Browsing by Author "Alemayehu, Shihunegn"

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    Testing Regression Models to Estimate Costs of Road Construction Projects
    (Addis Ababa University, 2014-11) Alemayehu, Shihunegn; Jekale, Wubshet (PhD)
    At the outset of the project, when the scope definitions are in the early stages of development, little information was available, yet there is often a need for some assessment of the potential cost. The owner needs to have a rough or approximate value for the project’s cost for purposes of determining the economic desirability of proceeding with design and construction. Special quick techniques are usually employed, utilizing minimal available information at this point to prepare a conceptual estimate. Little effort is expended to prepare this type of estimate, which often utilizes only a single project parameter, such as square meter of floor area, or span length of a bridge. Using available, historical cost information and applying like parameters, a quick and simple estimate can be prepared. The objective of this study is to develop conceptual and preliminary cost estimating models for asphalt road construction projects using historic data using statistical tools such as spss, and Rsoftware’s, based on sixteen sets of data collected in the Federal Road Projects. As the cost estimates are required at early stages of a project, considerations were given to the fact that the input data for ANNOVA F-test regression analysis to develop the cost models could be easily extracted from sketches or scope definition of the project. As a result in this study Six regression cost estimating models are developed to estimate the total cost of road construction project; among these models two include bid quantities, and four include project size ( i.e. road length and road width) as input variables. The coefficient of determination (r2) for the developed models is ranging from 0.65 to 0.98 which indicate that the predicted values from a forecast models fit with the real-life data. The values of the mean absolute percentage error (MAPE) of the developed regression models are ranging from ±16.3% for preliminary cost estimating and to ±38.9% for conceptual or ball park method of cost estimation, the results compare favorably with past researches which have shown that the estimate accuracy in the early stages of a project is between ±25% for preliminary method of cost estimating that can be related to specific characteristics of known sections or areas of the project and ±50% for conceptual method of cost estimating where early informed guesses made when virtually no drawings exist. The research finding shows how regression models based on the significant variables or bid quantities can be used to develop regression models as tools in forecasting future road construction cost that carry much greater reliability than the previous estimated value. The paper introduces the development of cost estimating techniques and principles from historic data in the archives from both a client and consultants viewpoint both in the early stage of pre-tendering or the planning phase and project-level Keywords: Cost estimating, Regression Model, Early cost estimate