عنوان مقاله [English]
Since the proper use of construction machinery in infrastructure projects is so important, it is essential to employ an optimum machinery selection in these projects, because a successful project is usually identified by its ability to be completed on time and within budget in conformance with technical requirements. In order to achieve these objectives, construction managers need
to be equipped with efficient decision-support tools which can help them to improve the distribution of the allocated project resources considering cost, time, and quality while simultaneously minimizing the risks of project failure. In addition, the environmental risks in projects' analysis may play an important role. Complicated as this is, balancing resource allocations and the
risk of project failure becomes even more complicated as the project's resources become more constrained. Advanced Programmatic Risk Analysis and Management Model (APRAM) is one of the recently developed methods which can be
used for risk analysis and management purposes considering schedule, cost and quality risks simultaneously. In this paper, first the APRAM method is modified in order to consider the environmental risks. This method can consider potential risks that might occur over the entire life cycle of the project, including technical and managerial failure risks. Therefore, the model can be used as an efficient decision-support tool for construction managers in machinery selection in infrastructure project where various alternatives might be available, technically. Three possible combinations of excavation machines which are usually used in subway projects are taken into account. All projects' risks related to cost, time, and environment are identified considering the capital costs which should be spent on each combination. Delphi method was applied in order to figure out the failure events and their associated probabilities. Finally, some graphs which can be used for optimization of combined excavating machinery are presented. The results show that it can be employed efficiently by construction managers.