Mahdavian, A; Shojaei, A; Salem, M; Yuan, J S and Oloufa, A A (2021) Data-driven predictive modeling of highway construction cost items. Journal of Construction Engineering and Management, 147(3): 04020180, ISSN 0733-9364
Abstract
The highway network is an economically necessary form of transportation that has a significant impact on the quality of the life of the citizens who use it. Cost overruns in highway projects have been a universal occurrence that jeopardize the development, maintenance, and expansion of this vital infrastructure. Incorrect cost estimations can drive decision makers to pass ineffective policies that have played a large role in the cost overruns of transportation construction projects. The existing prediction models in the literature are limited in one or multiple areas of modeling approach, inputs, and model development robustness. In this research, a model was developed to accurately predict the total construction cost of highway projects by utilizing machine learning algorithms. This study developed a modeling pipeline to automate much of the cost forecasting process, reducing the amount of manual work and dependence on skilled data scientists. This study used the Florida Department of Transportation's (FDOT's) critical highway construction cost items between 2001 and 2017 to test the model. The highways of Florida were selected for testing due to the states' population growth, high immigrant population, logistics, and hurricane frequency. This study used a pool of five categories of independent variables (69 variables total), including the construction market, energy market, socioeconomics, US economy, and temporal variables, which were compiled from relevant sources and existing literature. The results revealed that our linear model exhibits superiority in generalization and prediction of cost items over nonlinear models and is capable of accurately forecasting highway construction costs. Our suggested approach in this study also provides more accurate forecasts for the detailed cost estimation by considering the monthly historical information for the average 92.6% of the six highway construction types mentioned with a 92.51% prediction accuracy. By employing our developed model, local governments, network operators, contractors, and logistics sectors would be capable of a more exact prediction of highway construction costs.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction cost; cost forecast; data-driven predictive modeling; highway construction; machine learning; regression analysis |
| Index terms: | cost estimating, predictive modelling, local government, prediction model, accuracy, construction project, population, model development, machine learning, forecasting, cost forecasting, pipeline, construction cost, cost overrun, independent variable, immigrant, highway construction, regression analysis, construction market, testing, modelling |
| Subjects: | civil engineering, economics, production management, professional development, demography, market analysis, administrative law, infrastructure and transport systems, statistical analysis, artificial intelligence, professional practice, financial and cost management, analytical methods, prediction and forecasting |
| Topics: | Legal Issues, Digital Applications, Urban Studies, Research Practice, Project Management, Engineering Principles, Information Management, Cost Management |
| Descriptive scope: | 3 PCA |
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