Computational platform for probabilistic optimum monitoring planning for effective and efficient service life management

Abstract

Over the past decades, significant advances have been accomplished in developing SHM techniques to detect the existing damages in deteriorating structures and maintenance techniques to extend the service life of these structures. The application of SHM can lead to more accurate damage detection. By using the information obtained from SHM, the uncertainties associated with structural performance assessment and prediction can be reduced. If the advanced SHM techniques are optimally integrated in life-cycle management, the efficiency and effectiveness of service life management of deteriorating structures can be maximized. In this paper, a computational platform for optimum monitoring planning based on multi-objective optimization (MOPT) and decision making is presented. The main components integrated in this computational platform are (a) formulation of objectives for optimum monitoring planning; (b) MOPT and decision making for application of the best monitoring plan; and (c) updating the damage propagation and structural performance prediction. The objectives for optimum monitoring planning are formulated considering the availability of monitoring data, damage detection, maintenance, service life and life-cycle cost. Through the MOPT and decision making, the best monitoring plan is determined. The updating process integrates the information obtained from monitoring to improve the accuracy and reduce the uncertainty associated with the damage occurrence and propagation prediction and monitoring planning.