Artificial neural network for predicting drying shrinkage of concrete

Bal, Lyes; Buyle-Bodin, François

doi:10.1016/j.conbuildmat.2012.08.043

Cited by 125 publications

(54 citation statements)

References 10 publications

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“…ANN is commonly used to estimate quality criteria as it does not require complex mathematical model based on industrial applications, it has opportunity for easy use and has interface which provides facility of use to user in computer programming languages [28][29][30][31][32][33][34][35][36][37][38][39][40]. Contrary to other expert systems, operators who work in companies can use models based on artificial neural nets without requiring complex mathematical formulations and applications.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

“…Also, ANN's applied to many civil engineering applications such as drying shrinkage [28], ready mixed concrete delivery [29], slump model [30], concrete durability [31], mechanical behavior of concrete at high temperatures [32][33][34], workability of concrete with metakaolin and fly ash [35,36], and the long term effect of fly ash and silica fume on compressive strength [37], predicting comprehensive strength and slump for high strength concrete (HSC) [38], drying shrinkage of concrete [39], estimation of compressive strength of self-compacting concrete [40]. This paper argues a new model to predict the optimal mixture dosages of SRMC.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems

Şimşek¹,

İç²,

Şimşek³

2016

IJCIS

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In this study, a fuzzy multi-response standard ready-mixed concrete (SRMC) optimization problem is addressed. This problem includes two conflicting quality optimization objectives. One of these objectives is to minimize the production cost. The other objective is to assign the optimal parameter set of SRMC's ingredient to each activity. To solve this problem, a hybrid fuzzy multi-response optimization and artificial neural network (ANN) algorithm is developed. The ANN algorithm is integrated into the multi-response SRMC optimization framework to predict and improve the quality of SRMC. The results show that fuzzy multi-response optimization model is more effective than crisp multi-response optimization model according to final production cost. However, the ANN model also gave more accurate results than the fuzzy model considering the regression analysis results.: Standard ready-mixed concrete; Multi-response optimization; Taguchi method; Fuzzy TOPSIS; Artificial neural networks.

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Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems

Şimşek¹,

İç²,

Şimşek³

2016

IJCIS

View full text Add to dashboard Cite

show abstract

“…is algorithm was commonly applied [26]. is was also shown in other articles in the literature [48][49][50][51][52][53][54]. e artificial neural networks could be used to solve the complicated civil engineering problems [55].…”

Section: Prediction Model Based On Artificial Neuralmentioning

confidence: 97%

Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Tanyıldızı

2018

Advances in Civil Engineering

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e artificial neural network and support vector machine were used to estimate the compressive strength and flexural strength of carbon fiber-reinforced lightweight concrete with the silica fume exposed to the high temperature. Cement was replaced with three percentages of silica fumes (0%, 10%, and 20%). e carbon fibers were used in four different proportions (0, 2, 4, and 8 kg/m 3 ). e specimens of each concrete mixture were heated at 20°C, 400°C, 600°C, and 800°C. After this process, the specimens were subjected to the strength tests. e amount of cement, the amount of silica fumes, the amount of carbon fiber, the amount of aggregates, and temperature were selected as the input variables for the prediction models. e compressive and flexural strengths of the lightweight concrete were determined as the output variables. e model results were compared with the experimental results. e best results were achieved from the artificial neural network model. e accuracy of the artificial neural network model was found at 99.02% and 96.80%.

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“…The software MATLAB (R.2014.b) was chosen among different programming languages as this software provides most efficient and flexible environment to develop an ANN [28]. A program code is written to perform the necessary computations.…”

Section: Development Of the Model Using Annmentioning

confidence: 99%

Application of Soft Computing Techniques for Predicting Cooling Time Required Dropping Initial Temperature of Mass Concrete.

Bhattarai¹,

Zhou²,

Zhao³

et al. 2017

CEJ

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Minimizing the thermal cracks in mass concrete at an early age can be achieved by removing the hydration heat as quickly as possible within initial cooling period before the next lift is placed. Recognizing the time needed to remove hydration heat within initial cooling period helps to take an effective and efficient decision on temperature control plan in advance. Thermal properties of concrete, water cooling parameters and construction parameter are the most influencing factors involved in the process and the relationship between these parameters are non-linear in a pattern, complicated and not understood well. Some attempts had been made to understand and formulate the relationship taking account of thermal properties of concrete and cooling water parameters. Thus, in this study, an effort have been made to formulate the relationship for the same taking account of thermal properties of concrete, water cooling parameters and construction parameter, with the help of two soft computing techniques namely: Genetic programming (GP) software "Eureqa" and Artificial Neural Network (ANN). Relationships were developed from the data available from recently constructed high concrete double curvature arch dam. The value of R for the relationship between the predicted and real cooling time from GP and ANN model is 0.8822 and 0.9146 respectively. Relative impact on target parameter due to input parameters was evaluated through sensitivity analysis and the results reveal that, construction parameter influence the target parameter significantly. Furthermore, during the testing phase of proposed models with an independent set of data, the absolute and relative errors were significantly low, which indicates the prediction power of the employed soft computing techniques deemed satisfactory as compared to the measured data.

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Artificial neural network for predicting drying shrinkage of concrete

Cited by 125 publications

References 10 publications

Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems

Hybridizing a fuzzy multi-response Taguchi optimization algorithm with artificial neural networks to solve standard ready-mixed concrete optimization problems

Prediction of the Strength Properties of Carbon Fiber‐Reinforced Lightweight Concrete Exposed to the High Temperature Using Artificial Neural Network and Support Vector Machine

Application of Soft Computing Techniques for Predicting Cooling Time Required Dropping Initial Temperature of Mass Concrete.

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