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Parametric Cost Estimation Of Sewer Infrastructure Projects With Neural NetworksMohamed Marzouk1 , Lamia Korany2 and Magdy Omar3 Associate Professor, CEM Program, Nile University, Egypt, E-mail: [email protected] 2 Graduate Student, CEM Program, Nile University, Egypt, E-mail: [email protected] 3 Graduate Student, CEM Program, Nile University, Egypt, E-mail:[email protected]

Conceptual cost estimates play a crucial role in initial project decisions, although scope is not finalized and very limited design information is available during early project stages. The estimate provides significant information for project evaluations, engineering designs, cost budgeting and cost management. This study proposes a neural network (NN) approach to develop a parametric cost-estimating model for effectively predict sewer pipe line projects construction cost. The various cost drivers of sewer projects have been identified. Ten factors have been identified as important ones that influence the cost of sewer projects. A data set of twenty six sewer projects were collected to train and to test the neural network. The Results demonstrate that the model is able to replicate past sewer construction cost trends in Egypt with reasonable accuracy for the conceptual project stage. The network average absolute error is 21% and the average accuracy is 78%. Once the artificial neural network was built and tested, the influence of each input parameters to output variable was evaluated. This serves as a feedback, indicating which input channel has a significant effect. It is a method for extracting the cause and effect relationship between the inputs and outputs of the network. REFERENCES Mohamed Bouabaz and Mounir Hamami, A Cost Estimation Model for Repair Bridges Based on Artificial Neural Network, 2008 Chester G. Wilmot, M.ASCE, and Bing Mei, Neural Network Modeling of Highway Construction Costs, 2005. Dr. Kan Phaobunjong and Dr. Calin M. Popescu, Parametric Cost Estimating Model for Buildings, 2003.


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