Developing an Advanced Hybrid System of Structural Health Monitoring

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There is an essential and urgent need in engineering research communities for a reliable, efficient and robust structural health monitoring system to support the monitoring of aging structures like bridges. Many different vibration-based methods have been developed to detect damage in infrastructure, which are broadly categorized as data- and model-based approaches. However, damage detection methods that can account for the complexity of such problems as ambient temperature changes and non-uniform distribution of temperature across the bridges, still need to be developed. There is no best method in terms of accuracy, localization and quantification of damage, and cost efficiency. Most existing research has focused on only one or two tasks, such as detection or detection and localization of damage. A preferred solution to detect, localize, quantify and compensate for environmental effects would be to integrate both data and model-based methods. This would provide quantified damage information to help engineers make appropriate maintenance decisions. These concerns are driving the development of a new system of integrated hybrid damage detection systems that can address the shortcomings of existing methods.******My research program is focused on the development of innovative hybrid approaches to structural damage detection systems that combine data- and model-based methods in order to leverage the inherent strengths of both. The data-based method uses probabilistic/non-probabilistic pattern classifiers or supervised and unsupervised machine learning approaches to classify damage-sensitive features such as natural frequencies and mode shapes as ‘intact' or ‘damaged'. The model-based method uses physics model of the target structure, typically applying a finite element method. By combining the two approaches and sharing measured data from sensors, a hybrid method can be obtained that will provide quantified damage information with early detection and localization in a cost-efficient manner as compared with traditional independent approaches. However, the high possibility of false alarms due to noises, sensor malfunctions and complex environmental effects means that engineers would still have to make on-site visits to confirm that damage has occurred. To address this challenge, a novel computer-vision-based damage detection method will be developed using low resolution cameras (similar to those used in smartphones), advanced image processing techniques and machine learning methods used in the data-based approach to classify images as ‘intact' or ‘damaged'. This damage detection method will ultimately be combined with the new hybrid system. The new hybrid damage detection system with computer vision will supply information that is reliable, accurate and cost-efficient by providing an explicit visual record of structural damage and optimally sharing measured sensor data.