Deep learning algorithms for structural condition identification with limited monitoring data

Abstract

To obtain actual conditions of infrastructure assets and manage them more efficiently, extensive research efforts have been placed on structural health monitoring (SHM), especially those using data-driven methods. Recently, deep learning becomes a research hotspot in many application areas, including the SHM domain. Their performance largely relies on the quality and quantity of the training data, obtained either experimentally or numerically. Due to the time and expense restraints, field or laboratory test data are normally limited by the variation of structural conditions, while the quality of numerical simulation data is subjective to experts' modelling skills. Therefore, the actual performance of deep learning algorithms with limited training data needs to be studied, and the alternative ways to generate more training data need to be developed. In this work, we develop a new one-Dimensional Convolutional Neural Network (1D-CNN) for structural condition identification. A laboratory case study is conducted to evaluate the performance of the algorithm. A steel Warren truss bridge structure is constructed and instrumented with accelerometers and impact hammer. The vibration tests under seven different scenarios are conducted, and each scenario has five repeated test data. The algorithm is trained with different quantities of training data (from one test data to four test data for each scenario). The results show that condition identification results become reliable with at least three repeated test data. To overcome the challenge of limited monitoring data, we propose the potential application of Generative Adversarial Networks (GANs) to generate more reliable training data.