A practical hybrid NNGA system for predicting the compressive strength of concrete containing natural pozzolan using an evolutionary structure

Rebouh, Redouane; Boukhatem, Bakhta; Ghrici, Mohamed; Tagnit-Hamou, Arezki

doi:10.1016/j.conbuildmat.2017.05.165

Cited by 43 publications

(19 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the optimum set of weights was determined to be the best individual with the highest fitness value. Note that the parameters in GA were selected based on experiences, trial tests, and recommendations in the literature . The procedure of GA is illustrated in Figure .…”

Section: Methodsmentioning

confidence: 99%

A hybrid ensemble method for improved prediction of slope stability

Tang

2018

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

Accurate prediction of slope stability is a significant issue in geomechanics with many artificial intelligence (AI) techniques being utilised. However, the application of AI has not reached its full potential because of the lack of more robust algorithms. In this paper, we proposed a hybrid ensemble method for the improved prediction of slope stability using classifier ensembles and genetic algorithm. Gaussian process classification, quadratic discriminant analysis, support vector machine, artificial neural networks, adaptive boosted decision trees, and k-nearest neighbours were chosen to be individual AI techniques, and the weighted majority voting was used as the combination method. Validation method was chosen to be the 10-fold cross-validation, and performance measures were selected to be the accuracy, the receiver operating characteristic curve, and the area under the receiver operating characteristic curve (AUC). Grid search and genetic algorithm were used for the hyperparameter tuning and weight tuning respectively. The results show that the proposed hybrid ensemble method has great potential in improving the prediction of slope stability. Compared with individual classifiers, the optimum ensemble classifier achieved the highest AUC value (0.943) and the highest accuracy (0.902) on the testing set, denoting that the predictive performance has been improved. The optimum ensemble classifier with the Youden's cutoff was recommended for slope stability prediction with respect to the AUC value, the accuracy, the true positive rate, and the true negative rate. This research indicates that the use of the classifier ensembles, rather than the search for the ideal individual classifiers, might help for the slope stability prediction.

show abstract

Section: Methodsmentioning

confidence: 99%

A hybrid ensemble method for improved prediction of slope stability

Tang

2018

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

show abstract

“…The technique is based on a gradient descent technique. It is used for minimizing the error for a particular training pattern by adjusting the weights by a small amount at a time [15,23]. This technique is widely used in civil engineering applications [15].…”

Section: Description Of Neural Network Modelsmentioning

confidence: 99%

Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models

2021

Self Cite

View full text Add to dashboard Cite

Self-Compacting Concrete (SCC) is a relatively new type of concrete with high workability, high volume of paste and containing cement replacement materials such as slag, natural pozzolana and silica fume. Cement replacement materials provide a wide variety of benefits such as lower cost, reduced consumption of natural resources, reduced carbon dioxide emissions and improved fresh and hardened properties. SCC is used in many applications such as sections with congested reinforcement and high rise shear walls and there is a need for the prediction of the performance of SCC used. Artificial Neural networks (ANN) are widely used in civil engineering for the prediction of the performance of some engineering materials such as compressive strength and durability. However, currently, studies on SCC containing silica fume are very rare. In this paper, an artificial neural networks (ANN) model is developed to predict the compressive strength of SCC with silica fume using the Levenberg-Marquardt back propagation algorithm based on a database from 366 experimental studies. The model developed was correlated with a nonlinear relationship between the constituents (input) and the compressive strength of SCC (output). To evaluate the predictive ability and generalize the developed model, other researchers’ experimental results were compared with the model prediction and good agreements are found. A parametric study was conducted to study the sensitivity of the ANN proposed model to some parameters such as water/binder ratio and superplasticizer content. The model developed in this study can potentially be used for SCC compressive strength prediction with very acceptable results and a high correlation coefficient R2=0.93. The developed model is practical, easy to use and user friendly. Doi: 10.28991/cej-2021-03091642 Full Text: PDF

show abstract

“…Despite of the fact that several methods of designing were already developed, there is no any generalised systemic method for dosing the composition for an arbitrary concrete mixture until now (and certainly, there is no method for such complicated compositions as the slag-filled concretes). In these latter days, there are many publications, which are devoted to various attempts to develop any such generalised methodology [16,[22][23][24]. Many methods on the basis of the artificial intelligence have been already developed in order to forecast quality of the concrete and ensure optimisation in the course of the multi-factor analysis of the experimental-and-statistical data.…”

Section: Determination and Substantiated Selection Of The Ex-mentioning

confidence: 99%

Mechanical characteristics, as well as physical-and-chemical properties of the slag-filled concretes, and investigation of the predictive power of the metaheuristic approach

Borodin

Zhangabay

2019

Curved and Layered Structures

View full text Add to dashboard Cite

Our article is devoted to development and verification of the metaheuristic optimisation algorithm in the course of selection of the compositional proportions of the slag-filled concretes. The experimental selection of various compositions and working modes, which ensure one and the same fixed level of a basic property, is the very labour-intensive and time-consuming process. This process cannot be feasible in practice in many situations, for example, in the cases, where it is necessary to investigate composite materials with equal indicators of resistance to aggressive environments or with equal share of voids in the certain range of dimensions. There are many similar articles in the scientific literature. Therefore, it is possible to make the conclusion on the topicality of the above-described problem. In our article, we will consider development of the methodology of the automated experimental-and-statistical determination of optimal compositions of the slag-filled concretes. In order to optimise search of local extremums of the complicated functions of the multi-factor analysis, we will utilise the metaheuristic optimisation algorithm, which is based on the concept of the swarm intelligence. Motivation in respect of utilisation of the swarm intelligence algorithm is conditioned by the absence of the educational pattern, within which it is not necessary to establish a certain pattern of learning as it is necessary to do in the neural-network algorithms. In the course of performance of this investigation, we propose this algorithm, as well as procedure of its verification on the basis of the immediate experimental verification.

show abstract

A practical hybrid NNGA system for predicting the compressive strength of concrete containing natural pozzolan using an evolutionary structure

Cited by 43 publications

References 32 publications

A hybrid ensemble method for improved prediction of slope stability

A hybrid ensemble method for improved prediction of slope stability

Prediction of Compressive Strength of Self-Compacting Concrete (SCC) with Silica Fume Using Neural Networks Models

Mechanical characteristics, as well as physical-and-chemical properties of the slag-filled concretes, and investigation of the predictive power of the metaheuristic approach

Contact Info

Product

Resources

About