Automated site-specific assessment of steel structures through integrating machine learning and fracture mechanics

Perry, Brandon J.; Guo, Yanlin; Mahmoud, Hussam

doi:10.1016/j.autcon.2021.104022

Cited by 17 publications

(4 citation statements)

References 49 publications

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“…Generally, the fatigue life of welded joints is the sum of the stages of crack initiation and crack growth, and the FCGR is difficult to accurately describe the complete fatigue cycle. [ 173 ] In addition, the FCGR can be expressed by a certain formula, while the data‐driven method is a blackbox model, which is difficult to interpret the predicted results in detail. Therefore, the researchers turn their attention to the fatigue life prediction of welded joints gradually.…”

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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“…Images of such graphs can be recognized by means of (1) support vectors and (2) clustering the most relevant damage identifiers using the algorithm of the K-nearest neighbors. Work [12] elaborates on analyzing damage in steel structures. Cracks are identified by applying a stress intensity factor.…”

Section: Introductionmentioning

confidence: 99%

Load Identification in Steel Structural Systems Using Machine Learning Elements: Uniform Length Loads and Point Forces

Tusnin,

Alekseytsev,

Tusnina

2024

Buildings

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Actual load identification is a most important task solved in the course of (1) engineering inspections of steel structures, (2) the design of systems rising or restoring the bearing capacity of damaged structural frames, and (3) structural health monitoring. Actual load values are used to determine the stress–strain state (SSS) of a structure and accomplish various engineering objectives. Load identification can involve some uncertainty and require soft computing techniques. Towards this end, the article presents an integrated method combining basic provisions of structural mechanics, machine learning, and artificial neural networks. This method involves decomposing structures into primitives, using machine learning data to make projections, and assembling structures to make final projections for steel frame structures subjected to elastic strain. Final projections serve to identify parameters of point forces and loads distributed along the length of rods. The process of identification means checking the difference between (1) weight coefficient matrices applied to unit loads and (2) actual loads standardized using maximum load values. Cases of neural network training and parameters identification are provided for simple beams. The aim of this research is to enhance the reliability and durability of steel structures by predicting consequences of unfavorable load, including emergency impacts. The novelty of this study lies in the co-use of artificial intelligence elements and structural mechanics methods to predict load parameters using actual displacement curves of structures. This novel approach will enable engineering inspection teams to predict unfavorable load peaks, prevent emergency situations, and identify actual causes of emergencies triggered by excessive loading.

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“…Structural health monitoring (SHM) is evolving and encompassing novel, nondestructive and computational techniques such as machine learning. Using ML provides a safe, reliable, and sustainable assessment of the operational health and working conditions of engineering structures [1,2]. The SHM considers key elements: data curation and management, system identification and analysis, condition assessment and monitoring, and decision making or maintenance [3].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the novel machine learning-based tools provide a data-driven, automated, non-contact, and robust structural health assessment. Therefore, ML-based methods are considered time-saving, cost-effective, and sustainable means for SHM [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Predominant Research Themes in Using Machine Learning in Structural Health Monitoring

Akber,

Zhang

Proceedings of the Fourteenth International Conference on Computational Structures Technology

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Structural health monitoring (SHM) using non-destructive machine learning (ML) based technologies has gained considerable interest in research and industrial communities. Integrating the conventional methods of SHM with novel ML techniques gives robust, sustainable, and promising solutions to SHM. This study presents text mining-based methodology to identify predominant research themes in using ML in SHM. Two analyses are performed on literature data of 375 research studies; (1) co-occurrence analysis of keywords applied on author specified keywords and (2) topic modeling using latent dirichlet allocation (LDA) approach applied on abstracts. The finding shows that the research studies predominantly focus on detecting and classifying structural damages, investigating sensing systems or sensors, and feature extraction and analysis. Moreover, convolutional neural networks and support vector machines are the two mainly used ML algorithms, and bridges, dams, and wind turbines are found as the top three investigated engineering structures. This work can be further extended to include a systematic review of past studies to have an in-depth understanding of using ML in SHM and to find potential contributions and research gaps in the studied area.

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Automated site-specific assessment of steel structures through integrating machine learning and fracture mechanics

Cited by 17 publications

References 49 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

Load Identification in Steel Structural Systems Using Machine Learning Elements: Uniform Length Loads and Point Forces

Predominant Research Themes in Using Machine Learning in Structural Health Monitoring

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