Damage assessment of smart composite structures via machine learning: a review

Khan, Asif; Kim, Nayeon; Shin, J.K.; Kim, Heung Soo; Youn, Byeng D.

doi:10.1007/s42791-019-0012-2

Cited by 71 publications

(42 citation statements)

References 105 publications

(103 reference statements)

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“…Subsequently, those features were classified using ensemble based methods. In general, the ensemble based methods are extensively used in the area of structural health monitoring and damage assessment of composite structures [351,352]. Zang et al [353] characterized natural fiber reinforced polymer composites for their surface finish using acoustic emission sensors.…”

Section: Ensemble Based Methodsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Section: Ensemble Based Methodsmentioning

confidence: 99%

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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“…In terms of infrastructure damage assessment, the identification and quantification of the surface of catastrophe features are far from an accurate assessment of global damage (Khan et al 2019 ). The analysis of the catastrophe process and even of the scenario reproduction will be very helpful in accurately evaluating the damage state and mechanism of an infrastructure (Shafieezadeh and Burden 2014 ).…”

Section: New Technologies Applied In Idpmimentioning

confidence: 99%

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Dong

2022

Nat Hazards

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Natural hazards, which have the potential to cause catastrophic damage and loss to infrastructure, have increased significantly in recent decades. Thus, the construction demand for disaster prevention and mitigation for infrastructure (DPMI) systems is increasing. Many studies have applied intelligence technologies to solve key aspects of infrastructure, such as design, construction, disaster prevention and mitigation, and rescue and recovery; however, systematic construction is still lacking. Digital twin (DT) is one of the most promising technologies for multi-stage management which has great potential to solve the above challenges. This paper initially puts forward a scientific concept, in which DT drives the construction of intelligent disaster prevention and mitigation for infrastructure (IDPMI) systematically. To begin with, a scientific review of DT and IDPMI is performed, where the development of DT is summarized and a DT-based life cycle of infrastructures is defined. In addition, the intelligence technologies used in disaster management are key reviewed and their relative merits are illustrated. Furthermore, the development and technical feasibility of DT-driven IDPMI are illustrated by reviewing the relevant practice of DT in infrastructure. In conclusion, a scientific framework of DT-IDPMI is programmed, which not only provides some guidance for the deep integration between DT and IDPMI but also identifies the challenges that inspire the professional community to advance these techniques to address them in future research.

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“…Advancements in SHM techniques for composite structures widely favor the methods developed for other structures. Some examples of such methods can be found in [ 9 , 10 , 11 , 12 , 13 ]. Some of these methods are also listed in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Hassani

Mousavi

Gandomi

2021

Sensors

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This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

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Damage assessment of smart composite structures via machine learning: a review

Cited by 71 publications

References 105 publications

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Digital twin-driven intelligence disaster prevention and mitigation for infrastructure: advances, challenges, and opportunities

Structural Health Monitoring in Composite Structures: A Comprehensive Review

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