With the development of bridges, independent condition assessment of large-scale bridges has garnered significant attention over the past few decades. Data-driven structural health monitoring (SHM) techniques offer valuable information on the existing health of the structures, maintain safety, and uninterrupted use under varied operational conditions by undertaking timely risk and hazard mitigation. Traditional approaches, however, are not enough to monitor a large amount of SHM data and conduct systematic decision making for future maintenance. In this paper, a bridge health monitoring system is developed through the combination of building information modeling (BIM) and traditional bridge health monitoring that can organize and visualize a considerable amount of sensor data and subsequent structural health information over a prolonged period. The system can identify the structural damage by evaluating the data from sensors using Wigner-Ville distribution (WVD) in bilinear time-frequency analysis. A BIM-enabled platform is utilized to develop the proposed visualization tool for a long-span bridge and enable automated sensor data inventory into the BIM environment. The system has been tested for its robustness and functionality against the development requirements, and the results showed promising potential to support more effective bridge information management.

With the rapid development of sensor technology, structural health monitoring data have tended to become more massive. Deep learning has advantages when handling big data, and has therefore been widely researched for diagnosing structural anomalies. However, for the diagnosis of different structural abnormalities, the model hyperparameters need to be adjusted according to different application scenarios, which is a complicated process. In this paper, a new strategy for building and optimizing 1D-CNN models is proposed that is suitable for diagnosing damage to different types of structure. This strategy involves optimizing hyperparameters with the Bayesian algorithm and improving model recognition accuracy using data fusion technology. Under the condition of sparse sensor measurement points, the entire structure is monitored, and the high-precision diagnosis of structural damage is performed. This method improves the applicability of the model to different structure detection scenarios, and avoids the shortcomings of traditional hyperparameter adjustment methods based on experience and subjectivity. In preliminary research on the simply supported beam test case, the efficient and accurate identification of parameter changes in small local elements was achieved. Furthermore, publicly available structural datasets were utilized to verify the robustness of the method, and a high identification accuracy rate of 99.85% was achieved. Compared with other methods described in the literature, this strategy shows significant advantages in terms of sensor occupancy rate, computational cost, and identification accuracy.

The number of bridges in operation has increased. Along with the increase in the length of time bridges are in service, the structural safety of the bridges also decreases. Bridge substructure is a key component of bridges, but there are few studies on safety management and identification of water bridge substructure damage. Deep learning is a focus of research in the field of target detection, and this document lightens YOLO-v4 to achieve precise and intelligent determination of concrete cracks. This was combined with a point cloud algorithm to provide a three-dimensional estimate of faulty lesions. Finally, the BIM was combined with the method of identifying the underwater structure of the deck. Based on Revit, an integrated management system for underwater bridge structures is put in place. Performing detailed bridge damage management includes (1) 3D visualization of the bridge detail model view, (2) establishment of a bridge damage database, (3) bridge damage management, and (4) management of the comprehensive underwater bridge inspection cycle.

Highway bridges in coastal areas are seriously affected by the marine environment, while most of the existing test methods for bridge-reinforced concrete beams considering both corrosion and fatigue factors are carried out in an alternating manner, which cannot reflect the actual service conditions of the bridge structure. This paper focuses on an experimental study of the coupled influence of reinforcement corrosion and fatigue loading in reinforced concrete T-shaped beams. A novel loading test device that can realize the corrosion–fatigue coupling effect is designed, and then six reinforced concrete T-shaped beams are fabricated and tested. For the corrosion–fatigue coupling test beams, the variation law of beam cracks, failure modes, steel strain development law, load-deflection relationship, and fatigue life are analyzed and compared with that of the simple fatigue test beams. The test results show that the cracks of the test beam develop continuously with the fatigue loading times under the corrosion–fatigue coupling environment. The fatigue failure modes are all brittle fractures of the main steel bars, which present the shape of uneven oblique section tearing. The new testing device and approach can provide direct insights into the interaction of reinforcement corrosion and cyclic loading on the fatigue behavior of T-shaped RC beams, which can be further used to understand the long-term performance of bridge structures under complex marine environments.

In cases with a large number of sensors and complex spatial distribution, correctly learning the spatial characteristics of the sensors is vital for structural damage identification. Graph convolutional neural networks (GCNs), unlike other methods, have the ability to learn the spatial characteristics of the sensors, which is targeted at the above problems in structural damage identification. However, under the influence of environmental interference, sensor instability, and other factors, part of the vibration signal can easily change its fundamental characteristics, and there is a possibility of misjudging structural damage. Therefore, on the basis of building a high-performance graphical convolutional deep learning model, this paper considers the integration of data fusion technology in the model decision-making layer and proposes a single-model decision-making fusion neural network (S_DFNN) model. Through experiments involving the frame model and the self-designed cable-stayed bridge model, it is concluded that this method has a better performance of damage recognition for different structures, and the accuracy is improved based on a single model and has good damage recognition performance. The method has better damage identification performance in different structures, and the accuracy rate is improved based on the single model, which has a very good damage identification effect. It proves that the structural damage diagnosis method proposed in this paper with data fusion technology combined with deep learning has a strong generalization ability and has great potential in structural damage diagnosis.