, construction dispute, testing, Nigeria, accuracy, construction cost, dataset, survey, contract documentation, taxonomies, higher education institution, construction planning, higher education, questionnaire, management risk, machine learning, resolution, forest, construction project, risk factor |
| Subjects: | Geography, environmental science, construction planning, contractual arrangements, production management, knowledge organization and systems, dispute resolution, financial and cost management, professional development, data management, educational institutions, analytical methods, conflict resolution, artificial intelligence, professional practice, environmental hazards, data collection methods, financial risk |
| Topics: | Engineering Principles, Stakeholder Management, Information Management, Geographical Context, Procurement, Site Management, Research Practice, Project Management, Digital Applications, Education, Cost Management, Legal Issues, Sustainability |
| 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