Machine learning-based methods in structural reliability analysis: A review

Afshari, Sajad Saraygord; Enayatollahi, Fatemeh; Xu, Xiangyang; Liang, Xun

doi:10.1016/j.ress.2021.108223

Cited by 118 publications

(45 citation statements)

References 221 publications

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“…Reference [36] presents an example of the use of AI to handle the situation when a blade of a wind turbine turns to be unbalanced, indicating the points with faults, suitable to be also useful in predictive maintenance programs. It is the same kind of application found in the aerospace industry more recently, improving the quality assurance in most cases.…”

Section: Ds and Wind Power: Structural Monitoring Applicationmentioning

confidence: 99%

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Application of Data Science in the Sustainable Energy Industry

Marques¹

2022

Uncertainty Modeling: Fundamental Concepts and Models

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The application of Data Science (DS) in engineering fields is presented from a broad approach, taking the basics and historical motivation to apply statistical tools into practical needs, from Health Sciences to structural monitoring, the latter linked to the sustainable energy context, like solar, wind, geothermal and nuclear. Moreover, brief bullets about the DS techniques and models are also covered, most in the Machine Learning branch, but with some indications of Artificial Intelligence applications. Math techniques like Kringing, Support Vector Machines, Cross-Validation, Data Regularization, Surrogate Models, for instance, also make part of the text, where the practical issues are evaluated in a brief technical manner. The chapter covers the structural reliability and its DS application too.

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Section: Ds and Wind Power: Structural Monitoring Applicationmentioning

confidence: 99%

“…The selection of the training points is based on the training selected previously and the associated performance index, leading to a lower computing cost. Reference [37] has a fair literature review of the application of AI in the structural reliability.…”

Section: Ds and Wind Power: Structural Monitoring Applicationmentioning

confidence: 99%

Application of Data Science in the Sustainable Energy Industry

Marques¹

2022

Uncertainty Modeling: Fundamental Concepts and Models

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“…With the growth of computational methods, the application of statistical theories in recognition and prediction of patterns and machine learning (ML) methods were established. Afshari et al have presented a review of ML-based SRA methods in [13]. But recently, DL has quickly developed as a leading technique of ML and captured outstanding attention from scholars worldwide [14].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based methods in structural reliability analysis: a review

Afshari

Zhao

Zhuang

et al. 2023

Meas. Sci. Technol.

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One of the most significant and growing research fields in mechanical and civil engineering is Structural Reliability Analysis (SRA). A reliable and precise SRA usually has to deal with complicated , aand numerically expensive problems. Artificial intelligence-based (AI) nd specifically, Deep learning-based (DL) methods, have been applied to the SRA problems to reduce the computational cost and to improve the accuracy of reliability estimation as well. This article reviews the recent advances in using DL models in SRA problems. The review includes the most common categories of DL-based methods used in SRA. More specifically, the application of supervised methods, unsupervised methods, and hybrid deep learning methods in SRA are explained. In this paper, the supervised methods for SRA are categorized as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN ), Long short-term memory (LSTM), Bidirectional (Bi-LSTM) and Gated recurrent units (GRU). For the unsupervised methods, we have investigated methods such as Generative Adversarial Network (GAN), Autoencoders (AE), Self-Organizing Map (SOM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We have made a comprehensive survey of these methods in SRA. Aiming towards an efficient SRA, deep learning-based methods applied for approximating the limit state function (LSF) with First/Second Order Reliability Methods (FORM/SORM), Monte Carlo simulation (MCS), or MCS with importance sampling (IS). Accordingly, the current paper focuses on the structure of different DL-based models and the applications of each DL method in various SRA problems. This survey helps researchers in mechanical and civil engineering, especially those who are engaged with structural and reliability analysis or dealing with quality assurance problems.

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“…Traditional intelligent diagnosis methods include feature extraction using signal processing methods and fault classification by adopting machine learning (ML), and deep learning (DL) approaches [7], [11]- [15]. Those methods enable computers to solve problems without explicitly programming a specific problem-oriented algorithm and instead learn from data [8], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

et al. 2023

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Prognostics and Health Management (PHM) concerns predicting machines' behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines' state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models' speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach to diagnose rolling bearings, which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM.

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Machine learning-based methods in structural reliability analysis: A review

Cited by 118 publications

References 221 publications

Application of Data Science in the Sustainable Energy Industry

Application of Data Science in the Sustainable Energy Industry

Deep learning-based methods in structural reliability analysis: a review

Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

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