Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves

Behnood, Ali; Golafshani, Emadaldin Mohammadi

doi:10.1016/j.jclepro.2018.08.065

Cited by 286 publications

(117 citation statements)

References 57 publications

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“…Hammoudi et al [69] showed that the ANN technique is superior to the Response Surface Methodology in predicting compressive strength of recycled concrete aggregates. The prediction performance of the ANN technique for predicting the concrete compressive strength has also been reported in several other studies [70,71].…”

Section: Artificial Neural Network (Ann)supporting

confidence: 64%

“…If the number of artificial neurons in the hidden layer is too big or too small, it may result in over-or underfitting [81]. In this study, after several tests, eight hidden neurons-a number equal to the dimension in the input space-were selected, which is in line with the number suggested by Behnood and Golafshani [71]. Finally, the Levenberg-Marquardt learning algorithm was chosen for the training process of the model due to its higher efficiency ( [70,82] or [83]).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

et al. 2020

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This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength.

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Section: Artificial Neural Network (Ann)supporting

confidence: 64%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

et al. 2020

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“…Chithra et al [23] investigated the applicability of ANN for predicting the compressive strength of HPC containing nanosilica and copper slag. Several other researchers have used ANN-either individually, as a hybrid with other methods, or in ensemble models to predict the compressive strength of HPC [3,12,[24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete

2020

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Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously published studies. The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. k-fold cross-validation was used to assess the prediction reliability of the developed RELM model. The prediction results of the RELM model were evaluated using various error measures and compared with that of the standard extreme learning machine (ELM) and other methods presented in the literature. The findings of this research indicate that the compressive strength of HPC can be accurately estimated using the proposed RELM model.

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“…In combining AI methods with global optimization techniques, Bui et al [30] have implemented whale algorithm in order to better optimize the weight of a ANN model when predicting compressive strength of concrete. In another attempt, Behnood et al [31] have also predicted compressive strength of silica fume concrete based on a hybrid model involving ANN and multi-objective grey wolves technique. Based on these mentioned studies, it can be stated that the AI based methods could be able to analyze the nonlinear relationship between ingredients and compressive strength of various types of concrete for better prediction and assessment [32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

Pham

Dao

et al. 2019

Applied Sciences

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Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing the prediction accuracy of individual Adaptive Neuro Fuzzy Inference System (ANFIS) in predicting the compressive strength of manufactured sand concrete (MSC). The PCA technique was applied for reducing the noise in the input space, whereas, TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 configurations of MSC were used for the simulation, especially including the sand characteristics and the MSC long-term compressive strength. Using various validation criteria such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), the proposed method was validated and compared with several models, including individual ANFIS, Artificial Neural Networks (ANN) and existing empirical equations. The results showed that the proposed model exhibited great prediction capability compared with other models. Thus, it appeared as a robust alternative computing tool or an efficient soft computing technique for quick and accurate prediction of the MSC compressive strength.

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Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves

Cited by 286 publications

References 57 publications

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete

Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete

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