Accurate finite element (FE) models play an essential role in the health monitoring of operational bridges. Static structural deflections caused by vehicles, which are used in traditional finite element model updating (FEMU) methods, are often procured from field tests, interrupting the traffic and limiting the test loading scenarios. This study proposes a FEMU method that directly applies the massive, multi-source structural and traffic data in the operation phase to update the FE model, effectively solving the defects above. We use the computer vision-based vehicle load identification technique to accurately locate and weigh vehicle loads and carry out static simulations in the FE model based on the identified vehicle loads. The proposed FEMU objective function is established using indices including dynamic structural characteristics and vehicle-induced static structural responses. The static error index in the objective function integrates the curve shape and extrema difference of theoretic and measured static responses. Finally, we deploy a parallel particle swarm optimization (PSO) algorithm to find the global optimal updated FE model. A continuous scale bridge model is employed in the experimental studies with four typical scenarios. Compared to the results from the initial FE model, the updated FE model provides significantly better results both in dynamic and static aspects in all scenarios, and the static error indexes reduce by 75% on average. The proposed method offers a practical approach to deploying the monitoring data for real-time FEMU, which considers dynamic and static features and provides a basis for damage detection, performance assessment, and management.

<p>Along Binjiang Avenue, the landscape in Shanghai, China, a footbridge is needed to connect the two sides of the Qiantanyoucheng park. In this paper, the "Wing-spread Bridge" is designed and analyzed based on the environment and human requirements. The initial inspiration for this bridge comes from the stress ribbon structure. In the Wing-spread Bridge's structural design, the stress ribbon and arch are combined to reduce both components' horizontal force. Meanwhile, abutment, arch foot, and bridge tower are combined as one tower, and this tower is shaped like a dove according to the surrounding natural conditions. The combination and adjustment of the components make the structure beautiful and competitive in the landscape. Finally, this bridge's FE model is established, and the static is carried out based on it. The results show that the bridge can meet the specification requirements in static aspects.</p>

Estimating the load distribution of a bridge structure enables to evaluate the in-service state and predict the structural responses. This paper develops an iterative strategy to inversely estimate the traffic load distribution of a bridge from limited measurements. The computer vision technologies, including the YOLO network-based object detection and a pixel coordinate-based positioning approach, are used to locate the vehicle positions on the bridge deck and form a prior information vector of the input positions. Then, a generalized Tikhonov regularization method is proposed to estimate the load distribution using the bridge response and prior information. The regularization parameter is determined by the L-curve method. The fusion of computer vision and regularization can improve the load identification accuracy and reduce the overfitting effect. The developed approach is applied to numerical and experimental examples under various load conditions. The load can be accurately identified in all cases, and the full-field responses of the structures can be reconstructed with minor errors.

<p>In this paper, a neural network-based regional bridge condition degradation model establishment method is proposed for the problem of regional bridge network level assessment and management and maintenance strategy optimization. First, a subset of features for bridge condition prediction is extracted from the road network database, and a suitable secondary transcoding technique is selected to accommodate the training of artificial neural networks; then, a cost-sensitive training error is introduced to obtain the optimal bridge degradation model through model selection. To verify the feasibility of the method, a case study of a small road network in the main highway section of Shandong Province was selected to obtain the degradation model of the bridge group in the region, which provides a basis for the future maintenance strategy of the regional road network.</p>

<p>Accurate FE models play an important role in structure health monitoring (SHM). In the traditional static finite element model updating (FEMU) process, loading tests interrupting the traffic are required for obtaining static data, which is inconvenient. This paper proposes a novel static FEMU method based on computer vision technology and WIM system, avoiding the mentioned defects. Firstly, the static response simulation under traffic load is carried out with the computer vision determining the load location and the BIW system deciding the load value. Secondly, signal processing technology extracts the measured static data from the monitoring data. Thirdly, the PSO method is utilized to perform the FEMU. An experiment is designed on a bridge model with an SHM system, and results verify the convenience and accuracy of the proposed method</p>

<p>Wing-spread bridge is an innovative stress-ribbon arch pedestrian bridge expected to be built along Binjiang Avenue, Shanghai, China. Human-induced vibration is an important factor that needs to be considered in the operation period of pedestrian bridges. However, there is a lack of research on this new structure's dynamic characteristics and vibration reduction measures. In this paper, the finite element (FE) model of the Wing-spread Bridge is firstly established, and the modal analysis is conducted based on the FE model. Subsequently, the maximum acceleration of each mode under pedestrian dynamic load is calculated. The result shows that the maximum acceleration of the first- order lateral bending mode exceeds the best comfortable indicator. Finally, two tuned mass dampers (TMD) are designed to be installed at the top of the arches, and the vibration amplitude of the bridge with TMD meets the requirements.</p>

<p>In this paper, a large-span steel tied arch bridge's Bayesian FEMU is carried out based on the ambient vibration data. Firstly, the ERA method is used for modal identification. Then, the benchmark FE model of this bridge is established. Based on the sensitivity analysis, six updating parameters significantly affecting the natural frequency are selected. Subsequently, the objective function of the FEMU is established, and the DRAM algorithm is utilized to simulate the parameter samples conforming to the posterior distribution. Finally, the uncertainty analysis of the updated items is carried out. After FEMU, the results show that the model's frequency uncertainty is reduced, and the theoretical frequencies are highly consistent with the identified frequencies.</p>