Prediction of Concrete Strength Using Neural-Expert System

Gupta, Rajiv; Kewalramani, Manish; Goel, Amit

doi:10.1061/(asce)0899-1561(2006)18:3(462)

Cited by 101 publications

(22 citation statements)

References 8 publications

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“…al. [11] presented a neural-fuzzy inference system for predicting the compressive strength of HPC (Gupta et al 2006). The system parameters included concrete mix-design, specimen size and shape, curing technique and period, and the environmental conditions maximum temperature, relative humidity, and wind velocity.…”

Section: Applications Of Data Mining Techniques In Modeling Concretementioning

confidence: 99%

A robust neural network classifier to model the compressive strength of high performance concrete using feature subset selection

Malagavelli

Kiran

Kiranmai³

2012

Proceedings of the 5th ACM COMPUTE Conference: Intelligent &Amp; Scalable System Technologies

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High performance concrete (HPC) is a mixture of cement, fine aggregate, coarse aggregate, water and other ingredients. Modeling the compressive strength of HPC (or concrete strength) is a difficult task in building materials because it is influenced by the proportions of various ingredients within the HPC. Researchers have tried to confront this difficulty by modeling the strength of concrete using artificial neural networks (ANN). The influence of ingredients on concrete strength still remains unknown due to the "black box" nature of ANN. This paper investigates the influence of ingredients on concrete strength modeling using feature selection. A robust ANN-model using the knowledge from feature selection has been developed to predict the strength effectively. Experimental results have shown that the proposed model is efficient with respect to runtime and prediction.

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Section: Applications Of Data Mining Techniques In Modeling Concretementioning

confidence: 99%

A robust neural network classifier to model the compressive strength of high performance concrete using feature subset selection

Malagavelli

Kiran

Kiranmai³

2012

Proceedings of the 5th ACM COMPUTE Conference: Intelligent &Amp; Scalable System Technologies

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“…Kim et al [97] used the probabilistic neural networks for prediction of concrete strength based on mix proportions. Gupta et al [98] presented a neural-expert system for prediction of concrete strength based on concrete mix design, size and shape of specimen, curing technique and period, among others. Pham and Hadi [99] predicted stress and strain in Fiber Reinforced Polymer (FRP)-con ned square and rectangular columns using ANN.…”

Section: Prediction Applicationsmentioning

confidence: 99%

Neurocomputing in Civil Infrastructure

et al. 2016

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Abstract. This article presents a review of the recent applications of Arti cial Neural Networks (ANN) for civil infrastructure including structural system identi cation, structural health monitoring, structural vibration control, structural design and optimization, prediction applications, construction engineering, and geotechnical engineering. The most common ANN used in structural engineering is the backpropagation neural network followed by recurrent neural networks and radial basis function neural networks. In recent years, a number of researchers have used newer hybrid techniques in structural engineering such as the neuro-fuzzy inference system, time-delayed neuro-fuzzy inference system, and wavelet neural networks. Deep machine learning techniques are among the newest techniques to nd applications in civil infrastructure systems.

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“…Guang and Zong [25] used neural networks to predict 28-day compressive strength of concrete using multilayer feed-forward neural networks. Gupta et al [26] attempted to use ANN for accurate prediction of concrete strength based on the parameters like concrete mix design, size and shape of the specimen, curing technique and period, environmental conditions, etc. Yeh [27] demonstrated the capability of ANN to model the slump of a highly complex material Fly ash and slag concrete (FSC).…”

Section: Concrete Strength Modelingmentioning

confidence: 99%

Applications of Soft Computing in Civil Engineering: A Review

Chandwani¹,

Agrawal²,

Nagar³

2013

IJCA

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The field of engineering is a creative one. The problems encountered in this field are generally unstructured and imprecise influenced by intuitions and past experiences of a designer. The conventional methods of computing relying on analytical or empirical relations become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approximate computer based Soft Computing techniques inspired by the reasoning, intuition, consciousness and wisdom possessed by a human beings are employed. In contrast to conventional computing techniques which rely on exact solutions, soft computing aims at exploiting given tolerance of imprecision, the trivial and uncertain nature of the problem to yield an approximate solution to a problem in quick time. Soft Computing being a multi-disciplinary field uses a variety of statistical, probabilistic and optimization tools which complement each other to produce its three main branches viz., Neural Networks, Genetic Algorithms and Fuzzy Logic. The review paper presents the applications of two major Soft Computing techniques viz., Artificial Neural Networks and Genetic Algorithms in the field of Civil Engineering, which to some extent has replaced the time consuming conventional techniques of computing with intelligent and time saving computing tools.

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Prediction of Concrete Strength Using Neural-Expert System

Cited by 101 publications

References 8 publications

A robust neural network classifier to model the compressive strength of high performance concrete using feature subset selection

A robust neural network classifier to model the compressive strength of high performance concrete using feature subset selection

Neurocomputing in Civil Infrastructure

Applications of Soft Computing in Civil Engineering: A Review

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