Cost Estimation of Construction Projects under Risk and Cost-Influencing Factors

For the contractor to achieve maximum benefit, it is always necessary to minimize total project cost. Thus, a cost estimation process should be established to estimate the cost of completing the project with the required precision. Furthermore, construction projects are often carried out within a complex and dynamic environment where a number of interconnected factors can lead to uncertainty. To address these complexities, this study introduces a comprehensive approach for estimating construction project costs. Fifteen key risk factors and cost-influencing factors are extracted and identified based on a literature survey and experts' judgment. Different approaches, encompassing artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and regression, are used to estimate the project cost. Data envelopment analysis (DEA) is used to ascertain the most influential factors and risks on the total project cost. Data from 48 construction projects are collected, and DEA is effectively employed to evaluate the factors. The results of DEA and correlation analysis show that eight significant factors affect project cost, including number of project team members, risk of fluctuation in material price, project size, labor productivity, consultant experience, labor availability, and material monopoly. ANN is selected as the preferred method for project cost estimation with the minimum MAPE (Mean Absolute Percentage Error). Sensitivity analysis is performed to demonstrate the applicability of DEA in identifying the influential factors. The results indicate that the proposed ANN model, incorporating eight factors, predicts project costs with an acceptable margin of error.