Wusu, G E; Alaka, H; Yusuf, W; Mporas, I; Toriola-Coker, L and Oseghale, R (2024) A machine learning approach for predicting critical factors determining adoption of offsite construction in Nigeria. Smart and Sustainable Built Environment, 13(6), pp. 1408-1433. ISSN 2046-6099
Abstract
Purpose: Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors. Design/methodology/approach: The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI). Findings: The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors. Research limitations/implications: Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond. Practical implications: The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered. Originality/value: The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | machine learning; Nigeria; offsite construction; questionnaire survey |
| Index terms: | construction industry, decision tree, critical factor, Nigeria, Africa, exercise, building information modelling, influencing factor, housing, machine learning, Likert scale, questionnaire, methodology, survey, artificial neural network, literature review, forest |
| Subjects: | modelling and simulation, data analysis and analytics, artificial intelligence, regions and continents, risk assessment, data collection methods, decision analysis, industry analysis, information systems, environmental science, construction type, health behaviours and lifestyles, research methods, Geography |
| Topics: | Digital Applications, Research Practice, Construction Technology, Geographical Context, Health and Safety, Sustainability, Risk 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