Predicting bridge longitudinal displacement from monitored operational loads with hierarchical CNN for condition assessment

Sun, Zhen; Sun, Mengjin; Siringoringo, Dionysius M.; Dong, You; Lei, Xiaoming

doi:10.1016/j.ymssp.2023.110623

Cited by 9 publications

(3 citation statements)

References 51 publications

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“…Currently, data-driven prediction methods for bridge structural dynamics have mostly focused on Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) [21,22]. Sun et al [23] proposed a hierarchical convolutional neural network (HCNN) model for predicting bridge-bearing displacements using vehicle, wind, and temperature loads as input features. A case study of a cable-stayed bridge with a main span of 510 m and side spans of 215 m verified the effectiveness of this method in predicting bearing displacements, with an accuracy of more than 95.6%.…”

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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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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“…Data analysis research, based on displacement gauges from structural health monitoring (SHM) systems, has been conducted. A variety of methods, including correlation ftting [3,[7][8][9][10][11], mechanical performance analysis of bridge structures [12][13][14][15], Bayesian approaches [16,17], statistical machine learning [18], and data reconstruction [19,20], have been utilized to examine the displacement patterns of BEJs for damage evaluation. Te fndings indicate that the wear damage of BEJs is primarily due to large cumulative displacements, which are signifcantly infuenced by temperature and trafc loading.…”

Section: Introductionmentioning

confidence: 99%

Transformer‐Enhanced Traffic Load Simulation for Wear Evaluation of Bridge Expansion Joint

Dong,

Pan,

Wang

et al. 2024

Structural Control and Health Monitoring

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Timely wear evaluation is crucial in maintaining the functionality of bridge expansion joints (BEJs), ultimately ensuring the safety of bridges. Despite the significance of traffic load simulation (TLS) in simulation-based evaluation methods, existing TLS approaches face challenges in accurately modeling in situ traffic flow at a high fidelity. This paper presents a novel methodology and its application for evaluating the wear performance of BEJs, employing a Transformer-enhanced TLS approach. Initially, a tailored dataset is crafted for data-driven car-following modeling, leveraging an established spatial-temporal traffic load monitoring system. High-fidelity TLS with a mean absolute error (MAE) of 0.1738 m/s is then achieved using Transformer modules equipped with an attention mechanism. To evaluate the final wear life of BEJs, transient dynamic analysis and a calibrated finite element model of the bridge are employed to extract cumulative displacement. Additionally, a surrogate model is developed to depict the relationship between the hourly traffic weight on the entire bridge deck and the cumulative displacement of BEJs, yielding an impressive R-squared value of 0.96619. Comparative results demonstrate the superior performance of our proposed TLS approach over other data-driven approaches, with the linear model derived from our TLS approach outperforming the model generated by the conventional Monte Carlo-based TLS approach. To conclude, our proposed TLS emerges as a comprehensive and precise methodology for the wear evaluation of BEJs.

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“…The dataset collected during the inspection is either processed visually by an inspector or autonomously by a computer algorithm/model. Previous studies have mainly demonstrated image-based inspections using computer vision and deep learning techniques in crack detection, spalls, and corrosion in concrete bridges, bearing displacement, and bolt loosening, but very few on delamination [12,[18][19][20][21][22][23]. Past studies have detected sub-surface delamination using image processing methods and deep convolutional neural networks (DCNN) models on laboratory-prepared specimens.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Infrared Thermography Dataset for Delamination Detection in Reinforced Concrete Bridge Decks

Ichi,

Dorafshan

2024

Applied Sciences

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Structural health monitoring and condition assessment of existing bridge decks is a growing challenge. Conventional manned inspections are costly, labor-intensive, and often risky to execute. Sub-surface delamination, a leading cause of deck replacement, can be autonomously and objectively detected using infrared thermography (IRT) data with developed deep learning AI models to address some of the limitations associated with manned inspection. As one of the most promising classifiers, deep convolutional neural networks (DCNNs) have not been utilized to their fullest potential for delamination detection, arguably due to the scarcity of realistic ground truth datasets. In this study, a common encoder–decoder semantic segmentation-based DCNN is adapted through domain adaptation. The model was tuned and trained on a publicly available dataset to detect subsurface delamination in IRT data collected from in-service bridge decks. The authors investigated the effect of dataset augmentation, class imbalance, the number of classes, and the effect of background removal in the training dataset, resulting in an overall number of seventy-five UNET models. Four out of five bridges were adopted for training and validation, and the fifth bridge was for testing. Most models averaged 80 iterations, and the training progress finally reached a training accuracy of 75% with a loss of about 0.6 without any overfitting. The result showed a substantial difference in the minimum and maximum values for the evaluated performance metrics (0.447 and 0.773 for global accuracy, 0.494 and 0.657 for mean accuracy, 0.239 and 0.716 for precision, 0.243 and 0.558 for true positive rate (TPR), 0.529 and 0.899 for true negative rate (TNR), 0.282 and 0.550 for F1-score. The results also indicated that the models trained on the raw annotated balanced dataset performed best for half of the metrics. In contrast, the models trained on raw data (with no dataset enhancement) performed better when only global accuracy was considered.

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Predicting bridge longitudinal displacement from monitored operational loads with hierarchical CNN for condition assessment

Cited by 9 publications

References 51 publications

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

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

Transformer‐Enhanced Traffic Load Simulation for Wear Evaluation of Bridge Expansion Joint

Evaluation of Infrared Thermography Dataset for Delamination Detection in Reinforced Concrete Bridge Decks

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