Aligamhe, V I; Mustapa, M; Ogbu, C P and Misnan, M S (2024) Modelling cost-risk impact on Nigerian highway projects using multiple linear regression and artificial neural networks. Journal of Construction in Developing Countries, 29(2), pp. 107-128. ISSN 1823-6499
Abstract
Cost predictive models for highway projects are relatively scarce in developing countries, despite the frequency and magnitude of project cost overruns in such countries. This study identified critical cost risks impacting highway projects and modelled their impacts on the actual cost of the projects. Historical cost data on highway projects published by the Nigerian Federal Ministry of Power, Works and Housing in 2017 served as a preliminary list of projects for the study, while further cost data were obtained from highway engineers and quantity surveyors across Nigeria using the snowballing technique until 103 highway projects were identified. Project participants were purposively chosen to fill out questionnaires on costrisks factors associated with highway construction projects. The relative importance index and Pareto 80/20 rule were used to analyse the collected primary data. Thereafter, multiple linear regression and artificial neural network models were developed. Findings revealed that the increase in the cost of construction materials and labour and the fluctuations in foreign exchange rates were the most significant risks impacting highway project cost performance. A comparison of the models indicated that the artificial neural networks model performed better. Hence, the artificial neural networks model is a superior technique for modelling the relationship between cost risks and cost performance of highway projects.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial neural networks; cost performance of highway projects; cost risk of highway projects; multiple linear regression; Nigeria; highway projects |
| Index terms: | cost data, modelling, construction material, highway construction, Nigeria, historical cost data, engineer, quantity surveying, housing, cost performance, questionnaire, artificial neural network, risk impact, project cost, overrun, relative importance index, actual cost, developing country |
| Subjects: | data collection methods, profession, construction type, risk assessment, analytical methods, financial and cost management, economic analysis, building materials, modelling and simulation, Geography, development economics, economics, civil engineering, accounting and finance, environmental hazards, project controls |
| Topics: | Risk Management, Roles and Professions, Construction Technology, Sustainability, Business Strategy, Cost Management, Research Practice, Geographical Context, Engineering Principles, Time Control, International Construction |
| Descriptive scope: | 3 PCA |
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