In consideration of the important role that bridges play in transportation system, their safety, durability and serviceability have always been deeply concerned. Recently, many long-span bridges have been instrumented with Structural Health Monitoring Systems (SHMS) to provide bridge engineers with the information needed for decisions-making in management and maintenance. However, efficient use of monitoring data remains a challenge confronted before engineers. Recently, methodologies based on monitoring data while robust to random disturbance and sensitive to damage have received worldwide attention. In this context, this study proposes an innovative damage detection methodology based on structural responses induced by traffic load. First, vehicle-induced strain responses are found to be separable from the strain induced by operational loads, owing to their unique characteristics. This is achieved by a detailed investigation on the relationship between strain measurements and operational loads including temperature, wind as well as vehicles based on long-term monitoring data. From the vehicular load and pertinent strain response, the strain influence line (SIL) can be further identified. As a structural signature, SIL can be used to provide a reasonable assessment of the bridge health condition at least in the vicinity of strain monitoring point. Two damage indexes are therefore derived from the identified SIL, which are promising for damage evaluation because they are: (a) capable of revealing structural deterioration; (b) immune to influences of environmental changes; (c) adaptable to the random characteristic exhibited by long-term monitoring data. Besides, the SIL identification procedure and its theoretical basis are elaborated to respectively handle the case where the vehicle load is available or not, which is also applicable to identify the influence line of other measurements such as stresses. The proposed damage methodology is applied to the cable-stayed bridge spanning the main navigation channel of Shanghai Yangtze River Bridge, and the result shows its effectiveness.

Complicated traffic scenarios, including random change of vehicles' speed and lane, as well as the simultaneous presence of multiple vehicles on bridge, are main obstacles that prevents bridge weigh-in-motion (BWIM) technique from reliable and accurate application. To tackle the complicated traffic problems of BWIM, this paper develops a novel BWIM method which integrates deep-learning-based computer vision technique and bridge influence surface theory. In this study, bridge strains and traffic videos are recorded synchronously as the data source of BWIM. The computer vision technique is employed to detect and track vehicles and corresponding axles from traffic videos so that spatio-temporal paths of vehicle loads on the bridge can be obtained. Then a novel method is proposed to identify the strain influence surface (SIS) of the bridge structure based on the time-synchronized strain signals and vehicle paths. After the SIS is identified, the axle weight (AW) and gross vehicle weight (GVW) can be identified by integrating the SIS, time-synchronized bridge strain, and vehicle paths. For illustration and verification, the proposed method is applied to identify AW and GVW in scale model experiments, in which the vehicle-bridge system is designed with high fidelity, and various complicated traffic scenarios are simulated. Results confirm that the proposed method contributes to improve the existing BWIM technique with respect to complicated traffic scenarios.

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The exposure of bridge health-monitoring systems to extreme conditions often results in missing data, which constrains the health monitoring system from working. Therefore, there is an urgent need for an efficient data cleaning method. With the development of big data and machine-learning techniques, several methods for missing-data recovery have emerged. However, optimization-based methods may experience overfitting and demand extensive tuning of parameters, and trained models may still have substantial errors when applied to unseen datasets. Furthermore, many methods can only process monitoring data from a single sensor at a time, so the spatiotemporal dependence among monitoring data from different sensors cannot be extracted to recover missing data. Monitoring data from multiple sensors can be organized in the form of matrix. Therefore, matrix factorization is an appropriate way to handle monitoring data. To this end, a hierarchical probabilistic model for matrix factorization is formulated under a fully Bayesian framework by incorporating a sparsity-inducing prior over spatiotemporal factors. The spatiotemporal dependence is modeled to reconstruct the monitoring data matrix to achieve the missing-data recovery. Through experiments using continuous monitoring data of an in-service bridge, the proposed method shows good performance of missing-data recovery. Furthermore, the effect of missing data on the preset rank of matrix is also investigated. The results show that the model can achieve higher accuracy of missing-data recovery with higher preset rank under the same case of missing data.

This study presents an optimization flow for structural parameters of orthotropic steel bridge decks (OSDs) based on the fatigue performance and the structural weight. The effects of structural parameters, namely, the thicknesses of the deck plate, the U-rib, the diaphragm, and the longitudinal diaphragm, on equivalent fatigue stress ranges were investigated using surrogate modelling based on the response surface methodology (RSM). Finally, the Pareto optimum front was obtained via the nondominated sorting genetic algorithm II (NSGA-II). The results show that the established surrogate response surface model can predict the local equivalent stress amplitudes well. The deck thickness is the most significant factor for the fatigue performance of the deck to U-rib weld and the U-rib to diaphragm weld. The objectives of minimizing the equivalent stress range and the total structural weight are truly conflicting, and the optimum solutions for structural parameters can be obtained from the Pareto optimum front.