Hypothetical Modeling of Contractor’s Bid Markup Estimation for Road Construction Projects in Ethiopia
| dc.contributor.advisor | Abraham, Assefa (PhD) | |
| dc.contributor.author | Ameneshewa, Alemu | |
| dc.date.accessioned | 2020-07-06T06:18:00Z | |
| dc.date.accessioned | 2023-11-11T08:29:30Z | |
| dc.date.available | 2020-07-06T06:18:00Z | |
| dc.date.available | 2023-11-11T08:29:30Z | |
| dc.date.issued | 2020-03 | |
| dc.description.abstract | Markup is a factor that estimators apply to certain work activities or to the total cost of a bid to cover general overhead and profit. Estimating markup is an important decision for contractors as its size has to be low enough to win a contract, but high enough to make a profit. Studies which are done on cost estimation practice in Ethiopian construction industry agree in the need for a change in the current practice of bid markup estimation. But these studies have a gap in introducing a systematic tool for solving the problem. This research focuses on identifying and analyzing factors affecting bid markup in road projects and developing a model which will support local contractors’ decision in estimating bid markup size for road construction projects. The research uses integrated review of various literatures and questionnaire survey as data collection methods. Twenty-one factors that are considered to affect bid markup have been identified from literature review. Based on the results of the analysis, it appears that ‘complexity of project’, ‘number of competitors with strong desire to win a project’, ‘project location (region)’, ‘immediate need for work’ and ‘security need of project location’, are the top five ranked factors in terms of influencing bid markup size. Multiple linear regression (MLR) and artificial neural network (ANN) were selected for modeling bid markup estimation. The developed MLR equation contains eleven factors based on stepwise regression technique. The coefficient of correlation (R) was obtained as 0.882 with adjusted value of coefficient of determination (R ) = 0.745. The overall regression model was statistically significant, F (11, 73) = 23.297, p< 0.05. For developing the ANN model, various network structures were generated and tested. The most satisfactory model was the ANN8, which consists of 8 neurons in the hidden layer with R, R 2 2 , MAPE and RMSE values of 92.06%, 84.75%, 6.43% and 2.47 respectively. Cross validation for both models was done and satisfactory result was obtained. Statistical performance indicators shows that the ANN method of modeling predicts bid markup better than MLR method. But, the obtained values of the statistical performance indicators for the two models are closer to each other. Thus, both models can be considered as a satisfactory prediction tools for bid markup and can provide a starting point for estimators in a given road project bid markup estimation task. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/21885 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Markup | en_US |
| dc.subject | Multiple linear regression | en_US |
| dc.subject | Artificial neural network | en_US |
| dc.title | Hypothetical Modeling of Contractor’s Bid Markup Estimation for Road Construction Projects in Ethiopia | en_US |
| dc.type | Thesis | en_US |