Predicting the Compressive Strength of Rubberized Concrete Using Artificial Intelligence Methods

Gregori, Amedeo; Castoro, Chiara; Venkiteela, Giri

doi:10.3390/su13147729

Cited by 16 publications

(6 citation statements)

References 47 publications

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“…Compressive strength predictions for rubberized concrete were made utilizing Gaussian process regression (GPR) models and support vector machine (SVM) by Gregori et al [23]. Previous studies were analyzed for compressive strength data.…”

Section: Crumb Rubbermentioning

confidence: 99%

Review of Recycling Natural and Industrial Materials Employments in Concrete

Rasool

Al-Moameri

Abdulkarem

2023

jeasd

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Concrete is the most commonly used and dependable substance and the most consumed among all other construction materials. However, the construction industry’s insatiable need for building supplies has led to the widespread exploitation of natural resources like river sand and stone. However, one of the most pressing problems in the construction industry and others nowadays is excessive waste and byproducts. Promoting ecological control and the goal of sustainable progress has put environmental pressure on all businesses, including construction, to embrace eco-friendly practices. Environmentalists’ concerns about the dangers posed by the widespread use of natural resources in building construction are growing. In some instances, construction debris may be harmful to the environment. Global building growth and redevelopment plans have worsened demolition in the construction industry. The demand for construction materials is increasing yearly due to urbanism, but their costs are rising simultaneously. Using industrial waste like polymers and rubbers and natural waste like olive stones and pumice stones to replace cement, fine aggregate, and coarse aggregate to reduce building costs conserves natural resources from exhaustion and protects the environment from waste impact and risks. The current research shows how waste can be used to replace materials on construction sites and the impact of the properties of these materials on the quality of concrete.

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Section: Crumb Rubbermentioning

confidence: 99%

Review of Recycling Natural and Industrial Materials Employments in Concrete

Rasool

Al-Moameri

Abdulkarem

2023

jeasd

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“…In recent years, soft computing represented by machine learning (ML) has made remarkable achievements in the prediction of rubber-concrete material performance, e.g., artificial neural networks (ANN) [ 12 , 13 ], support vector machine (SVM) [ 14 , 15 ], back-propagation neural network (BPNN) [ 16 , 17 ], extreme learning machine (ELM) [ 18 , 19 ], multi-layer perceptron (MLP) [ 20 , 21 ] and trees-based models [ 22 , 23 ]. Among the ML models, the random forest (RF) model has an excellent resistance to overfitting and a fitting ability in solving prediction problems [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material

et al. 2023

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The application of aseismic materials in foundation engineering structures is an inevitable trend and research hotspot of earthquake resistance, especially in tunnel engineering. In this study, the pelican optimization algorithm (POA) is improved using the Latin hypercube sampling (LHS) method and the Chaotic mapping (CM) method to optimize the random forest (RF) model for predicting the aseismic performance of a novel aseismic rubber-concrete material. Seventy uniaxial compression tests and seventy impact tests were conducted to quantify this aseismic material performance, i.e., strength and energy absorption properties and four other artificial intelligence models were generated to compare the predictive performance with the proposed hybrid RF models. The performance evaluation results showed that the LHSPOA-RF model has the best prediction performance among all the models for predicting the strength and energy absorption property of this novel aseismic concrete material in both the training and testing phases (R2: 0.9800 and 0.9108, VAF: 98.0005% and 91.0880%, RMSE: 0.7057 and 1.9128, MAE: 0.4461 and 0.7364; R2: 0.9857 and 0.9065, VAF: 98.5909% and 91.3652%, RMSE: 0.5781 and 1.8814, MAE: 0.4233 and 0.9913). In addition, the sensitive analysis results indicated that the rubber and cement are the most important parameters for predicting the strength and energy absorption properties, respectively. Accordingly, the improved POA-RF model not only is proven as an effective method to predict the strength and energy absorption properties of aseismic materials, but also this hybrid model provides a new idea for assessing other aseismic performances in the field of tunnel engineering.

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“…Machine learning has been widely used in classification and prediction by scholars because it can dig out intrinsic relationships from large amounts of historical data for classification or prediction. Such as, deep learning [23,24], random forest (RF) [25], support vector machine (SVM) [26] and backpropagation neural network (BPNN) [27,28], and so on, have been gradually applied to prediction in various engineering fields because of its good accuracy. A comparative study was made by Liu et al [29] on the prediction of frost resistance of recycled concrete by using three methods, including ANN, Gaussian process regression, and multivariate adaptive regression spline; the results showed that, among the three methods, the prediction accuracy of ANN model is the best.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Frost Resistance of Iron Ore Tailings Concrete Based‐BP Neural Network

Fu,

Li,

Han

et al. 2023

Journal of Sensors

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In order to predict the relative dynamic elastic modulus (RDEM), which is used to reflect the frost resistance of iron ore tailings concrete, the backpropagation neural network (BPNN) was used in this study. Here, one hidden layer was chosen in the structure of BPNN. It is well known that the number of neurons in the hidden layer is the key of BPNN; hence, checking the features of overfitting was chosen in this paper to determine the number of neurons in the hidden layer. According to the actual conditions of freeze–thaw cycle test, a BPNN model of 2D input vector and 1D output vector was established. Thirty datasets from the test were used for training and test the proposed ANN. The results showed that the predicted values were in good agreement with the measured ones, and the correlation coefficient between them reached 0.9505. It showed that the BPNN, with the ability to solve nonlinear problems, had great advantages in predicting RDEM, and it can be used as reliable and simple forecasting tools for the prediction of frost resistance of iron ore tailings concrete.

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Predicting the Compressive Strength of Rubberized Concrete Using Artificial Intelligence Methods

Cited by 16 publications

References 47 publications

Review of Recycling Natural and Industrial Materials Employments in Concrete

Review of Recycling Natural and Industrial Materials Employments in Concrete

Application of the Improved POA-RF Model in Predicting the Strength and Energy Absorption Property of a Novel Aseismic Rubber-Concrete Material

Prediction of the Frost Resistance of Iron Ore Tailings Concrete Based‐BP Neural Network

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