Deep learning based structural damage identification for the strain field of a subway bolster

Yang, Chengxing; Yang, Liting; Guo, Weinian; Xu, Ping

doi:10.1016/j.aej.2023.09.031

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…While embedding architecture into materials is not new, recent advancements in additive manufacturing technologies have revolutionized the creation of cellular materials with increasingly complex and tailored designs. These technologies allow for precise control over the internal structure and properties of the materials, offering unprecedented possibilities for material engineers (Guo et al, 2021;Kladovasilakis et al, 2022;Yang et al, 2023b).…”

Section: Introductionmentioning

confidence: 99%

“…This selection enables a comprehensive evaluation of surrogate modeling techniques, from straightforward to highly complex, to identify the most effective approach for designing sandwich panels with cellular truss cores exhibiting large phononic band gaps. Our choice aligns with recent advancements in machine learning applications for engineering problems (Yang et al, 2023a;Yang et al, 2023b). These surrogate models were trained using data generated from a parameterized finite element (FE) model developed explicitly for the panels under investigation.…”

Section: Introductionmentioning

confidence: 99%

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Efficient design of sandwich panels with cellular truss cores and large phononic band gaps using surrogate modeling and global optimization

Meruane,

Puiggros,

Fernandez

et al. 2024

Front. Mech. Eng.

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Recent advancements in additive manufacturing technologies and topology optimization techniques have catalyzed a transformative shift in the design of architected materials, enabling increasingly complex and customized configurations. This study delves into the realm of engineered cellular materials, spotlighting their capacity to modulate the propagation of mechanical waves through the strategic creation of phononic band gaps. Focusing on the design of sandwich panels with cellular truss cores, we aim to harness these band gaps to achieve pronounced wave suppression within specific frequency ranges. Our methodology combines surrogate modeling with a comprehensive global optimization strategy, employing three machine learning algorithms—k-Nearest Neighbors (kNN), Random Forest Regression (RFR), and Artificial Neural Networks (ANN)—to construct predictive models from parameterized finite element (FE) analyses. These models, once trained, are integrated with Particle Swarm Optimization (PSO) to refine the panel designs. This approach not only facilitates the discovery of optimal truss core configurations for targeted phononic band gaps but also showcases a marked increase in computational efficiency over traditional optimization methods, particularly in the context of designing for diverse target frequencies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient design of sandwich panels with cellular truss cores and large phononic band gaps using surrogate modeling and global optimization

Meruane,

Puiggros,

Fernandez

et al. 2024

Front. Mech. Eng.

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Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

Zar,

Hussain,

Akbar

et al. 2024

Int J Mech Mater Des

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In this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

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Autonomous data-driven delamination detection in laminated composites with limited and imbalanced data

Azad,

Kim,

Kim

2024

Alexandria Engineering Journal

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Deep learning based structural damage identification for the strain field of a subway bolster

Cited by 5 publications

References 40 publications

Efficient design of sandwich panels with cellular truss cores and large phononic band gaps using surrogate modeling and global optimization

Efficient design of sandwich panels with cellular truss cores and large phononic band gaps using surrogate modeling and global optimization

Towards vibration-based damage detection of civil engineering structures: overview, challenges, and future prospects

Autonomous data-driven delamination detection in laminated composites with limited and imbalanced data

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