Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.

Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial differential equations as the governing equations are fourth-order nonlinear equations. Here we develop a multi-level physics-informed neural network framework where an aggregation model is developed by combining multiple neural networks, with each one involving only first-order or second-order partial differential equations representing different physics information such as geometrical, constitutive, and equilibrium relations of the structure. The proposed framework demonstrates a remarkable advancement over the classical neural networks in terms of the accuracy and computation time. The proposed method holds the potential to become a promising paradigm for structural mechanics computation and facilitate the intelligent computation of digital twin systems.

The vehicles moving on a bridge excite bridge vibration and can also serve as response receivers because the vehicle dynamic response contains the vibration information of the bridge. A methodology was proposed in a previous study for extracting bridge modal properties, such as natural frequencies and modal shapes, from the vehicle dynamic responses. A specialized test vehicle consisting of a tractor and two following trailers was developed in which the two trailers towed by the tractor moving along the bridge were used as the dynamic response receivers. The responses of one trailer with a time shift were subtracted from the responses of the other to obtain the residual responses that were then processed with fast Fourier transformation (FFT) and short-time Fourier transformation (STFT) to extract the bridge modal properties. In the present paper, field test data of an existing bridge were adopted to verify the proposed methodology. In the vehicle and bridge system, a bridge finite-element (FE) model was updated using the measured accelerations and strains of the bridge; two types of test vehicle models were proposed for use in simulations of the tractor–trailer test system; and the measured surface-roughness profile was used in the numerical simulation. Parametric studies have been conducted to determine the trailer mass and stiffness. Vehicle Model I shows a good capacity for extracting bridge frequencies and the first two modal shapes with a dominant vertical component. However, Vehicle Model II performed better than Vehicle Model I on the extraction of bridge modal shapes that are dominant in lateral bending.