800 publications from this institution
In recent years, vibration-based structural damage identification has made significant progress by exploiting data-driven deep learning techniques, which can efficiently extract damage-sensitive features from a large amount of data. However, in some practical engineering applications, large volumes of measurement data are not readily available. This paper proposes a novel physics-guided residual neural network (PhyResNet) framework to improve the robustness and accuracy of structural damage identification under data-scarce conditions. In contrast to the state-of-the-art purely data-driven ResNet, the proposed method embedded available physics knowledge (e.g., governing equations of dynamics) of structures into the feature learning process via a novel physics-based loss function. The input-output relationship of the network is constrained to retain its physical meaning implicitly while the demand for large amounts of labeled training data is reduced. Notably, even with only 5 % of the dataset used for training, PhyResNet achieves a 13.1 % improvement in R-Value. The performance of the proposed approach is evaluated through both numerical and experimental verifications. Results demonstrate that damage localization and quantification are achieved with high accuracies and good robustness.
Moving vehicles equipped with various types of sensors can efficiently monitor the health conditions of a population of transportation infrastructure such as bridges. This paper presents a mobile crowdsensing framework to identify dense spatial-resolution bridge mode shapes using sparse drive-by measurements. The proposed method converts mode shape identification into a physical-informed optimization problem with two objective function terms. The first objective minimises the mode shape identification error based on the fact that the ratio of a specific order mode shape value at any two locations is time-invariant. Since the bridge mode shape should be globally smooth even when the local stiffness is discontinuous, the smoothness of the identified mode shape is introduced as the second objective. The feasibility and advantages of the proposed model are verified numerically and through large-scale experimental studies. Numerical results demonstrate that the proposed method can efficiently identify bridge mode shapes with a desirable accuracy. The adverse effects of road roughness and measurement noise on the mode shape identification accuracy are substantially suppressed by introducing crowdsensing and making use of collected responses over multiple trips. The applicability of the proposed method for bridges having varying cross sections and multiple spans is also studied. A series of drive-by tests with different vehicle masses and speeds are conducted on a large-scale footbridge. The experimental results verify that the proposed method can accurately identify the bridge mode shapes and is robust to vehicle mass and speed variation. The identification accuracy of large-scale bridge mode shapes using crowdsensing drive-by measurements is demonstrated in this study.