Artificial neural networks for mechanical strength prediction of lightweight mortar

S.V, Razavi; Z, Jumaat M.; EI-Shafie, Ahmed H.; Mohammadi, P.

doi:10.5897/sre11.311

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…where X is the measured experimental data, X min and X max are the minimum and maximum values of chosen actual data such as stress (σ), strain (ε), strain rate (ε), and deformation temperature (T), respectively, and X N is the normalized data. The experimental values are normalized between more than 0 and less than 0.95, because in the end points, the transfer functions showed a slow learning rate behavior while training the network model [40]. Likewise, data samples (100%) are randomly partitioned into three sets as training set (70%), validation set (15%) and test set (15%) as listed in Table 2 in order to perform the network training process.…”

Section: Flow Stress Modeling Of Aisi-1045 Steel Using An Ann With Bamentioning

confidence: 99%

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Murugesan

Sajjad

Jung

2019

Metals

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The isothermal tensile test of medium carbon steel material was conducted at deformation temperatures varying from 650 to 950 ∘ C with an interval of 100 ∘ C and strain rates ranging from 0.05 to 1.0 s − 1 . In addition, the scanning electron microscopy (SEM) procedures were exploited to study about the surface morphology of medium carbon steel material. Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For model training and testing purpose, the variables such as deformation temperature, strain rate, and strain data were considered as inputs and the flow stress data were used as targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination ( R 2 ) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.999 and 1.335%, respectively. For improving the model predictability, the constrained nonlinear function based optimization procedures was adopted to obtain the best candidate selections of weights and biases. By evaluating each test conditions, it was found that the average absolute relative error based on the optimized ANN-BP model varied from 0.728% to 1.775%. Overall, the trained ANN-BP models proved to be much more efficient and accurate by means of flow stress prediction against the experimental data for all the tested conditions. These optimized results displayed that an ANN-BP model is more accurate for flow stress prediction than that of the conventional flow stress models.

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Section: Flow Stress Modeling Of Aisi-1045 Steel Using An Ann With Bamentioning

confidence: 99%

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Murugesan

Sajjad

Jung

2019

Metals

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“…There are in scientific literature many applications of intelligent tools relative to mortars most of them based on artificial neural networks. Thus, artificial neural networks were used to predict compressive strength of mortar: for different cement grades (ESKANDARI et al [2]); for mixtures containing different cement strength classes (ESKANDARI-NADDAF and KAZEMI [3]); containing metakaolin (SARIDEMIR [4]); using different saw waste for sand replacement (MAHZUZ et al [5]); for different scoria percentages instead of sand (RAZAVI et al [6]). Artificial neural networks were also used to predict rubberized mortar properties (TOPÇU and SARIDEMIR [7]), model the influence of salt on desorption isotherm and hygral state of cement mortar (KONIORCZYK and WOJCIECHOWSKI [8]), evaluate sand/cement ratio on mortar using ultrasonic transmission inspection (MOLERO et al [9]) and establish a relationship between microstructural characteristics and compressive strength of cement mortar (ONAL and OZTURK [10]).…”

Section: Introductionmentioning

confidence: 99%

Prediction of restrained shrinkage crack width of slag mortar composites using data mining techniques

Martins

Camões²

2019

Matéria (Rio J.)

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The purpose of this study is to develop data mining models to predict restrained shrinkage crack widths of slag mortar cementitious composites. A database published by BILIR et al. [1] was used to develop these models. As a modelling tool R environment was used to apply these data mining (DM) techniques. Several algorithms were tested and analyzed using all the combinations of the input parameters. It was concluded that using one or three input parameters the artificial neural networks (ANN) models have the best performance. Nevertheless, the best forecasting capacity was obtained with the support vector machines (SVM) model using only two input parameters. Furthermore, this model has better predictive capacity than adaptativenetwork-based fuzzy inference system (ANFIS) model developed by BILIR et al. [1] that uses three input parameters.

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Neuro-fuzzy systems in determining light weight concrete strength

Tosee

Nikoo

2019

J. Cent. South Univ.

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Artificial neural networks for mechanical strength prediction of lightweight mortar

Cited by 6 publications

References 32 publications

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Hybrid Machine Learning Optimization Approach to Predict Hot Deformation Behavior of Medium Carbon Steel Material

Prediction of restrained shrinkage crack width of slag mortar composites using data mining techniques

Neuro-fuzzy systems in determining light weight concrete strength

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