Machine Learning-Based Methods in Structural Reliability Analysis: A Review
TL;DR: A review of the use of ML models in structural reliability analysis can be found in this article, which includes the most common types of ML methods used in SRA, including artificial neural networks (ANN), support vector machines (SVM), Bayesian methods and Kriging estimation with active learning perspective.
About: This article is published in Reliability Engineering & System Safety.The article was published on 2022-03-01 and is currently open access. It has received 37 citations till now. The article focuses on the topics: Reliability (semiconductor) & Machine learning.
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TL;DR: In this article , an adaptive reliability method via physics-informed neural network (PINN) is proposed, which is simulation free as governing partial differential equations are solved via PINN, and an active learning approach adaptively shifts training focus to important regions.
15 citations
TL;DR: Wang et al. as discussed by the authors proposed a Convolutional Kernel Aggregated Domain Adaptation (CKADA) strategy for fault knowledge transfer, where a convolutional kernel aggregated layer with domain-mixed attention weights was first designed to harness the diverse learning capacities of multiple kernels.
12 citations
TL;DR: In this paper , a two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft, is presented.
Abstract: In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available.
8 citations
TL;DR: In this paper , the authors used Monte Carlo simulation with a machine learning-based surrogate model (ML-MCS) to calibrate the reliability of reinforced concrete slab-column joints in a real engineering example.
Abstract: Reinforced concrete slab-column structures, despite their advantages such as architectural flexibility and easy construction, are susceptible to punching shear failure. In addition, punching shear failure is a typical brittle failure, which introduces difficulties in assessing the functionality and failure probability of slab-column structures. Therefore, the prediction of punching shear resistance and corresponding reliability analysis are critical issues in the design of reinforced RC slab-column structures. In order to enhance the computational efficiency of the reliability analysis of reinforced concrete (RC) slab-column joints, a database containing 610 experimental data is used for machine learning (ML) modelling. According to the nonlinear mapping between the selected seven input variables and the punching shear resistance of slab-column joints, four ML models, such as artificial neural network (ANN), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) are established. With the assistance of three performance measures, such as root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), XGBoost is selected as the best prediction model; its RMSE, MAE, and R2 are 32.43, 19.51, and 0.99, respectively. Such advantages are also reflected in the comparison with the five empirical models introduced in this paper. The prediction process of XGBoost is visualized by SHapley Additive exPlanation (SHAP); the importance sorting and feature dependency plots of the input variables explain the prediction process globally. Furthermore, this paper adopts Monte Carlo simulation with a machine learning-based surrogate model (ML-MCS) to calibrate the reliability of slab-column joints in a real engineering example. A total of 1,000,000 samples were obtained through random sampling, and the reliability index β of this practical building was calculated by Monte Carlo simulation. Results demonstrate that the target reliability index requirements under design provisions can be achieved. The sensitivity analysis of stochastic variables was then conducted, and the impact of that analysis on structural reliability was deeply examined.
7 citations
TL;DR: In this article , the punching shear strength of FRP-reinforced concrete slabs was examined and two machine learning (ML) models were developed and proposed to accurately predict the strength compared to the available models.
7 citations
References
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