Virtual Monitoring of orthotropic steel deck using bridge weigh-in-motion algorithm: Case study

The identification of influence surfaces (ISs) for bridge structures offers an efficient tool for understanding traffic loads and assessing structural conditions. In general, ISs of a real bridge can be identified through calibration tests using calibration vehicles with known weights moving across the bridge. However, the existing methods face difficulties in considering comprehensive factors, such as the lateral movement, speed variation, and track width of the calibration vehicle, as well as bridge dynamic effects. These factors inevitably introduce inaccuracies into the task of identification. To comprehensively consider these factors, this study proposes a deep learning-based method that combines deep multilayer perceptrons (MLPs) and computer vision (CV), with deep MLP adopted to identify bridge ISs and CV employed to obtain the position coordinates of the calibration vehicle’s wheels. A series of numerical simulations and field experiments on an in-service bridge were carried out to validate the proposed framework and compare it against a broadly established method to such an end—Quilligan’s method. The results show the accuracy, robustness, and practicability of the proposed framework.

Section: Learning Iss Using Mlpsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bridge influence surface identification using a deep multilayer perceptron and computer vision techniques

Jian,

Xia,

Chatzi

et al. 2023

“…Along with the damage detection methods in particular, bridge assessment studies using B-WIM system are also carried out by researchers in the recent decade. 5,[62][63][64][65][66][67][68][69] Since the inception of B-WIM concept, strain responses are being used in the B-WIM algorithm. However, it was found that strain data is less sensitive to damage and shows better performance only when the damage is at the sensor location or within proximity.…”

Section: Introductionmentioning

confidence: 99%

Application of bridge weigh-in-motion system in bridge health monitoring: a state-of-the-art review

Paul

Roy

2023

Bridge health monitoring (BHM) has grown as an essential component of the maintenance paradigm in any transportation network around the world. The mostly used visual inspection method has limitations such as inspector bias, inaccessibility in remote areas, need for human physical presence at the bridge location, etc. On the other hand, vibration-based BHM methods require artificial excitation to the bridge. This motivates researchers to explore potential alternatives for BHM. The bridge weigh-in-motion (B-WIM) system has emerged as a promising alternative with the potential to augment traditional BHM techniques. The advantages of B-WIM systems are durability, portability, easy installation, etc. In addition to weight estimation of passing vehicles, it provides other structural informations that are helpful for bridge assessment. It also overcomes the limitations of pavement-based WIM systems. Most importantly, a B-WIM system uses the same components as a standard BHM system, making BHM a by-product and economical compared to a standalone BHM system. With the development of B-WIM theory, recent researches have revealed the feasibility of B-WIM system for BHM. However, the applicability of the B-WIM system for BHM in any type of bridge and condition is limited. Through the fundamentals of B-WIM theory, this paper reviews the existing bridge response-based BHM methods that involved B-WIM algorithm. The findings are discussed in order to identify the remaining challenges and provide insight to the future aspects.

“…Structural health monitoring (SHM) systems are widely used in civil engineering. [1][2][3][4] SHM systems generally contain various types of sensors to monitor structural response and environmental factors, such as acceleration, displacement, temperature, wind, etc. [5][6][7] However, sensors inevitably produce a large number low-quality or abnormal monitoring data because of the external interferences, usually including power failure, network malfunction, electromagnetic interference, etc.…”

Section: Introductionmentioning

confidence: 99%

Data quality evaluation for bridge structural health monitoring based on deep learning and frequency-domain information

Deng

Zhong

et al. 2022

Abnormal data recognition is of great importance in structural health monitoring. Most of the existing studies focused on detecting obvious abnormal data, which has obvious abnormal time-domain waveform. Pseudo normal data, which is normally looking in time domain but chaotic in frequency domain, did not receive enough attention and were likely to be misclassified as normal data. As a result, structural performance may be incorrectly evaluated because pseudo normal data are not recognized and eliminated from the monitoring data. This study developed a novel quality evaluation framework for monitoring data of bridge dynamic response. The main novelty of the proposed framework is that the frequency-domain information is used to characterize the quality of the monitoring data so that the normal data, obvious abnormal data, and pseudo normal data can be accurately distinguished. In the framework, the frequency-domain information was obtained by fast Fourier transform (FFT), and the Gramian angular field images converted from FFT results were used to train a designed convolutional neural network (CNN). The cable acceleration data of the Waitan cable-stayed bridge were taken as an example to verify the accuracy of the proposed framework. Compared with the CNN models based on time-domain images and time-frequency stacked images, this framework can better recognize pseudo normal data from the monitoring data. In large-scale testing, the classification accuracy of all channels was more than 96%. Finally, two cable acceleration sensors of another cable-stayed bridge were used to demonstrate the feasibility of the framework in cross-object application. The results show that the framework has good accuracy and robustness in large-scale monitoring data quality evaluation and cross-object application.