Innovative Data Fusion Enabled Structural Health Monitoring Approach

Bayesian Probabilistic Approach to Structural Health Monitoring

A Bayesian probabilistic methodology for structural health monitoring is presented. The method uses a sequence of identified modal parameter data sets to compute the probability that continually updated model stiffness parameters are less than a specified fraction of the corresponding initial model stiffness parameters. In this approach, a high likelihood of reduction in model stiffness at a lo...

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...

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...

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...