Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

Chopra, Palika; Sharma, Rajendra K.; Kumar, Maneek

doi:10.1155/2016/7648467

Cited by 143 publications

(72 citation statements)

References 15 publications

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“…For ANN architectures and training of the same, the significant internal parameters include learning rate, initial weights, learning cycle ,number of training epochs, numbers of hidden layers, numbers of neurons in each hidden layer and transfer functions for hidden layers and output layers [11]. In this study, a three-layered feed-forward network was trained with back-propagation (BP) training algorithm.…”

Section: Development Of the Model Using Annmentioning

confidence: 99%

“…The numbers of neurons in the hidden layer was adjusted 13 after doing many trial and errors. A non-linear hyperbolic tangent sigmoid function and linear function were used as transfer functions in hidden and an output layer respectively due to their ability to learn the complex non-linear relation between an input parameter and an output parameter [11]. Network training parameters adopted to construct an ANN model are summarized in Tab.…”

Section: Development Of the Model Using Annmentioning

confidence: 99%

“…ANN method is widely used by many researchers for determining different properties of concrete [11][12][13] and concrete structures [14][15][16]. Among these soft computing techniques, GP which was first introduced by John Koza (1992) [17], is another branch of machine learning methods which automatically generates computer models based on the rule of natural genetic evolution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Development Of the Model Using Annmentioning

confidence: 99%

Section: Development Of the Model Using Annmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Bhattarai¹,

Zhou²,

Zhao³

et al. 2017

CEJ

View full text Add to dashboard Cite

show abstract

“…The architecture of ANN consists of artificial neurons analogous to natural neurons of the human brain that are clumped into series of input, hidden and output layers. According to Chopra et al [18] there are three essentials to consider before structuring the architecture of ANN model, namely: 1) Topologyorganization and interconnection of a neural network into layers; 2) Learning -information storage in the network; and 3) Recall -retrieval of information from the network. These essentials are reflected with the following internal parameters: 1) performance function, 2) learning function, 3) weights and biases, 4) hidden layers and neurons, 5) and transfer function.…”

Section: B Model Developmentmentioning

confidence: 99%

“…Specifically, tansig function operates by returning outputs compressed between -1 and 1 in which it has an ability to learn complex nonlinear relation between the input and output parameters. On the other hand, Levernberg-Marquardt learning function is the most suitable algorithm for concrete related data according to available literatures [18,19]. It is attributed to be significantly high speed training method especially for moderately sized feedforward neural networks as well as non-linear problems.…”

Section: Regional Conference In Civil Engineering (Rcce)mentioning

confidence: 99%

Strength Durability-Based Design Mix of Self-Compacting Concrete with Cementitious Blend using Hybrid Neural Network-Genetic Algorithm

Rinchon

2017

IJPS

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Abstract-Sustainable development in self-compacting concrete (SCC) has been studied extensively for the recent years for the purpose to address its growing demand in construction projects. Sustainable SCC can be defined as concrete mix with partially replaced cement content that varies from low to high level using different mineral admixtures. Silica fume and fly ash which is considered as the most common sustainable mineral admixtures for binary and ternary cementitious blends show good effect to the compressive strength and chloride penetration resistivity of hardened SCC. In this paper, this effect was further investigated by using two widely used biological inspired computing models namely the artificial neural network (ANN) and genetic algorithm (GA). The test results of compressive strength and chloride ion penetration resistance from thirty-six concrete samples with varying replacement level of binary and ternary cementitious blends were utilized as inputs for model development. ANN was used to obtain models that describe analytically the relationship of material components to the compressive strength and chloride penetration resistivity. The derived models were further explored through optimization using GA. Results shows that ANN was able to establish the relationship of strengthdurability parameters to the material components while GA is able to derived optimal mix proportion for best strengthdurability performance. The present study also validates the sensitivity of the replacement level of silica fume and fly ash as a ternary cementitious blend to the strength-durability performance of SCC. This indicates that high volume content of ternary blended cement can improve chloride penetration resistivity and exhibited high compressive strength.

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Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model

Cook

Monyake

Hayat

et al. 2020

Engineering Reports

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Froth flotation process is extensively used for selective separation of base metal sulfides from uneconomic mineral resources. Reliable prediction of process outcomes (metal recovery and grade) is vital to ensure peak performance. This work employs an innovative hybrid machine learning (ML) model-constructed by combining the random forest model and the firefly algorithm-to predict froth flotation efficiency of galena and chalcopyrite in relation to various experimental process parameters. The hybrid model's prediction performance was rigorously evaluated, and compared against four different standalone ML models. The outcomes of this study illustrate that the hybrid ML model has the prediction ability to process outcomes with high-fidelity, while consistently outperforming the standalone ML models. K E Y W O R D Scomplex sulfide ore, firefly algorithm, froth flotation, machine learning, random forests INTRODUCTIONFroth flotation process is the most widely used practice to concentrate sulfide minerals-the main economic sources of world supplies of base metals. The flotation efficiency of metal sulfides is influenced by several key process variables (eg, water chemistry, reagents chemistry, feed characteristics, cell type, and aeration rate). [1][2][3] Although each of these parameters influences the flotation process outcomes (metal recovery and grade) independently, their interdependence

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Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

Cited by 143 publications

References 15 publications

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

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

Strength Durability-Based Design Mix of Self-Compacting Concrete with Cementitious Blend using Hybrid Neural Network-Genetic Algorithm

Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model

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