Contractor prequalification assessment in the construction industry is an essential part of the project development process because contractors play a pivotal role in the extension of projects and resources. The main objective of the present study is comprised prequalification assessment for classifying contractors by applied the EDAS method for recognizing the contractors' potential before competitive tendering and obtaining bids. First, an inclusive, detailed list of 56 sub-factors under 5 main factors for project prequalification was compiled following a thorough literature review, and review of contractors by experts of Bandar Imam Khomeini municipality who already have done projects with contractors. Second, used the CRITIC method for obtained the weighing and importance of each factor. Third, classified the contractors by applied the EDAS system for recognizing the contractors' potential before competitive tendering and obtaining bids. Finally, the prequalification assessment process was developed to obtaining the rank of each contractor and help the stakeholders to select the right contractors. The effectiveness of the present approach was tested by applying it to a case study of the prequalification assessment of four construction companies' in Bandar Imam Khomeini municipality, Khuzestan, Iran. It is worth mentioning that the prequalification assessment by the proposed approach is approved by the project stakeholders and is consistent with their expectations. It can be concluded that based on relevant ranking and weighing of companies that procedure can be extended to the same studies in this regard, and the contribution of the present study is to propose a support system for prequalification and identification of contractors' ability, before assigning projects to companies for success in projects.

The Artificial Neural Networks is now used in many fields. They have become well established as viable, multipurpose, robust computational methodologies with solid theoretic support and with strong potential to be effective in any discipline, especially in construction. The input of the Artificial Neural Network (ANN) characterizes the different realizations of resources. The output is capable of characterizing the objectives and constraints of the optimization, such as attainment of regulatory goals, value of cost functions and time. The supervised learning algorithm of back propagation was used to train the network, once trained, the ANN begins a search through various realizations of pumping patterns to determine whether or not they will be successful. The simple genetic algorithm technique has also been used for optimization of project cost and time. A case study of a project under JNNURM program being executed by K P C Projects Ltd. has been presented in this study. Due to the recent severe global recession, company is facing severe problem and all the construction activities slowed down. To increase the productivity of all resources, it is necessary to forecast the costs arriving from resources so that the total cost of project can be reduced. The present case study deals with the construction of 512 Houses in (G+3) pattern, in 32 blocks located at Karmanghat, Hyderabad. It is observed from the results that the Neural Networks approach has optimized the total project cost by 3.91%, and the duration of the project has been reduced around 5% of the total duration of the project.