Hypothetical Modeling Of Contractors Bid Markup Estimation For Road Construction Projects In Ethiopia

Markup is a factor that estimators apply to certain work activities or to the total costrnof a bid to cover general overhead and profit. Estimating markup is an important decision forrncontractors as its size has to be low enough to win a contract, but high enough to make arnprofit. Studies which are done on cost estimation practice in Ethiopian construction industryrnagree in the need for a change in the current practice of bid markup estimation. But thesernstudies have a gap in introducing a systematic tool for solving the problem. This researchrnfocuses on identifying and analyzing factors affecting bid markup in road projects andrndeveloping a model which will support local contractors’ decision in estimating bid markuprnsize for road construction projects. The research uses integrated review of various literaturesrnand questionnaire survey as data collection methods. rnTwenty-one factors that are considered to affect bid markup have been identified fromrnliterature review. Based on the results of the analysis, it appears that ‘complexity of project’,rn‘number of competitors with strong desire to win a project’, ‘project location (region)’,rn‘immediate need for work’ and ‘security need of project location’, are the top five rankedrnfactors in terms of influencing bid markup size. rnMultiple linear regression (MLR) and artificial neural network (ANN) were selectedrnfor modeling bid markup estimation. The developed MLR equation contains eleven factorsrnbased on stepwise regression technique. The coefficient of correlation (R) was obtained asrn0.882 with adjusted value of coefficient of determination (Rrn) = 0.745. The overall regressionrnmodel was statistically significant, F (11, 73) = 23.297, p< 0.05. For developing the ANNrnmodel, various network structures were generated and tested. The most satisfactory modelrnwas the ANN8, which consists of 8 neurons in the hidden layer with R, Rrn2rn2rn, MAPE and RMSE rnvalues of 92.06%, 84.75%, 6.43% and 2.47 respectively. Cross validation for both models rnwas done and satisfactory result was obtained. rnStatistical performance indicators shows that the ANN method of modeling predictsrnbid markup better than MLR method. But, the obtained values of the statistical performancernindicators for the two models are closer to each other. Thus, both models can be consideredrnas a satisfactory prediction tools for bid markup and can provide a starting point forrnestimators in a given road project bid markup estimation task.

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