A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit

Yang, Jianxi; Yang, Fei; Zhou, Yingxin; Wang, Di; Ren, Li; Wang, Guiping; Chen, Wangqiao

doi:10.1016/j.ins.2021.02.064

Cited by 46 publications

(20 citation statements)

References 26 publications

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“…A traditional CNN structure for classification is shown in Fig 5 [155]. The high capability and efficiency of deep learning networks in adapting to various issues and complexities in SHM of bridges, as well as their ability to function properly in learning from large amounts of data, has led to valuable studies in this field in recent years [156][157][158][159][160][161][162][163][164]. For bridge damage detection, Fernandez-Navamuel et al, (2022) developed a supervised deep learning strategy that incorporates Finite Element models to enhance the training phase of a deep neural network.…”

Section: ) Deep Learningmentioning

confidence: 99%

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

et al. 2022

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In the age of the smart city, things like the Internet of Things (IoT) and big data analytics are making big changes to the way traditional structural health monitoring (SHM) is done. Also, the capacity, flexibility, and robustness of artificial intelligence (AI) techniques for solving complex real-world problems have led to an increasing interest in applying these methods to SHM systems of infrastructures in recent years. Therefore, an analytical evaluation of recent advancements in SHM for infrastructures appears to be important. The bridge is one of the significant transportation infrastructures where existing environmental and destructive variables can have a negative impact on the structure's life and health. The SHM system for bridges in different stages of their life cycle, such as construction, development, management, and maintenance, is seen as a complementary part of intelligent transportation systems (ITS). The main goal of this study is to look at how AI can be used to improve the current state of the art in data-driven SHM systems for bridges, including conceptual frameworks, advantages, and challenges, as well as existing approaches. This article presents an overview of the role of AI in data-driven SHM systems for bridges in the future. Finally, some potential research possibilities in AI-assisted SHM are also emphasized and detailed.

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Section: ) Deep Learningmentioning

confidence: 99%

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

et al. 2022

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“…LSTM neural network can use not only current characteristic information but also intermediate results generated by previous training. LSTM neural network realizes forgetting of invalid information and storage of valid information and solves the problems of gradient explosion and gradient disappearance 35 . The LSTM neural network and unit structures are shown in Figure 2a.…”

Section: Correlation Model Of Deflection Vehicle Load and Temperaturementioning

confidence: 99%

Correlation model of deflection, vehicle load, and temperature for in‐service bridge using deep learning and structural health monitoring

Deng

Zhai

et al. 2022

Structural Contr & Hlth

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Deflection is an important issue in bridge structural health monitoring. An accurate deflection-vehicle load-temperature correlation model is critical to abnormal data identification, deflection prediction under extreme conditions, and bridge structural assessment. However, because of the discrete distribution in time domain of vehicle load and the extreme complexity of the deflectionvehicle load-temperature correlation, the correlation modeling method needs further studies. A novel deflection-vehicle load-temperature correlation modeling method is developed in this study. Based on the concept of deflection influence line (DIL), the raw vehicle load monitoring data are transformed into time-continuous vehicle influence coefficient (VIC). By using gated recurrent unit (GRU) neural network, a correlation model with inputs of VIC and environmental temperature data and output of deflection data is established.Taking a suspension bridge in China as an example, the prediction accuracy of short-, medium-, and long-term correlation models is tested. Moreover, based on the correlation model, a decomposition method of temperature-and vehicle-induced deflection components is proposed. The results show that the predicted deflection of the short-term correlation model is basically consistent with the real-time monitoring data, while the medium-and long-term correlation models have accurate prediction ability for the deflection extreme values in a certain time window. The temperature-and vehicle-induced deflection components separated by using the correlation model are in good agreement with the wavelet decomposition (WD) results, with clear physical meaning and independent of empirical judgment.

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Section: Related Literaturementioning

confidence: 99%

“…A variety of approaches has been proposed for analysing vibration measurements in related applications, e.g., monitoring of bearings [17][18][19] or bridges [20]. In many approaches, time-frequency-based methods such as empirical mode decomposition [17], wavelets [18] and correlation measures [20] were combined with machine learning and artificial intelligence (AI). For example, Kumar et al [19] detected bearing defects by using a sparse cost function and convolutional neural networks and therewith addressed the challenges of a small training dataset.…”

Section: Related Literaturementioning

confidence: 99%

Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades

Loss

Bergmann

2021

Applied Sciences

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Monitoring the structural health of wind turbine blades is essential to increase energy capture and operational safety of turbines, and therewith enhance competitiveness of wind energy. With the current trends of designing blades ever longer, detailed knowledge of the vibrational characteristics at any point along the blade is desirable. In our approach, we monitor vibrations during operation of the turbine by wirelessly measuring accelerations on the outside of the blades. We propose an algorithm to extract so-called vibration-based fingerprints from those measurements, i.e., dominant vibrations such as eigenfrequencies and narrow-band noise. These fingerprints can then be used for subsequent analysis and visualisation, e.g., for comparing fingerprints across several sensor positions and for identifying vibrations as global or local properties. In this study, data were collected by sensors on two test turbines and fingerprints were successfully extracted for vibrations with both low and high operational variability. An analysis of sensors on the same blade indicates that fingerprints deviate for positions at large radial distance or at different blade sides and, hence, an evaluation with larger datasets of sensors at different positions is promising. In addition, the results show that distributed measurements on the blades are needed to gain a detailed understanding of blade vibrations and thereby reduce loads, increase energy harvesting and improve future blade design. In doing so, our method provides a tool for analysing vibrations with relation to environmental and operational variability in a comprehensive manner.

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A data-driven structural damage detection framework based on parallel convolutional neural network and bidirectional gated recurrent unit

Cited by 46 publications

References 26 publications

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

Artificial Intelligence and Structural Health Monitoring of Bridges: A Review of the State-of-the-Art

Correlation model of deflection, vehicle load, and temperature for in‐service bridge using deep learning and structural health monitoring

Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades

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