Journal ArticleDOI
Assessment of load carrying capacity of castellated steel beams by neural networks
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TLDR
In this paper, the performance of the nonlinear finite element (FE) method to evaluate the load carrying capacity and failure mode of simply supported castellated steel beams is discussed. And the numerical results indicate that the best accuracy associates with the adaptive neuro-fuzzy inference system and the neural network models provide better accuracy than the proposed equations.About:
This article is published in Journal of Constructional Steel Research.The article was published on 2011-05-01. It has received 77 citations till now. The article focuses on the topics: Adaptive neuro fuzzy inference system & Buckling.read more
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Journal ArticleDOI
Machine learning for structural engineering: A state-of-the-art review
TL;DR: An overview of ML techniques for structural engineering is presented in this article with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets.
Journal ArticleDOI
Load-carrying capacity of locally corroded steel plate girder ends using artificial neural network
Sajjad Tohidi,Yasser Sharifi +1 more
TL;DR: In this paper, a three-layer Back-Propagation Neural Network (BPNN) has been developed to predict the residual buckling strength of damaged members of a bridge.
Journal ArticleDOI
Deep learning-based axial capacity prediction for cold-formed steel channel sections using Deep Belief Network
TL;DR: A deep learning-based axial capacity prediction for cold-formed steel channel sections is developed using Deep Belief Network and it was found that the DBN was conservative by 9%, 6% and 8% for stub columns, intermediate columns, and slender columns, respectively.
Journal ArticleDOI
Neural networks for inelastic distortional buckling capacity assessment of steel I-beams
Sajjad Tohidi,Yasser Sharifi +1 more
TL;DR: In this paper, an Artificial Neural Network (ANN) model is developed as a reliable modeling method for simulating and predicting the ultimate moment capacities for intermediate doubly-symmetric steel I-beams.
Journal ArticleDOI
Deep learning-based procedure for structural design of cold-formed steel channel sections with edge-stiffened and un-stiffened holes under axial compression
TL;DR: Based on DBN prediction data, a comprehensive reliability analysis was conducted, which shows the proposed equations can predict the enhanced and reduced axial capacity of CFS channel sections with edge-Stiffened/un-stiffened web holes accurately.
References
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Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
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ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
Book
Neural network design
TL;DR: This book, by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules, as well as methods for training them and their applications to practical problems.