The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting time of the signal, the resolution of images, and the number of images. 2) The second step is to study damage prediction with four different stress levels. The FE model is used to simulate structural performance with different delamination conditions. Moreover, except for field test results, the artificial delamination model is created. We performed numerous finite element (FE) simulation to create inputs for CNN for damage detection. The result shows improved NDT results, and CNN can achieve structural performance prediction. We performed six tasks based on these objectives: Task 1. literature review; Task 2. Collect data from bridges; Task 3. perform filed test NDT results; Task 4. Develop FE model based on field test results; Task 5. development of a machine learning model for damage prediction.
Ham, S., Romanoschi, S., Wu, Y., Kumar David, D., & Kang, S. (2022). Performance monitoring leveraging advanced AI technique with CNN. Retrieved from https://repository.lsu.edu/transet_data/142