Collecting the information of traffic load, especially heavy trucks, is crucial for bridge statistical analysis, safety evaluation, and maintenance strategies. This paper presents a traffic sensing methodology that combines a deep learning based computer vision technique with the influence line theory. Theoretical background and derivations are introduced from both aspects of structural analysis and computer vision techniques. In addition, to evaluate the effectiveness and accuracy of the proposed traffic sensing method through field tests, a systematic analysis is performed on a continuous box-girder bridge. The obtained results show that the proposed method can automatically identify the vehicle load and speed with promising efficiency and accuracy and most importantly cost-effectiveness. All these features make the proposed methodology a desirable bridge weigh-in-motion system, especially for bridges already equipped with structural health monitoring system.

A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and stability, whereas the cost and construction induced issues are inevitable. A recently proposed traffic sensing methodology, combining computer vision techniques and traditional strain based instrumentation, achieves obvious overall improvement for simple traffic scenarios with less passing vehicles, but are enfaced with obstacles in complicated traffic scenarios. Therefore, a traffic monitoring methodology is proposed in this paper with extra focus on complicated traffic scenarios. Rather than a single sensor, a network of strain sensors of a pre-installed bridge structural health monitoring system is used to collect redundant information and hence improve accuracy of identification results. Field tests were performed on a concrete box-girder bridge to investigate the reliability and accuracy of the method in practice. Key parameters such as vehicle weight, velocity, quantity, type and trajectory are effectively identified according to the test results, in spite of the presence of one-by-one and side-by-side vehicles. The proposed methodology is infrastructure safety oriented and preferable for traffic load monitoring of short and medium span bridges with respect to accuracy and cost-effectiveness.

Mode shapes have been playing a vital role in the research and application of bridge structural health monitoring. This paper presents a novel indirect method identifying bridge mode shapes using dynamic responses of a tractortrailer vehicle model, which consists of one tractor and three instrumented trailers. In an effort to eliminate the road roughness effect, accelerations of adjacent trailers are firstly subtracted. Wavelet analysis is then employed to identify bridge mode shapes from the subtracted accelerations in an iterative manner. Furthermore, wavelet denoising algorithm is adopted to improve the identification accuracy in the presence of measurement noise. Systematic numerical simulations, in which a tractor-trailer model passes over an expressway bridge, are conducted in order to investigate the performance of the proposed method. Sensitivity analysis including vehicle speed, class of road roughness, and noise level are studied in this numerical investigation. Results demonstrate that the proposed method is able to identify bridge modal frequencies and mode shapes with satisfactory resolution, accuracy, and robustness.

Summary Over the last several decades, a lot of bridges have been equipped with the bridge structural health monitoring system, leading to an accumulation of voluminous monitoring data. Since the sensors and associated transmission hardware are subjected to harsh environments, the monitoring data frequently contains various faults, and it is laborious to cleanse the data manually. For the purpose of automatically detecting and classifying faulty monitoring data in large quantities, this paper proposes a novel method that uses the relative frequency distribution histograms (RFDH) of monitoring data as well as the one‐dimensional convolutional neural network (1‐D CNN). The overall procedure of this method can be described as follows: First, RFDHs are constructed from different classes of hour‐long data segments. Second, inverted envelopes of the RFDHs are labeled as the training data to train the 1‐D CNN. Third, a well‐trained 1‐D CNN is used to detect and classify long‐term monitoring data according to their RFDHs of hour‐long data segments. Comprehensive validation of the proposed method is conducted with selective acceleration data collected from two long‐span bridges. The validation yields satisfactory results, demonstrating the accuracy, efficiency, and generality of the method.

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