Abstract
All complex projects take place in environments of great uncertainty. Maintaining a monitoring and control system from the early stages of execution is a critical factor in the success of this type of project. Large hydroelectric power station construction projects are regarded as highly complex because they are affected by factors such as the risks inherent in a variety of fields of engineering, geology and the environment, the long execution times, and the large number of multidisciplinary activities to be carried out in parallel, among others. These types of projects are commonly affected by cost overruns and delays. This work develops a methodology for the monitoring and control of complex construction projects in the hydroelectric sector that enables a periodical calculation of metrics for physical progress, financial progress, and predictions for costs and durations on completion of the project. The verification of the efficiency of this methodology was based on stochastic simulation models applied to real projects in the hydropower sector. The results showed that the proposed methodology improved efficiency compared with existing traditional methodologies. The proposed methodology allows the simultaneous consideration of costs, deadlines, criticality, and risks of the activities of the analyzed projects and also incorporates multicriteria decision techniques to manage the influence of key aspects during the development of the project.
Highlights
The development of complex construction projects around the world has produced statistical evidence that shows the significant cost and duration of execution deviations compared with the planned costs and durations
It is apparent that all of the case studies have a low probability of compliance with the planned costs and durations. This scenario continues with the results obtained by applying the CTCR and Earned Value Management (EVM) methodologies
With regard to cost forecasts on the completion of complex construction projects in the hydropower sector, the study by Urgiles, Claver, and Sebastian [29] shows the efficiency of EVM applied to the case studies from the present work, indicating that the efficiency of the predictions improves as the execution of the projects progresses, to the point that after approximately 60% of the time of the planned duration has passed, the cost forecasts it presents fit the simulated costs with a 100% probability of occurrence
Summary
The development of complex construction projects around the world has produced statistical evidence that shows the significant cost and duration of execution deviations compared with the planned costs and durations. The study presented by San Cristóbal [1] analyzed a sample of 130 projects and indicated that 81.5% ended outside the planned time period. In this context and in the construction of large hydroelectric power stations, various studies have been performed, such as those published by Ansar et al [2], Sovacool et al [3], and Awojobi and Jenkins [4], in which large samples of hydroelectric projects at a global level were analyzed, ranging from 58 to 235 projects. In line with the International Project Management Association (IPMA) classification of a complex project [5], and according to the studies by Ammen and Jacob [6], Kermanshachi, Dao, Shane and
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