Quantitative analysis for modeling uncertainty in construction costs of transportation projects with external factors

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Baek, Minsoo
Ashuri, Baabak
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Highway construction costs are subject to significant upward and downward variations from project to project and over time. Variations in construction cost disturb transportation agencies in making right investment decisions and estimating accurate construction costs for projects. Transportation agencies face considerable uncertainty in estimating project costs that often leads to significant over- and under-estimation of highway construction costs. The underestimation of project costs can lead to cost overrun, financial problem, and project delay or cancellation. The overestimation of project costs results in an inefficient budget allocation of public funds that could be used on other needed projects. Transportation agencies can also face credibility issues with the public if cost estimation problems remain unresolved. A wide range of variables has been identified in different studies to explain variations in construction cost. There is a value in conducting a research study that attempts to consider a comprehensive list of variables with potentials to explain the variations. The study needs to simultaneously take into account all possible explanatory variables to examine their relations with construction costs. The overarching objective of this research is to assess the effects of several potential variables on explaining variations in submitted unit price bids for major asphalt line items in highway projects. First, stepwise regression analysis will be utilized to develop an explanatory model for describing variations in the submitted unit price bid. The identified variables used to build the explanatory model are classified into two major tiers. Tier 1 represents project specific factors, such as variables related to project characteristics, project location and its distance to major supply sources and price adjustment clauses. Tier 2 represents global and external factors, such as variables related to level of activities in local highway construction market, macroeconomic indicators and energy market conditions. Secondly, it is shown that there is a significant spatial correlation between construction project cost and geographical location of the project that a generalized linear modeling approach may overlook. Geographically weighted regression analysis will be conducted to develop explanatory models for describing variations in the submitted unit price bids considering the spatial correlation. Lastly, the effect of natural disasters on highway construction costs will be examined. Cumulative sum (CUSUM) control chart will be utilized to monitor and detect the change in submitted unit price bids for hurricane-impacted and not hurricane-impacted areas. The primary contributions of this research to the existing body of knowledge are: (1) creation of a multiple regression model to explain variations in submitted unit price bids; (2) creation of local regression models to describe variations in the submitted unit price bids considering the spatial correlation; and (3) empirical assessment of the impact of natural disasters on the variation in the submitted unit price bids. The primary contributions of this research to the state of practice are: (1) enhancing the capability of cost engineers in preparing more-accurate budgets and bids; (2) aiding a bottom-up estimating approach that requires more knowledge about the projects and market; and (3) helping capital project planners set and adjust the timing of the project lettings in the light of market conditions.
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