Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete

Naser, Ali H.; Badr, Ali; Henedy, Sadiq N.; Ostrowski, Krzysztof; Imran, Hamza

doi:10.1016/j.cscm.2022.e01262

Cited by 43 publications

(20 citation statements)

References 68 publications

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“…Various studies have investigated the Cs of environmentally friendly concrete. As a result, an extensive dataset with 164 experiments on the Cs of eco-friendly concrete incorporating both RA and GGBFS material was recently assembled in reference [ 50 ]. The ML model for predicting the concrete Cs was trained and tested using this dataset.…”

Section: Dataset Usedmentioning

confidence: 99%

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Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

Alhakeem

Jebur

Henedy³

et al. 2022

Materials

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A crucial factor in the efficient design of concrete sustainable buildings is the compressive strength (Cs) of eco-friendly concrete. In this work, a hybrid model of Gradient Boosting Regression Tree (GBRT) with grid search cross-validation (GridSearchCV) optimization technique was used to predict the compressive strength, which allowed us to increase the precision of the prediction models. In addition, to build the proposed models, 164 experiments on eco-friendly concrete compressive strength were gathered for previous researches. The dataset included the water/binder ratio (W/B), curing time (age), the recycled aggregate percentage from the total aggregate in the mixture (RA%), ground granulated blast-furnace slag (GGBFS) material percentage from the total binder used in the mixture (GGBFS%), and superplasticizer (kg). The root mean square error (RMSE) and coefficient of determination (R2) between the observed and forecast strengths were used to evaluate the accuracy of the predictive models. The obtained results indicated that—when compared to the default GBRT model—the GridSearchCV approach can capture more hyperparameters for the GBRT prediction model. Furthermore, the robustness and generalization of the GSC-GBRT model produced notable results, with RMSE and R2 values (for the testing phase) of 2.3214 and 0.9612, respectively. The outcomes proved that the suggested GSC-GBRT model is advantageous. Additionally, the significance and contribution of the input factors that affect the compressive strength were explained using the Shapley additive explanation (SHAP) approach.

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Section: Dataset Usedmentioning

confidence: 99%

“…The ML model for predicting the concrete Cs was trained and tested using this dataset. Details can be consulted in [ 50 ]. According to [ 39 ], the data collected from the collected experimental studies consider the effect of both GGBFS and RCA on the Cs of concrete.…”

Section: Dataset Usedmentioning

confidence: 99%

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

Alhakeem

Jebur

Henedy³

et al. 2022

Materials

Self Cite

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“…According to Equation ( 9), the expressions of the first three principal components are as follows: The input and output layers of the Spearman and PCA optimized BP neural network model were 3 and 1, respectively, and the interval of the number of nodes of the hidden layer was [3,12]. The input and output layers of the nonoptimized BP neural network model were 13 and 1, and the interval of its number of nodes in the hidden layer was [5,14].…”

Section: Pca Analysis Of Input Variablesmentioning

confidence: 99%

“…Al-Jamimi et al [11] got a conclusion that support vector machine (SVM) and genetic algorithm (GA) as the mixed model (SVM GA) had the best effect on the prediction of concrete compressive strength. Naser et al [12] proposed multivariate adaptive regression splines (MARS) to predict the compressive strength of ecofriendly concrete.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Wang

Zhao

Zhang³

et al. 2022

Wireless Communications and Mobile Computing

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Using real-time production data of concrete to predict its 28-day compressive strength has great significance for improving the engineering structure quality and overcoming the shortage of the traditional tests long period of concrete compressive strength. The current research has the shortcomings such as insufficient prediction accuracy, inadequate matching between data characteristics and model characteristics, and redundant input parameter information. This paper proposes a BP neural network prediction model optimized by Spearman and PCA. The model first uses Spearman method to reduce the number of the input variables by eliminating variables that have a low correlation with the compressive strength and then uses PCA to eliminate the correlation between material-related variables. Following this, the new uncorrelated input variables optimized by Spearman and PCA are input to the BP neural network-based model to predict the compressive strength of concrete. The results showed that it yielded the mean absolute percentage error (MAPE) of 2.78% and the root mean-squared error (RMSE) of 1.66 MPa, far lower than the error of 4.82% and 2.92 MPa obtained by the nonoptimized BP neural network, respectively. The proposed model fully exploits real-time monitoring data from the concrete mixing station, and its results are close to those of traditional tests. It has great practical significance to guide the concrete production and construction, shorten the production cycle, and reduce the project cost.

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“…Several researchers investigated the properties of concrete incorporating waste materials using experimental testing (Balasubramaniam & Stephen, 2022;Brekailo et al, 2022;Kabir et al, 2016). Other studies used regression analysis to predict the properties of green concrete (Naser et al, 2022;Waghmare et al, 2022). In addition to others who used machine learning techniques such as neural networks to forecast the relationship between various properties of green concrete (Mater et al, 2022;Ray et al, 2022).…”

Section: Research Significance and Objectivesmentioning

confidence: 99%

Decision Analysis for the Influence of Incorporating Waste Materials on Green Concrete Properties

Bakhoum

Mater

2022

Int J Concr Struct Mater

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Concrete industry is challenged by sustainability and technical concerns. Sustainability includes minimization of raw material usage, energy consumption, and emission of greenhouse gases, while technical concerns comprise the enhancement of mechanical properties and durability such as compressive strength, resistance to chloride, acids, and elevated temperatures. Therefore, recycling of industrial waste in manufacturing of green concrete has become a robust viable alternative to disposal, due to the limited natural resources and raw materials which contribute to sustainable construction. Consequently, this research aims to develop an approach using a multicriteria decision-making algorithm based on Analytical Hierarchy Process (AHP), to select the most suitable industrial waste to achieve the desired green concrete properties. The research starts by determining the alternatives including 18 industrial wastes, and the criteria including 14 properties of concrete. After that, an experimental database for the influence of the alternatives on the criteria is established based on the literature. Then, an algorithm is developed using a python script to analyze the influence of incorporating each of the industrial waste alternative on both the mechanical and sustainable properties of concrete. Subsequently, the efficiency of the proposed algorithm is validated using three case studies that present different circumstances of concrete specifications. Based on the proposed approach, the decision-maker can assign the appropriate residual waste to be incorporated into the concrete mix according to its application in a user-friendly manner. Such approach can support both sustainable use of waste materials and enhancement of concrete properties.

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Application of Multivariate Adaptive Regression Splines (MARS) approach in prediction of compressive strength of eco-friendly concrete

Cited by 43 publications

References 68 publications

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

Prediction of Compressive Strength of Concrete Using the Spearman and PCA-Based BP Neural Network

Decision Analysis for the Influence of Incorporating Waste Materials on Green Concrete Properties

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