Machine learning-based bridge cable damage detection under stochastic effects of corrosion and fire

Feng, Jiaxiao; Gao, Kang; Gao, Wei; Liao, Yuchen; Wu, Gang

doi:10.1016/j.engstruct.2022.114421

Cited by 33 publications

(6 citation statements)

References 33 publications

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“…185 The illustrations of SVM for linear and nonlinear classifications are presented in Figure 17(a) and (b) 185 respectively. SVM finds a wide range of applications, such as looseness detection in bolted joints, 129 prediction of mechanical properties of cement mortar, 186 damage detection in frames, 187 fault diagnosis in engines, 188 damage detection in bridges, 189 etc.…”

Section: Overview Of ML Methods For Bolt Looseness Detectionmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

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The integrity of bolted joints is still a challenging problem owing to the gross or localized slip at the interfacial surfaces of the joints when subjected to external disturbances such as vibrations. This slip escalates the interfacial movement and, thus, leads to a decrease in preload levels, that is, looseness of the bolted assemblies. In the last decade, modal analysis, wave propagation, and percussion methods were traditionally used to detect looseness in bolted connections. With an increase in computational power, machine learning algorithms such as neural networks, random forests, decision trees, and support vector machines complemented the traditional methods in accurate looseness estimation. Subsequently, this integration paved the path for real-time health monitoring of bolted joints. This paper summarizes recent investigations on looseness detection in bolted assemblies based on traditional methods and machine learning algorithms. The working principle, advantages, challenges, and applications of the aforementioned methods are also detailed in this paper. Apart from these investigations, the latest studies on the Internet of Things-based health monitoring of structures are also reviewed to explore their adaptability in remote monitoring of bolted connections for damage detection in the future.

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Section: Overview Of ML Methods For Bolt Looseness Detectionmentioning

confidence: 99%

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Chelimilla

Chinthapenta

Kali

et al. 2023

Structural Health Monitoring

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“…Through assimilation and training with historical response data from numerous bridge structures, it is possible to develop predictive models. Methods such as Support Vector Machine (SVM) [16], Random Forests (RFs) [17], and neural networks [18] can be employed to predict the dynamic responses of bridge structures by learning the relationship between input features (such as wind speed, wind direction, and structural geometry) and output responses. In contrast to SVM and RF methods, neural networks excel in capturing intricate nonlinear connections, boasting adaptive learning capabilities that allow them to tailor predictions to diverse bridge structures and operating conditions [19,20].…”

Section: Introductionmentioning

confidence: 99%

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds

Liu,

Meng,

et al. 2024

Sensors

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With the rapid development of big data, the Internet of Things (IoT), and other technological advancements, digital twin (DT) technology is increasingly being applied to the field of bridge structural health monitoring. Achieving the precise implementation of DT relies significantly on a dual-drive approach, combining the influence of both physical model-driven and data-driven methodologies. In this paper, two methods are proposed to predict the displacement and dynamic response of structures under strong winds, namely, a Bayesian Neural Network (BNN) model based on Bayesian inference and a finite element model (FEM) method modified based on genetic algorithms (GAs) and multi-objective optimization (MOO) using response surface methodology (RSM). The characteristics of these approaches in predicting the dynamic response of large-span bridges are explored, and a comparative analysis is conducted to evaluate their differences in computational accuracy, efficiency, model complexity, interpretability, and comprehensiveness. The characteristics of the two methods were evaluated using data collected on the Forth Road Bridge (FRB) as an example under unusual weather conditions with strong wind action. This work proposes a dual-driven approach, integrating machine learning and FEM with GNSS and Earth Observation for Structural Health Monitoring (GeoSHM), to bridge the gap in the limited application of dual-driven methods primarily applied for small- and medium-sized bridges to large-span bridge structures. The research results show that the BNN model achieved higher R2 values for predicting the Y and Z displacements (0.9073 and 0.7969, respectively) compared to the FEM model (0.6167 and 0.6283). The BNN model exhibited significantly faster computation, taking only 20 s, while the FEM model required 5 h. However, the physical model provided higher interpretability and the ability to predict the dynamic response of the entire structure. These findings help to promote the further integration of these two approaches to obtain an accurate and comprehensive dual-driven approach for predicting the structural dynamic response of large-span bridge structures affected by strong wind loading.

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“…Over the past years, machine and deep learning have performed well predicting mechanical properties (Bagherzadeh et al, 2023; Shafighfard et al, 2022) and structural response (Hou et al, 2022). As for bridge real-time response prediction, several algorithms have been successful in predicting and assessing bridge responses, such as auto-regression and moving-average (ARMA) model (Fan et al, 2016; Wang et al, 2013), support machine regression (SVR) (Feng et al, 2022), recurrent neural networks (RNNs) (Zhang et al, nd), and others (Lu et al, 2021; Ren et al, 2022). Among them, RNNs can remarkably simulate the model’s time-dependence and non-linearity, significantly improving the ability to predict long-sequence responses.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based interferometric synthetic aperture radar framework for abnormal displacement deformation prediction of bridges

Feng,

Gao,

et al. 2023

Advances in Structural Engineering

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This paper first proposes a novel framework for combining deep learning approach and Synthetic Aperture Radar (SAR) technique to evaluate and predict the condition of bridge displacement . The Long short-term memory (LSTM) neural network and the Small Baseline Subsets InSAR (SBAS-InSAR) are used to predict the longitudinal deformation of the bridge. Firstly, the proposed framework based on LSTM is established to obtain the relationship between the longitudinal deformation of the bridge and the influence parameters (such as temperature, temporal baseline, and others). In particular, the residual connection (RC) module is adapted to form the residual long short-term memory (Res-LSTM) neural network to improve prediction accuracy and robustness. A case study in Nanjing is analyzed to verify the performance of the proposed framework. The extracted datasets obtained from Sentienl-1 are divided into the training, validation, and testing dataset, which can satisfy the requirements in the engineering field. The R 2 and MSE of the testing datasets utilizing the proposed model are 93.17% and 15.62. Furthermore, the results indicated that the proposed framework based on Res-LSTM could predict and warn the abnormal structural deformation, extend the lifespan of structures, and minimize structural damage due to excessive deformation.

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Machine learning-based bridge cable damage detection under stochastic effects of corrosion and fire

Cited by 33 publications

References 33 publications

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Review on recent advances in structural health monitoring paradigm for looseness detection in bolted assemblies

Application of Response Surface-Corrected Finite Element Model and Bayesian Neural Networks to Predict the Dynamic Response of Forth Road Bridges under Strong Winds

A deep learning-based interferometric synthetic aperture radar framework for abnormal displacement deformation prediction of bridges

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