Analysis of Structural Health Monitoring Data from Hammersmith Flyover

A case study for application of fuzzy inference and data mining in structural health monitoring

In this study, a system for monitoring the structural health of bridge deck and predicting various possible damages to this section was designed based on measuring the temperature and humidity with the use of wireless sensor networks, and then it was implemented and investigated. A scaled model of a conventional medium sized bridge (length of 50 meters, height of 10 meters, and with 2 piers) wa...

Data Mining for Structural Health Monitoring

Data Mining Technology for Structural Health Monitoring

Data mining (DM) technology provides a potential solution to structural health monitoring (SHM) as a deeper data analysis method. In this paper, a comprehensive introduction to the background, definition, function, process, methods and advantage of DM technology is made. The research and application state of DM technology in various fields is reviewed. Responding to the need of SHM and the char...

Feature Selection in Structural Health Monitoring Big Data Using a Meta-Heuristic Optimization Algorithm

This paper focuses on the processing of structural health monitoring (SHM) big data. Extracted features of a  structure are reduced using an optimization algorithm to find a minimal subset of salient features by removing noisy, irrelevant and redundant data. The PSO-Harmony algorithm is introduced for feature selection to enhance the capability of the proposed method for processing the  measure...

Automation of Data Collection for Pwas-based Structural Health Monitoring

Crack detection through piezoelectric wafer active sensors (PWAS) [1] is emerging as an effective and powerful technique in structural health monitoring (SHM). Because of the piezoelectric properties of the PWAS, they act as both transmitters and receivers of guided Lamb waves for such applications. With arrays of PWAS attached to the structure, excitation signals are sent to one of the PWAS an...