Predicition Of Compressive Strength In Light-Weight Self-Compacting Concrete By ANFIS Analytical Model

Vakhshouri, Behnam; Nejadi, Shami

doi:10.1515/ace-2015-0014

Cited by 20 publications

(10 citation statements)

References 28 publications

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“…The results indicated that both models showed excellent performance in strength prediction. Vakhshouri and Nejadi used ANFIS to predict compressive strength of self-compacting lightweight concrete [ 29 ]. In their research, an artificial intelligence was applied as a basic approach to simulate the non-linear and complex behaviour of concrete.…”

Section: Introductionmentioning

confidence: 99%

Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies

et al. 2017

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In this study, the effects of nano-CuO (NC) on engineering properties of fibre-reinforced mortars incorporating metakaolin (MK) were investigated. The effects of polypropylene fibre (PP) were also examined. A total of twenty-six mixtures were prepared. The experimental results were compared with numerical results obtained by adaptive neuro-fuzzy inference system (ANFIS) and Primal Estimated sub-GrAdient Solver for SVM (Pegasos) algorithm. Scanning Electron Microscope (SEM) was also employed to investigate the microstructure of the cement matrix. The mechanical test results showed that both compressive and flexural strengths of cement mortars decreased with the increase of MK content, however the strength values increased significantly with increasing NC content in the mixture. The water absorption of samples decreased remarkably with increasing NC particles in the mixture. When PP fibres were added, the strengths of cement mortars were further enhanced accompanied with lower water absorption values. The addition of 2 wt % and 3 wt % nanoparticles in cement mortar led to a positive contribution to strength and resistance to water absorption. Mixture of PP-MK10NC3 indicated the best results for both compressive and flexural strengths at 28 and 90 days. SEM images illustrated that the morphology of cement matrix became more porous with increasing MK content, but the porosity reduced with the inclusion of NC. In addition, it is evident from the SEM images that more cement hydration products adhered onto the surface of fibres, which would improve the fibre–matrix interface. The numerical results obtained by ANFIS and Pegasos were close to the experimental results. The value of R2 obtained for each data set (validate, test and train) was higher than 0.90 and the values of mean absolute percentage error (MAPE) and the relative root mean squared error (PRMSE) were near zero. The ANFIS and Pegasos models can be used to predict the mechanical properties and water absorptions of fibre-reinforced mortars with MK and NC.

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Section: Introductionmentioning

confidence: 99%

Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies

et al. 2017

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“…The initiation of the damage processes of concrete structural elements take place in the material structure and several models and theories describing these processes are determined [14][15][16][17][18] similarly as for other materials [19][20][21]. They mey represent the first step in analyzing the behavior of the concrete under different conditions [22,23].…”

Section: Conventional Methods To Limit the Influence Of Forced Deformmentioning

confidence: 99%

A Case Study of Pre-Service Cracks in the Concrete Decks of a Two-Level Basement Car Park

Kossakowski

Ślusarczyk

2017

Archives of Civil Engineering

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This paper deals with the prevention of failure of structural elements made of reinforced concrete. It discusses preservice cracks in the concrete decks of an underground parking facility. The cracks were assessed by analyzing their morphology. The results were used to determine the crack causes and the mechanisms of their initiation and growth.Some design solutions to prevent or reduce the occurrence of pre-service cracks are also presented.

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“…Among the prediction models, ANN provides more accurate predictions for concrete mechanical properties [19]. In recent years, mechanical properties and the complex behavior of the concrete have been analyzed with the aid and abilities of the artificial intelligence-based methods [20,21]. Prediction of bending strength of the SLC from the mix ingredients and fresh properties is a particularly complex question.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

Yıldızel¹,

Uzun²,

Çöğürcü³

2019

ijisae

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Many studies have been conducted on the prediction of fiber reinforced concrete strength; however, there are very rare data concerning the prediction of bending strength values of self-leveling glass fiber reinforced concrete. And there is no study for prediction of bending strength of self-leveling glass fiber reinforced concrete from mixture ingredients and slump values. In the present study, relationships between the bending strength and the mixture proportions are explored. An artificial neural network model (ANN) is designed with an extensive experimental data including 395 four-point bending tests, and input parameters as white cement amount, maximum aggregate size, glass fiber content, water cement ratio, superplasticizer and metakaolin content and slump test results. Effect of each parameter on the bending strength is investigated with the developed model. An empirical and user-friendly formula was obtained with the generalization capabilities of the ANN. Results showed that the prediction results are in good agreement with the field data. And these numerical results with high efficiency can make it possible to use the neural design for real-life self-leveling glass fiber reinforced concrete applications.

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Predicition Of Compressive Strength In Light-Weight Self-Compacting Concrete By ANFIS Analytical Model

Cited by 20 publications

References 28 publications

Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies

Effect of Nano-CuO on Engineering and Microstructure Properties of Fibre-Reinforced Mortars Incorporating Metakaolin: Experimental and Numerical Studies

A Case Study of Pre-Service Cracks in the Concrete Decks of a Two-Level Basement Car Park

Prediction of Bending Strength of Self-Leveling Glass Fiber Reinforced Concrete

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