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Impact of risk on time performance of highway projects in Nigeria
In developing countries, schedule predictive models are scarce for highway construction projects. In spite of this, highway projects in these countries experience severe schedule risk. This study identified critical schedule risks impacting highway projects and modelled their impact on the project’s schedule. A preliminary list of completed and ongoing highway projects published in 2017 by Nigeria’s Federal Ministry of Works and Housing (FMWH) privuded the background upon which historical data on highway projects time performance was obtained. More data was obtained from quantity surveyors and highway/civil engineers across Nigeria using the snowballing technique until 103 highway projects and their participants were purposively selected to fill out questionnaires. Relative Importance Index (RII) and Pareto’s 80/20% rule were used to analyze and identify highway projects critical delay-risk factors. Artificial Neural Network (ANN) model was developed using critical schedule risks as predictors and project time overruns as the dependent variables. The result of the study revealed that the model has good predictive accuracy (87.93%), while delay in payment, non-availability of equipment, and spare parts are the most critical highway schedule risks. This finding should spur policymakers to delegitimize indebtedness to highway contractors, which will in turn encourage the contractors to invest in construction equipment and machines.
Impact of risk on time performance of highway projects in Nigeria
In developing countries, schedule predictive models are scarce for highway construction projects. In spite of this, highway projects in these countries experience severe schedule risk. This study identified critical schedule risks impacting highway projects and modelled their impact on the project’s schedule. A preliminary list of completed and ongoing highway projects published in 2017 by Nigeria’s Federal Ministry of Works and Housing (FMWH) privuded the background upon which historical data on highway projects time performance was obtained. More data was obtained from quantity surveyors and highway/civil engineers across Nigeria using the snowballing technique until 103 highway projects and their participants were purposively selected to fill out questionnaires. Relative Importance Index (RII) and Pareto’s 80/20% rule were used to analyze and identify highway projects critical delay-risk factors. Artificial Neural Network (ANN) model was developed using critical schedule risks as predictors and project time overruns as the dependent variables. The result of the study revealed that the model has good predictive accuracy (87.93%), while delay in payment, non-availability of equipment, and spare parts are the most critical highway schedule risks. This finding should spur policymakers to delegitimize indebtedness to highway contractors, which will in turn encourage the contractors to invest in construction equipment and machines.
Impact of risk on time performance of highway projects in Nigeria
Aligamhe, Victor I. (author) / Mustapa, Muzani (author) / Ogbu, Chukwuemeka P. (author)
2024-04-02
4141562 byte
Conference paper
Electronic Resource
English
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