Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network

Tavakoli, Hamid; Omran, Omid Lotfi; Shiade, Masoud Falahtabar; Kutanaei, Saman Soleimani

doi:10.1590/s1679-78252014001100002

Cited by 46 publications

(4 citation statements)

References 33 publications

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“…In the study of Tavakoli et al (2014) [61], they have predicted the combined effects of nano-silica particles (0 to 6% replacement of cement content) and three fiber types (steel, polypropylene and glass) on the mechanical properties (compressive, tensile and flexural strength) of reinforced selfcompacting concrete (SCC) using ANN. The experimental data was used to train ANN, and two input variables (percentage of nano particles and fiber) and three outputs (flexural tensile strength, tensile strength behavior and compressive strength) were used.…”

Section: Ann Prediction and Modeling Studiesmentioning

confidence: 99%

Modeling and Prediction of Flexural Strength of Hybrid Mesh and Fiber Reinforced Cement-Based Composites Using Artificial Neural Network (Ann)

Sakthivel¹

2016

geomate

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ABSTRACT:In this paper, Artificial Neural Network (ANN) has been used to predict the equivalent flexural strength of hybrid mesh and fiber reinforced cement-based composites (HMFRCBC). Three ANN models (Models 1, 2 and 3) were developed for predicting the flexural strength of cement-based composites. Model 1 used 48 data of the previously published data of the present authors and Model 2 used 48 data (out of the 75 ANN validated data) from previous studies related to mesh reinforced cement-based composites. Model 3 with 98 data (combined data sets of Model 1 and Model 2) employed seven input parameters, namely the width and depth of slab, cylinder compressive strength, mesh ultimate strength, mesh volume fraction, fiber volume fraction, fiber ultimate strength, and an output, the ultimate flexural strength of mesh and fiber reinforced cement based composites. Hidden layer was fixed based on 5 trial runs for Models 1 and 2, and 10 trial runs for Model 3. All the three models (Models 1, 2 and 3) were trained with 80% of the data, and tested with balance 20% of the data. For Models 1, 2 and 3, the Lowest Individual Error (LIE) of 11.18%, 6.95% and 11.56% (respectively) is achieved in Trial Run No.1.4 (with I-H-O, Input-Hidden Neurons-Output of 5-6-1), Trial Run No.2.2 (5-4-1) and Trial Run No.3.7 (7-10-1) respectively. Also, the lowest absolute average deviation (AAD%) of 4.81%, 3.51% and 4.58% (respectively); lowest Root Mean Square Error (RMSE) of 0.86, 0.76 and 0.99 (respectively); and highest R 2 of 0.933, 0.988 and 0.975 (respectively) are seen for these trials 1.4, 2.2, 3.7 in Models 1, 2 and 3 respectively. All the three ANN models were found to be in good agreement with actual results, and these three ANN models can serve as simple but reliable predictive tools in determination of flexural strength of HMFRCBC.

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Section: Ann Prediction and Modeling Studiesmentioning

confidence: 99%

Modeling and Prediction of Flexural Strength of Hybrid Mesh and Fiber Reinforced Cement-Based Composites Using Artificial Neural Network (Ann)

Sakthivel¹

2016

geomate

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“…Similarly, Kim, H. et al [19], Rusch, S et al [20], and Lo Monte et al [21] have concluded that the BS EN 1992-1-1-2004 [22] code is effective in estimating shrinkage for normally vibrated concrete, while Savija, B et al [23], Rao, A. S. et al [24], and Lee, Y. J. et al [25] have shown the same for VMA type SCC. Tavakoli, M et al [26], Zhou, J et al [27], and Tavakoli, M et al [28] have concluded that IS 1343:2012 [29] code is effective in estimating shrinkage for normally vibrated concrete. Similarly, Sivakumar, A et al [30], Gaur, D. R et al [31], Priyanka, P et al [32], and Singh, B [33] have demonstrated that IS 1343:2012 code is effective in predicting shrinkage for VMA type SCC.…”

Section: Introductionmentioning

confidence: 99%

Shrinkage Behaviour of Self-Compacting Concrete with Higher Binder Content: Experimental Results and Predictive Equation

Bhirud,

Vaidya,

et al. 2024

Preprint

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This research breaks new ground in self-compacting concrete (SCC) by exploring the benefits of incorporating more binder. We investigate three concrete mixtures with varying binder content (550, 600, and 650 kg/m 3) to assess their workability, compressive strength, and shrinkage. Our findings reveal a significant increase in strength throughout the concrete's lifespan with higher binder content. Additionally, the mix exhibits enhanced stability and cohesion. The research also explores the cost-effective use of fly ash as a partial binder replacement, reducing the reliance on expensive superplasticizers. However, existing prediction models (IS 1343-2012, BS EN 1992-1-1-2004, and ACI 209 R-92) were found inaccurate for high-binder SCC shrinkage. To address this gap, we propose a novel equation for predicting shrinkage in SCC with increased binder content, based on our experimental data. This innovative approach paves the way for the optimized design and cost-effective use of high-strength SCC in construction projects.

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“…For example, Tavakoli et al [10] have predicted the energy absorption capability of fiberreinforced self-compacting concrete which contains nanosilica particles via an MLP-(multilayer perceptron-) type artificial neural network. Tavakoli et al [11] simultaneously researched the mechanical properties of self-compacting concrete with nanosilica particles and various fibers via the MLP artificial neural network. In addition to that, many scholars utilized the ANN for concrete compressive strength prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Concrete in Wet‐Dry Environment by BP Artificial Neural Networks

Liang

Qian

Chen

et al. 2018

Advances in Materials Science and Engineering

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Engineering structure degradation in the marine environment, especially the tidal zone and splash zone, is serious. The compressive strength of concrete exposed to the wet-dry cycle is investigated in this study. Several significant influencing factors of compressive strength of concrete in the wet-dry environment are selected. Then, the database of compressive strength influencing factors is established from vast literature after a statistical analysis of those data. Backpropagation artificial neural networks (BP-ANNs) are applied to establish a multifactorial model to predict the compressive strength of concrete in the wet-dry exposure environment. Furthermore, experiments are done to verify the generalization of the BP-ANN model. This model turns out to give a high accuracy and statistical analysis to confirm some rules in marine concrete mix and exposure. In general, this model is practical to predict the concrete mechanical performance.

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Prediction of combined effects of fibers and nanosilica on the mechanical properties of self-compacting concrete using artificial neural network

Cited by 46 publications

References 33 publications

Modeling and Prediction of Flexural Strength of Hybrid Mesh and Fiber Reinforced Cement-Based Composites Using Artificial Neural Network (Ann)

Modeling and Prediction of Flexural Strength of Hybrid Mesh and Fiber Reinforced Cement-Based Composites Using Artificial Neural Network (Ann)

Shrinkage Behaviour of Self-Compacting Concrete with Higher Binder Content: Experimental Results and Predictive Equation

Prediction of Compressive Strength of Concrete in Wet‐Dry Environment by BP Artificial Neural Networks

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