A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Evaluation of Indirect Cost Estimation in the Egyptian Construction Industry
In construction projects, site overhead costs are an essential component of the contractor's budget as that is what mainly differs a company from another when bidding for a project. The purpose of this research is to reduce the risk, enhance the bid accuracy, and minimize the amount of time and effort in estimating the site overhead costs. This thesis will focus on developing an artificial neural network model that allows a fast, efficient, and accurate estimate for the percentage of site overhead costs for construction projects executed in Egypt to avoid any reduction in the company’s profit or incompletion of the project. This research has three folds; the first fold includes the literature review for the site overhead costs and identifies the significant factors that affect it from previous studies which are project type, duration, location, budget, client type, contract type, company category, extra manpower required, special site requirements, and contractor joint venture. Research articles and publications are included in this section to review the ability of artificial neural networks in cost estimation and other areas related to construction management. Everything in this fold was used as a foundation for the second fold. The second fold is mainly concerned with data collection and analysis through a questionnaire sent to construction companies. Forty projects were collected and analyzed to measure the impact and weight of each factor on the site overhead costs. Finally, the third fold includes developing and testing the model using Neural Designer software by coding the data collected and using it as a database to develop the model. The development process included several models with a multilayer perceptron, different activation functions, and several neurons. The best model was selected based on the lowest RMSE value with 0.2920 and five projects were kept for testing the model and showed a high correlation coefficient of R2=0.888. The selected model was exported to python language and viewed using ...
Evaluation of Indirect Cost Estimation in the Egyptian Construction Industry
In construction projects, site overhead costs are an essential component of the contractor's budget as that is what mainly differs a company from another when bidding for a project. The purpose of this research is to reduce the risk, enhance the bid accuracy, and minimize the amount of time and effort in estimating the site overhead costs. This thesis will focus on developing an artificial neural network model that allows a fast, efficient, and accurate estimate for the percentage of site overhead costs for construction projects executed in Egypt to avoid any reduction in the company’s profit or incompletion of the project. This research has three folds; the first fold includes the literature review for the site overhead costs and identifies the significant factors that affect it from previous studies which are project type, duration, location, budget, client type, contract type, company category, extra manpower required, special site requirements, and contractor joint venture. Research articles and publications are included in this section to review the ability of artificial neural networks in cost estimation and other areas related to construction management. Everything in this fold was used as a foundation for the second fold. The second fold is mainly concerned with data collection and analysis through a questionnaire sent to construction companies. Forty projects were collected and analyzed to measure the impact and weight of each factor on the site overhead costs. Finally, the third fold includes developing and testing the model using Neural Designer software by coding the data collected and using it as a database to develop the model. The development process included several models with a multilayer perceptron, different activation functions, and several neurons. The best model was selected based on the lowest RMSE value with 0.2920 and five projects were kept for testing the model and showed a high correlation coefficient of R2=0.888. The selected model was exported to python language and viewed using ...
Evaluation of Indirect Cost Estimation in the Egyptian Construction Industry
Othman, Sherif (author)
2021-01-01
Miscellaneous
Electronic Resource
English
DDC:
690
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