Fatigue crack detection in welded structural components of steel bridges using artificial neural network

Mashayekhi, Maryam; Santini‐Bell, Erin; Azam, Saeed Eftekhar

doi:10.1007/s13349-021-00488-7

Cited by 14 publications

(9 citation statements)

References 38 publications

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“…Mashayekhi et al [81] implemented ANN for fatigue fracture identification in welded steel bridge structural components. An ANN-based FL evaluation of FSW AA2024-T351 aluminum alloy was created by Masoudi et al [82].…”

Section: Neural Network Based Methodsmentioning

confidence: 99%

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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In the shipbuilding, construction, automotive, and aerospace industries, welding is still a crucial manufacturing process because it can be utilized to create massive, intricate structures with exact dimensional specifications. These kinds of structures are essential for urbanization considering they are used in applications such as tanks, ships, and bridges. However, one of the most important types of structural damage in welding continues to be fatigue. Therefore, it is necessary to take this phenomenon into account when designing and to assess it while a structure is in use. Although traditional methodologies including strain life, linear elastic fracture mechanics, and stress-based procedures are useful for diagnosing fatigue failures, these techniques are typically geometry restricted, require a lot of computing time, are not self-improving, and have limited automation capabilities. Meanwhile, following the conception of machine learning, which can swiftly discover failure trends, cut costs, and time while also paving the way for automation, many damage problems have shown promise in receiving exceptional solutions. This study seeks to provide a thorough overview of how algorithms of machine learning are utilized to forecast the life span of structures joined with welding. It will also go through their drawbacks and advantages. Specifically, the perspectives examined are from the views of the material type, application, welding method, input parameters, and output parameters. It is seen that input parameters such as arc voltage, welding speed, stress intensity factor range, crack growth parameters, stress histories, thickness, and nugget size influence output parameters in the manner of residual stress, number of cycles to failure, impact strength, and stress concentration factors, amongst others. Steel (including high strength steel and stainless steel) accounted for the highest frequency of material usage, while bridges were the most desired area of application. Meanwhile, the predominant taxonomy of machine learning was the random/hybrid-based type. Thus, the selection of the most appropriate and reliable algorithm for any requisite matter in this area could ultimately be determined, opening new research and development opportunities for automation, testing, structural integrity, structural health monitoring, and damage-tolerant design of welded structures.

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Section: Neural Network Based Methodsmentioning

confidence: 99%

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Gbagba,

Maccioni,

Concli

2023

Applied Sciences

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“…[23,[131][132][133][134] After the establishment of the database and the analysis of the IFs, different data-driven algorithms were used to predict the fatigue performance. Specifically, several effectively approaches (Bayesian model, [135][136][137] ANN, [67,129,[138][139][140][141] particle swarm optimization (PSO)-BPNN, [75,142] Ant colony optimization-BPNN, [39,55] Figure 12. Prediction framework of cyclic stress-strain property from microstructure via FEM, two-point correlation, and ML.…”

Section: Progress In Prediction Approachesmentioning

confidence: 99%

“…[ 23,131–134 ] After the establishment of the database and the analysis of the IFs, different data‐driven algorithms were used to predict the fatigue performance. Specifically, several effectively approaches (Bayesian model, [ 135–137 ] ANN, [ 67,129,138–141 ] particle swarm optimization (PSO)‐BPNN, [ 75,142 ] Ant colony optimization‐BPNN, [ 39,55 ] SVM, [ 56,143–145 ] GA‐ANN, [ 146 ] GA‐BPNN, [ 146,147 ] RF, [ 89,137,148,149 ] DNN, [ 112,150–152 ] convolution neural network (CNN), [ 153–155 ] long short‐term memory (LSTM), [ 156–158 ] 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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“…Currently, they are extensively employed in domains such as identification, classification, regression and clustering. Mashayekhi et al [1] developed a platform that uses artificial neural networks to perform data-driven fatigue assessment on steel bridge welding structural components and verified the effectiveness of detecting fatigue damage through one year of health monitoring system data. Huang et al [2] proposed a structural damage recognition method that considers temperature changes based on support vector machines (SVMs) and moth flame optimisation (MFO).…”

Section: Introductionmentioning

confidence: 99%

Indirect identification of bridge damage based on coupled vehicle–bridge vibration and 2D-CNN

Chen,

Zhang,

Wan

et al. 2024

Meas. Sci. Technol.

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This study puts forth a methodology to discern structural damage in bridges that employs two-dimensional convolutional neural network (2D-CNN), which is rooted in the principles of continuous wavelet transform (CWT) theory. The method combines the vehicle–bridge coupled vibration response with deep learning models to extend the application of indirect bridge damage identification methods. To test the proposed method, a spatial vehicle and bridge computational model is established for a three-span continuous beam bridge, and bridge damage is simulated by reducing the stiffness of the unit under different damage conditions. Considering the stochastic nature of road roughness, a self-developed vehicle-bridge coupled vibration analysis program is utilized to acquire the vehicle acceleration response signal and construct the dataset. The 2D-CNN model, with its high sensitivity to two-dimensional data features, is used to extract features from the vehicle vertical acceleration vibration signal. The signal undergoes transformation via CWT, resulting in a 2D grayscale time-frequency image. This image is subsequently utilised as input to construct the 2D-CNN model. Results demonstrate that this method performs well in the identification of bridge structural damage, exhibiting high accuracy in identifying the location and severity of such damage. Thus, a novel avenue is provided for the identification and assessment of bridge structural damage.

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Fatigue crack detection in welded structural components of steel bridges using artificial neural network

Cited by 14 publications

References 38 publications

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures

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

Indirect identification of bridge damage based on coupled vehicle–bridge vibration and 2D-CNN

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