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Journal ArticleDOI

Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams

TLDR
In this paper, the applicability of support vector machine (SVM) based regression to predict fracture characteristics and failure load (P max ) of high strength and ultra high strength concrete beams was examined.
About
This article is published in Engineering Fracture Mechanics.The article was published on 2013-01-01. It has received 54 citations till now. The article focuses on the topics: Support vector machine.

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Citations
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Journal ArticleDOI

A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete

TL;DR: The result indicates that the MFA-ANN hybrid system can obtain a better prediction of the high-performance concrete properties and can provide an efficient and accurate tool to predict and design HPC.
Journal ArticleDOI

Support vector machines in structural engineering: a review

TL;DR: The results showed that SVM is superior to various other learning techniques considering the generalization capability of produced model.
Journal ArticleDOI

A novel support vector regression (SVR) model for the prediction of splice strength of the unconfined beam specimens

TL;DR: In this article, the authors developed a robust model for the prediction of splice strength encompassing significant parameters in a wide range using support vector regression which is for the first time used for this assessment.
Journal ArticleDOI

Modeling of steelmaking process with effective machine learning techniques

TL;DR: Overall, SVR performs best and DENFIS the next best followed by ANN and RF methods respectively, which suggest that the prediction precision given by SVR can meet the requirement for the actual production of steel.
Journal ArticleDOI

Modelling of compressive strength of geopolymer paste, mortar and concrete by optimized support vector machine

TL;DR: It was concluded that ICOA and GA are more suitable to optimize parameters of SVM for predicting compressive strength of the considered geopolymers, while ANN model was remained as one of the simplest approaches which can be used with reasonable accuracy for the problem of this paper.
References
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Book

The Nature of Statistical Learning Theory

TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI

Yielding of steel sheets containing slits

TL;DR: In this article, a relation between extent of plastic yielding and external load applied was investigated, and panels containing internal and edge slits were loaded in tension and lengths of plastic zones were measured.
Book

Nonlinear Programming: Theory and Algorithms

TL;DR: The book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques.

Analysis of crack formation and crack growth in concrete by means of fracture mechanics and finite elements

TL;DR: In this article, fracture mechanics is introduced into finite element analysis by means of a model where stresses are assumed to act across a crack as long as it is narrowly opened, which may be regarded as a way of expressing the energy adsorption in the energy balance approach.
Journal ArticleDOI

Analysis of crack formation and crack growth in concrete by means of fracture mechanics and finite elements

TL;DR: In this article, fracture mechanics is introduced into finite element analysis by means of a model where stresses are assumed to act across a crack as long as it is narrowly opened, which may be regarded as a way of expressing the energy adsorption in the energy balance approach.
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