| description abstract | Structural deterioration of buried pipeline due to adverse corrosion effect is among the leading causes of increasing possibility of pipe failure. As a result, maintenance intervention becomes a fundamental task for good engineering management program. However, in recent years, different probabilistic techniques have been used for the evaluation and estimation of cost-effective maintenance strategies of buried pipe based on performance indicators such as failure probability. Whereas the probabilistic approach is used to account for the variabilities associated with the structural parameters, the nonprobabilistic method has been utilized to deal with the limitations of the probabilistic approach, especially for cases in which the data for proper probability evaluation of the design parameters are not adequate. In this study, a new maintenance technique is developed to determine the optimal time for the maintenance of buried pipeline using a combination of fuzzy and subset simulation (probabilistic and nonprobabilistic) approaches for computing pipe reliability and risk, based on α-level cut. The strategy aims at assessing the cost-efficiency required for the determination of the optimal time for maintenance using multiobjective optimization based on the fuzzy-subset annual reliability, risk, and total maintenance cost. The times for essential maintenance schedules are obtained based on a performance indicator (annual reliability or risk), and the optimization is performed using a genetic algorithm. The applicability is demonstrated with a numerical example, and the method provides engineering technicians with the needed tools for the determination of the optimal time interval required to carry out maintenance of the buried pipelines. | |
