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Corresponding Author

El-Kholy, A.

Subject Area

Civil and Environmental Engineering

Article Type

Original Study

Abstract

This paper presents two models for predicting the delay percentage in water and sewage projects in Egypt. The first model based on regression analysis. 74 causes that lead to delay in water and sewage projects gathered from literature. A questionnaire survey was made on construction contractors of water and sewage projects in Egypt to evaluate the relative important of these causes. 14 causes were obtained as the most significant causes that affect the delay percentage (DP) and these are the independent variables of the proposed model. Data for the occurrence of the previous causes on a yes/no basis and the corresponding DP( dependent variable ) for 20 water and sewage projects was collected . The data was divided into two sets, the first set contains 12 projects for the purpose of model building. The results revealed that there was a strong linear relationship between DP and 9 causes from 14 causes that significantly affect DP projects. The second set contains 8 projects for the validation purpose and comparison with the second model. The second model is a statistical fuzzy approach which is a hybrid approach from fuzzy logic and regression analysis. A regression equation between each cause and DP using projects of first set was extracted. The relative weight of each cause is determined by its coefficient of determination (R2) VALUE. The degree of severity each cause had received from questionnaire analysis was used to fuzzify this cause. A trapezoidal membership function was used to represent he delay percentages in water and sewage projects in general depending on 18 out of the previous 20 projects. Two projects Were excluded from this function due to their divergence values from other projects. Thus, the expected delay percentage of a project is then determined using fuzzy rules. Validation of the two models using projects of the second set revealed that regression model has prediction capabilities higher than that of statistical fuzzy model.

Keywords

Regression Analysis; Questionnaire Survey; Statistical Fuzzy Model Water and Sewage Projects

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