Journal ArticleDOI
Reliability-based design: Artificial neural networks and double-loop reliability-based optimization approaches
TLDR
Two advanced optimization approaches to solving a reliability-based design problem are presented, one based on the utilization of an artificial neural network and a small-sample simulation technique and the other using a double-loop optimization method based on small- sample simulation.About:
This article is published in Advances in Engineering Software.The article was published on 2017-06-27. It has received 49 citations till now. The article focuses on the topics: First-order reliability method & Probabilistic-based design optimization.read more
Citations
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Journal ArticleDOI
Surrogate-assisted reliability-based design optimization: a survey and a unified modular framework
Maliki Moustapha,Bruno Sudret +1 more
TL;DR: This paper proposes a generalization of the existing surrogate-assisted and simulation-based RBDO techniques using a unified framework that includes three independent blocks, namely adaptive surrogate modelling, reliability analysis, and optimization.
Journal ArticleDOI
Artificial Neural Networks Based Optimization Techniques: A Review
Maher G. M. Abdolrasol,Sajid Hussain,Taha Selim Ustun,Mahidur R. Sarker,Mahammad A. Hannan,Ramizi Mohamed,Jamal Abd Ali,Saad Mekhilef,Abdalrhman Milad +8 more
TL;DR: In this article, an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA), is presented.
Journal ArticleDOI
A robust method for safety evaluation of steel trusses using Gradient Tree Boosting algorithm
TL;DR: The numerical results show that the developed GTB models provide high accurate (more than 90%) regardless of the number of training data and design variable types and have the best performance in most considered cases.
Journal ArticleDOI
Component design optimisation based on artificial intelligence in support of additive manufacturing repair and restoration: Current status and future outlook for remanufacturing
TL;DR: A comprehensive and comparative outline of remanufacturing repair and restoration, using both conventional and automated methods is provided and a future outlook on AI-based optimisation for component design to facilitate Repair and restoration using additive manufacturing is presented.
Journal ArticleDOI
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.
References
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TL;DR: In this paper, two sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies and they are shown to be improvements over simple sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
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