Structural monitoring system based on sensitivity analysis and a neural network

A design procedure for a structure's monitoring system using sensitivity analysis and a neural network is developed. The monitoring system is to be used to monitor damage to members critically affecting the overall safety of structures. Recently, many techniques for evaluating the damage of isolated members and models of simple structures have been investigated. However, actual structures are large, and their complex behavior may not be based on damage of isolated members or their simple models. To monitor large structures realistically, structures' data on behavior at many points need to be monitored. Identifying the optimal locations and numbers of these monitoring points and assessing the safety of the entire structure from the limited data constitute the monitoring system design problem. The procedure presented for this design problem is a two-step process. In the first step, using sensitivity analysis and damage-assessment techniques, individual members are ranked according to their influence on the failure probability of the entire structure or according to their effect on the abnormal behavior of the structure. Based on the rank, critical members are identified. In the second step, sensitivity analysis and a neural network are used to determine the optimal locations. In addition, the optimal number of sensors for monitoring damage to the critical members selected in step 1 is also suggested. Truss and frame examples are used to show the validity and applicability of the monitoring system design procedure.