Structural Reliability Assessment of Steel Four-Bolt Unstiffened Extended End-Plate Connections Using Monte Carlo Simulation and Artificial Neural Networks

Abbasianjahromi, Hamidreza; Shojaeikhah, Somayeh

doi:10.1007/s40996-020-00480-z

Cited by 7 publications

(2 citation statements)

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“…A threshold is Although deep NNs perform well, shallow ones are effective and sufficiently accurate in many fields. They are still prevalent and have been successfully used recently [44][45][46]. The papers published after 2019 are included in this review though one hidden layer is used.…”

Section: Mlp-based Mcsmentioning

confidence: 99%

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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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Section: Mlp-based Mcsmentioning

confidence: 99%

“…One of the key challenges of time-varying SRA is the required computational effort. Recent updates of the LSTM method (such [108,110,114]) and MLP-based methods such as [44] have been proposed to estimate time-varying PoF. The combination of some approaches has also offered good performance for timevarying SRA.…”

Section: Time-varying and Real-time Sramentioning

confidence: 99%