Analysis of durability of high performance concrete using artificial neural networks

Parichatprecha, Rattapoohm; Nimityongskul, Pichai

doi:10.1016/j.conbuildmat.2008.04.015

Cited by 106 publications

(45 citation statements)

References 11 publications

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“…But there is a need to develop a holistic model from the available experimental data of the present authors and also from related studies to predict the strength parameters of HMFRCBC. This is because rational and readymade or easy-to-use equations are not available in design codes to accurately predict the properties of hybrid composites [12]. Even, the numerical modeling methods are static and cannot be generalized well on datasets outside those for which they were designed [13].…”

Section: Steelmentioning

confidence: 99%

“…Parichatprecha and Nimityongskul (2009) [12] have analyzed the influence of the content of water and cement, water-binder ratio, and the replacement of fly ash and silica fume on the durability of high performance concrete For developing ANN models, they have used 86 data from previous studies [58] on high strength concrete. The mean absolute percentage error of predicted test results was found to be 13.88% and the absolute fraction of variance (R 2 ) was 0.9741.…”

Section: Ann Prediction and Modeling Studiesmentioning

confidence: 99%

“…ANN adapts to experimental test results, empirical data or theoretical results [24], tolerates approximate or imprecise data, gets continuously trained (or retrained), solves complex problems and provides accurate predictive solutions [19], [22]- [23], [25]- [27]. ANN learns from historical or previously measured data, captures unknown data better than traditional statistical methods and solves new problems with no prior idea on the nature of these interactions [12], [28].…”

Section: Steelmentioning

confidence: 99%

See 2 more Smart Citations

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: Steelmentioning

confidence: 99%

Section: Ann Prediction and Modeling Studiesmentioning

confidence: 99%

Section: Steelmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Where X i is ith input or output variable X. More details regarding construction of ANN can be found in the quoted Refs [25][26][27][28].…”

Section: Artificial Neural Network (Ann) Modellingmentioning

confidence: 99%

Enhancing Degradation of Low Density Polyethylene Films by Curvularia lunata SG1 Using Particle Swarm Optimization Strategy

Raut

Sharma

et al. 2015

Indian J Microbiol

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In the present study, artificial neural network (ANN) modelling coupled with particle swarm optimization (PSO) algorithm was used to optimize the process variables for enhanced low density polyethylene (LDPE) degradation by Curvularia lunata SG1. In the non-linear ANN model, temperature, pH, contact time and agitation were used as input variables and polyethylene bio-degradation as the output variable. Further, on application of PSO to the ANN model, the optimum values of the process parameters were as follows: pH = 7.6, temperature = 37.97°C, agitation rate = 190.48 rpm and incubation time = 261.95 days. A comparison between the model results and experimental data gave a high correlation coefficient (R 2 ANN ¼ 0:999). Significant enhancement of LDPE bio-degradation using C. lunata SG1by about 48 % was achieved under optimum conditions. Thus, the novelty of the work lies in the application of combination of ANN-PSO as optimization strategy to enhance the bio-degradation of LDPE.Electronic supplementary material The online version of this article

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“…In addition, maintaining a low water to binder ratio with adequate workability makes the design process more complicated (Parichatprecha and Nimityongskul 2009).…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete

Najigivi

Khaloo

zad

et al. 2013

Int J Concr Struct Mater

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In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of SiO 2 particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance.

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Analysis of durability of high performance concrete using artificial neural networks

Cited by 106 publications

References 11 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)

Enhancing Degradation of Low Density Polyethylene Films by Curvularia lunata SG1 Using Particle Swarm Optimization Strategy

An Artificial Neural Networks Model for Predicting Permeability Properties of Nano Silica–Rice Husk Ash Ternary Blended Concrete

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