Construction projects are key indicators of a country's national growth and development. In a development project, the largest amount of investment is made during the project implementation phase. One of the main reasons for the loss of financial resources and failure in construction projects is the ...
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Construction projects are key indicators of a country's national growth and development. In a development project, the largest amount of investment is made during the project implementation phase. One of the main reasons for the loss of financial resources and failure in construction projects is the incorrect selection of minor contractors in the projects. The selection of a contractor is one of the key decisions of managers and decision makers. At present, there is no efficient method based on the principles of modern management for selecting a contractor, and no attention is paid to scientific methods and appropriate techniques. It is obvious that there are several quantitative and qualitative indicators in determining the qualifications of contractors, including the ability to do the job, sufficient economic strength, quality, etc., which are important for employers. The purpose of this study is to minimize the losses caused by improper selection of component contractors and to provide a suitable method for selecting component contractors.In this research, after studying books, journals and articles related to important methods and indicators in evaluating and selecting contractors, it is identified and after that, 33 sub-criteria are selected from 6 main criteria. Then, these criteria are provided to the owners of active technicians and experts and engineers with different experiences in the country's development projects using a questionnaire and are examined and analyzed using the first questionnaire in Spss statistical software. Out of 33 criteria, 20 criteria with the highest weight as criteria were selected in the second questionnaire and the network (RBF) is implemented in Matlab environment. Using the high ability of the neural network in prediction, the best criteria for selecting component contractors are prepared in a table, of which the first 10 criteria are selected in terms of score (time reduction and project quality maintenance). Which can be considered as the final results of this research.
Abstract The increasing momentum of the development process has led to the selection of a suitable contractor as an important factor in the success of projects. Due to the considerable quantity of construction budget in countries that are developing, particular attention should be paid to subcontractor ...
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Abstract The increasing momentum of the development process has led to the selection of a suitable contractor as an important factor in the success of projects. Due to the considerable quantity of construction budget in countries that are developing, particular attention should be paid to subcontractor selection to avoid wasting capital, time and reducing quality of work. Subcontractor selection is one of the Basic decisions of managers and decision makers. There is not currently the method of efficient and modern management approach to contractor selection and no attention is paid to scientific methods and techniques. Identifying and evaluating a set of contractor selection criteria will eliminate inefficient contractors from the bidding process. Criteria need to be collected and processed to increase the performance of selected contractors and preparing the needs of the community and the organization's standards. Obviously, there are many qualitative and quantitative indicators to determine the qualifications of contractors, including the ability to do the job, the economic value, the quality, etc. that are important to employers. The purpose of this study is to minimize the losses caused by inappropriate selection of subcontractor and to present a suitable method for selecting subcontractor. In the neural network approach, models for efficient and effective prediction of subcontractor are selected based on the criteria. The most important advantage of radial neural networks over back propagation (BP) conventional networks is the accuracy and speed of approximation in the output of network.
