Journal ArticleDOI
Predicting the shear strength of reinforced concrete beams using artificial neural networks
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TLDR
In this paper, the authors used ANNs to predict the ultimate shear strength of reinforced concrete (RC) beams with transverse reinforcements, and the results showed that ANNs have strong potential as a feasible tool for predicting the ultimate strength of RC beams with reinforced reinforcement within the range of input parameters considered.About:
This article is published in Engineering Structures.The article was published on 2004-05-01. It has received 222 citations till now. The article focuses on the topics: Shear strength & Compressive strength.read more
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Predicting the compressive strength and slump of high strength concrete using neural network
TL;DR: In this paper, a neural network was used to predict compressive strength and slump of high strength concrete (HSC) using the available test data of 187 different concrete mix-designs of HSC gathered from the literature.
Journal ArticleDOI
Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models
TL;DR: In this paper, several regression, neural networks (NNT) and ANFIS models are constructed, trained and tested to predict the 28-days compressive strength of no-slump concrete (28-CSNSC).
Journal ArticleDOI
Machine learning prediction of mechanical properties of concrete: Critical review
TL;DR: Examination of several Machine Learning models for forecasting the mechanical properties of concrete, including artificial neural networks, support vector machine, decision trees, and evolutionary algorithms are examined.
Journal ArticleDOI
Appraisal of long-term effects of fly ash and silica fume on compressive strength of concrete by neural networks
TL;DR: In this paper, the effects of fly ash and silica fume replacement content on the strength of concrete cured for a long-term period of time by neural networks were investigated.
Journal ArticleDOI
Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete.
TL;DR: Two artificial intelligence approaches, namely adaptive neuro fuzzy inference (ANFIS) and artificial neural network (ANN), were used to predict the compressive strength of GPC, where coarse and fine waste steel slag were used as aggregates.
References
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Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
Book
Introduction To The Theory Of Neural Computation
TL;DR: This book is a detailed, logically-developed treatment that covers the theory and uses of collective computational networks, including associative memory, feed forward networks, and unsupervised learning.
Related Papers (5)
Shear design procedure for reinforced normal and high-strength concrete beams using artificial neural networks. Part II: beams with stirrups
Antoni Cladera,Antonio Marí +1 more