Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete

Gençel, Osman; Kocabaş, Fikret; Gök, Mustafa Sabri; Köksal, Fuat

doi:10.1016/j.conbuildmat.2011.03.040

Cited by 45 publications

(5 citation statements)

References 39 publications

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“…Furthermore, the prediction accuracy of the ordinary statistical methods may not be satisfied without proper algorithmic support. Based on previous experimental results, ANN models were used for predicting the mix proportion of polymer concrete to demonstrate the potential in saving time and costs [27,29]. Moreover, an ANN model with one hidden layer and 11 hidden neurons was utilised for predicting the compressive strength of concrete containing silica fume; 0.9724 of the R 2 value indicating the high prediction accuracy of the ANN model was obtained.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

et al. 2021

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Not only can waste rubber enhance the properties of concrete (e.g., its dynamic damping and abrasion resistance capacity), its rational utilisation can also dramatically reduce environmental pollution and carbon footprint globally. This study is the world’s first to develop a novel machine learning-aided design and prediction of environmentally friendly concrete using waste rubber, which can drive sustainable development of infrastructure systems towards net-zero emission, which saves time and cost. In this study, artificial neuron networks (ANN) have been established to determine the design relationship between various concrete mix composites and their multiple mechanical properties simultaneously. Interestingly, it is found that almost all previous studies on the ANNs could only predict one kind of mechanical property. To enable multiple mechanical property predictions, ANN models with various architectural algorithms, hidden neurons and layers are built and tailored for benchmarking in this study. Comprehensively, all three hundred and fifty-three experimental data sets of rubberised concrete available in the open literature have been collected. In this study, the mechanical properties in focus consist of the compressive strength at day 7 (CS7), the compressive strength at day 28 (CS28), the flexural strength (FS), the tensile strength (TS) and the elastic modulus (EM). The optimal ANN architecture has been identified by customising and benchmarking the algorithms (Levenberg–Marquardt (LM), Bayesian Regularisation (BR) and Scaled Conjugate Gradient (SCG)), hidden layers (1–2) and hidden neurons (1–30). The performance of the optimal ANN architecture has been assessed by employing the mean squared error (MSE) and the coefficient of determination (R2). In addition, the prediction accuracy of the optimal ANN model has ben compared with that of the multiple linear regression (MLR).

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Section: Introductionmentioning

confidence: 99%

Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

et al. 2021

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“…The prediction results matched well with the experimental results. Gencel et al (2011Gencel et al ( , 2013 adopted an artificial neural network and linear regression algorithm to study the abrasion resistance of concrete with different constituents. The results demonstrated that an artificial neural network is more reliable than a conventional linear regression algorithm.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the strength of concrete radiation shielding based on LS-SVM

Ren

Shen

2015

Annals of Nuclear Energy

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“…Determination of the compressive strength of concrete requires preparation, curing, and testing of special specimens [ 5 ]. The early prediction of concrete properties is an important activity, tests measuring properties of hardened concrete like strength and deformation are carried out using number of tests, and some of tests and test categorization are found in the study of Gencel et al [ 6 , 7 ]. Prediction of compressive strength of concrete is an important activity in construction technology.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

Nagwani

Deo

2014

The Scientific World Journal

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Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm.

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Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete

Cited by 45 publications

References 39 publications

Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete

Prediction of the strength of concrete radiation shielding based on LS-SVM

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

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