LSTM approach for condition assessment of suspension bridges based on time-series deflection and temperature data

Wang, Chengwei; Ansari, Farhad; Wu, Bo; Li, Shuangjiang; Morgese, Maurizio; Zhou, Jianting

doi:10.1177/13694332221133604

Cited by 28 publications

(12 citation statements)

References 48 publications

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“…From Figure 8, it can be seen that the optimization ER model is able to fit the assessment of the FP with the state reference value given by the expert very well, with an MSE of only 0.000084155. As shown in Table 2, only the optimized ER model demonstrates the smallest error compared with the traditional ER model, BP neural network [20], and LSTM [21]. From both the above aspects, it demonstrates that the model assesses the health status of the FP more accurately.…”

Section: Experimental Verificationmentioning

confidence: 99%

Aviation fuel pump health state assessment based on evidential reasoning and random forests

Zhang,

Chen,

et al. 2024

Electronics Letters

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As the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

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Section: Experimental Verificationmentioning

confidence: 99%

Aviation fuel pump health state assessment based on evidential reasoning and random forests

Zhang,

Chen,

et al. 2024

Electronics Letters

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“…Many valuable studies have been conducted in the field of applying deep learning algorithms to SHM and pattern recognition [126]. Wang et al [127] used bridge deflections to establish a deep learning-based strategy for structural health monitoring. Damage was determined using a LSTM framework applied to time-series data of deflection and temperature.…”

Section: Shm Of Bridge and Aimentioning

confidence: 99%

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

et al. 2022

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The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

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“…Compared with the temperature effect of cable suspension bridges (Lin et al, 2022; Wang et al, 2022; Xia et al, 2013; Zhou and Sun, 2019a; Zhu et al, 2022), the temperature action of cable-stayed bridges is more complicated for two reasons. First, a large number of stay cables induces high indeterminacy within the bridge.…”

Section: Introductionmentioning

confidence: 99%

Temperature behavior of cable-stayed bridges. Part II — temperature actions by using unified analysis

Shan

Qiang²,

et al. 2023

Advances in Structural Engineering

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The temperature action of long-span cable-stayed bridges is complicated because of the high indeterminacy of their structure. Previous studies on the temperature behavior of bridges were either limited by finite sensors, failing to capture the accurate relation between the temperature field and temperature-induced responses, or constrained to a “divide-and-conquer” strategy, requiring considerable manual intervention and regarded as computationally inefficient. This study develops a unified approach to the investigation of thermal behaviors of cable-stayed bridges by integrating the heat-transfer analysis and structural analysis based on the same refined global 3D finite element model. The companion paper (Part I) investigates the temperature distribution, while this paper (Part II) focuses on temperature-induced responses. The temperature distribution data is automatically converted to thermal loads, and thermal elements are changed to structural elements to calculate the temperature-induced responses of the bridge. Results show that the effect of the temperature variation of cables is nonnegligible and should be taken into account during the structural analysis. The longitudinal displacement of the girder and the longitudinal displacement of the tower top are mainly influenced by the average girder temperature, the mid-span deflection and the cable stress are dominated by the cable temperature and average girder temperature, and the stress of the girder is controlled by the vertical temperature difference. The ratio of the thermal stress to the dead load stress of the girder can reach 96%. The calculated displacement and stress of the bridge agree well with the corresponding measurements, consequently verifying the effectiveness of the proposed unified approach to calculating temperature-induced responses.

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LSTM approach for condition assessment of suspension bridges based on time-series deflection and temperature data

Cited by 28 publications

References 48 publications

Aviation fuel pump health state assessment based on evidential reasoning and random forests

Aviation fuel pump health state assessment based on evidential reasoning and random forests

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

Temperature behavior of cable-stayed bridges. Part II — temperature actions by using unified analysis

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