Data‐driven prediction and interpretation of fatigue damage in a road‐rail suspension bridge considering multiple loads

Sun, Zhen; Santos, João; Caetano, Elsa

doi:10.1002/stc.2997

Cited by 27 publications

(4 citation statements)

References 67 publications

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“…Correlation between multiple IFs and fatigue life. [90] www.advancedsciencenews.com www.aem-journal.com SVM, [56,[143][144][145] GA-ANN, [146] GA-BPNN, [146,147] RF, [89,137,148,149] DNN, [112,[150][151][152] convolution neural network (CNN), [153][154][155] long short-term memory (LSTM), [156][157][158] radial basis function neural network (RBFNN), [53,88,159,160] etc.) were developed to realize the fatigue performance prediction of welded joints.…”

Section: Progress In Prediction Approachesmentioning

confidence: 99%

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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Fatigue fracture of welded joints is an important cause for engineering accidents. Due to the coexistence of so many influencing factors, the current prediction model of fatigue behavior cannot be used for all service conditions. The progress of each module (database establishment, data processing, data augmentation, dimension reduction, and comprehensive consideration of influencing factors) during the process of constructing a prediction model is summarized herein. Specifically, the state‐of‐art prediction models of fatigue strength, fatigue crack growth rate, fatigue life, and fatigue reliability are introduced. Moreover, suggestions for future work are given at the end of each section. Finally, according to the influential prediction methods, the establishment of fatigue performance prediction methods of welded joints via data‐driven method is summarized.

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Section: Progress In Prediction Approachesmentioning

confidence: 99%

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

Feng

Zhao

et al. 2023

Adv Eng Mater

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“…Numerous studies have been conducted to use the SVM in SHM of bridges [131][132][133][134][135][136][137]. Li et al, (2019) applied particle swarm optimization-based SVM to classify cable surface defects of cable-stayed bridges.…”

Section: ) Support Vector Machine (Svm)mentioning

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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“…With the increasing emphasis on the capabilities of machine learning (ML) in handling large amounts of data, its application in the field of civil engineering has become more widespread. To effectively utilize and mine information from bridge health monitoring data, data mining methods such as support vector machines (SVMs) [ 10 ], random forest (RF) [ 11 , 12 ], gradient boosting regression trees (GBRTs) [ 13 ], and extreme gradient boosting (XGBoost) [ 14 ] have been employed. The extensively employed RF algorithm, as applied by Sun [ 11 ] for forecasting the cumulative vertical displacement of bridge structures, still presents opportunities for accuracy enhancement.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Impact of Environment Loads on Displacements in a Suspension Bridge with a Data-Driven Approach

Li,

Meng,

et al. 2024

Sensors

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Long-span bridges are susceptible to damage, aging, and deformation in harsh environments for a long time. Therefore, structural health monitoring (SHM) systems need to be used for reasonable monitoring and maintenance. Among various indicators, bridge displacement is a crucial parameter reflecting the bridge’s health condition. Due to the simultaneous bearing of multiple environmental loads on suspension bridges, determining the impact of different loads on displacement is beneficial for the better understanding of the health conditions of the bridges. Considering the fact that extreme gradient boosting (XGBoost) has higher prediction performance and robustness, the authors of this paper have developed a data-driven approach based on the XGBoost model to quantify the impact between different environmental loads and the displacement of a suspension bridge. Simultaneously, this study combined wavelet threshold (WT) denoising and the variational mode decomposition (VMD) method to conduct a modal decomposition of three-dimensional (3D) displacement, further investigating the interrelationships between different loads and bridge displacements. This model links wind speed, temperature, air pressure, and humidity with the 3D displacement response of the span using the bridge monitoring data provided by the GNSS and Earth Observation for Structural Health Monitoring (GeoSHM) system of the Forth Road Bridge (FRB) in the United Kingdom (UK), thus eliminating the temperature time-lag effect on displacement data. The effects of the different loads on the displacement are quantified individually with partial dependence plots (PDPs). Employing testing, it was found that the XGBoost model has a high predictive effect on the target variable of displacement. The analysis of quantification and correlation reveals that lateral displacement is primarily affected by same-direction wind, showing a clear positive correlation, and vertical displacement is mainly influenced by temperature and exhibits a negative correlation. Longitudinal displacement is jointly influenced by various environmental loads, showing a positive correlation with atmospheric pressure, temperature, and vertical wind and a negative correlation with longitudinal wind, lateral wind, and humidity. The results can guide bridge structural health monitoring in extreme weather to avoid accidents.

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Data‐driven prediction and interpretation of fatigue damage in a road‐rail suspension bridge considering multiple loads

Cited by 27 publications

References 67 publications

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

A State‐of‐Art Review on Prediction Model for Fatigue Performance of Welded Joints via Data‐Driven Method

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

Quantifying the Impact of Environment Loads on Displacements in a Suspension Bridge with a Data-Driven Approach

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