Application of soft computing techniques for intelligent sensor data aggregation in structural health monitoring

Abstract

Recent advances in data gathering and analysis through sensors are opening up new avenues for smart building technology Smart buildings have brought structures and technology together During their lifetime structural buildings are prone to several kinds of damages These damages will lead to deterioration of the entire building Manual inspection is time consuming in structures like bridges which runs for several miles and the labor burdens are extremely more Timely rehabilitation of the buildings is essential to save both human lives and assets Structural Health Monitoring SHM has the capability to monitor and anticipate the events that may affect the health of the buildings The health of the building is periodically monitored or when events like the earthquake fire etc occurs In the proposed research work sensors measure the physical parameters such as temperature corrosion etc which induce cracking in the structural element namely column and transmit this information to the Remote Monitoring System RMS through the Cluster Heads CH for analysis During the data transmission congestion occurrence is avoided by selection of optimal CH using a soft computing approach of Biogeography Based Krill Herd BBKH by considering the distance between the nodes, fairness index of the flows and the buffer occupancy level of CH. From the information collected at the RMS the severity level of the cracks is classified into fine, moderate and severe crack by using the soft computing approach namel Fuzzy Cognitive Map FCM Based on the severity level maintenance is initiated by the structural engineers newline