Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete

Amlashi, Amir Tavana; Abdollahi, Serveh; Goodarzi, Saeed; Ghanizadeh, Ali Reza

doi:10.1016/j.jclepro.2019.05.168

Cited by 104 publications

(28 citation statements)

References 56 publications

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“…Classification, clustering, and regression are examples of machine learning approaches that can be used to estimate a variety of parameters with varying degrees of effectiveness and predict the precise ultrasonic pulse velocity of concrete. As a result of recently evolved artificial intelligence, the mechanical properties of different material types can be forecasted with the help of supervised machine learning (ML) algorithms [46]. ML approaches, e.g., classification, regression, and clustering, are deployed for statistical processes and for the prediction of compressive strength with high accuracy [47].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

et al. 2022

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Researchers and engineers are presently focusing on efficient waste material utilization in the construction sector to reduce waste. Waste marble dust has been added to concrete to minimize pollution and landfills problems. Therefore, marble dust was utilized in concrete, and its prediction was made via an artificial intelligence approach to give an easier way to scholars for sustainable construction. Various blends of concrete having 40 mixes were made as partial substitutes for waste marble dust. The ultrasonic pulse velocity of waste marble dust concrete (WMDC) was compared to a control mix without marble dust. Additionally, this research used standalone (multiple-layer perceptron neural network) and supervised machine learning methods (Bagging, AdaBoost, and Random Forest) to predict the ultrasonic pulse velocity of waste marble dust concrete. The models’ performances were assessed using R2, RMSE, and MAE. Then, the models’ performances were validated using k-fold cross-validation. Furthermore, the effect of raw ingredients and their interactions using SHAP analysis was evaluated. The Random Forest model, with an R2 of 0.98, outperforms the MLPNN, Bagging, and AdaBoost models. Compared to all the other models (individual and ensemble), the Random Forest model with greater R2 and lower error (RMSE, MAE) has a superior performance. SHAP analysis revealed that marble dust content has a positive and direct influence on and relationship to the ultrasonic pulse velocity of concrete. Using machine learning to forecast concrete properties saves time, resources, and effort for scholars in the engineering sector.

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Section: Introductionmentioning

confidence: 99%

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

et al. 2022

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“…This procedure limited the range of applicability of the developed models [36]. Similarly, the properties of WFS-based concrete were predicted by using tree structure modeling techniques and ANN [37,38]. However, a parametric study could not be conducted for the developed models due to the linear nature of these modeling techniques, which can be regarded as an effective tool to assess whether the developed models have accurately learned the underlying physical phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm-Based Modeling of Split Tensile Strength of Foundry Sand-Based Concrete

et al. 2022

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Foundry sand (FS) is produced as a waste material by metal casting foundries. It is being utilized as an alternative to fine aggregates for developing sustainable concrete. In this paper, an artificial intelligence technique, i.e., gene expression programming (GEP) has been implemented to empirically formulate prediction models for split tensile strength (ST) of concrete containing FS. For this purpose, an extensive experimental database has been collated from the literature and split up into training, validation, and testing sets for modeling purposes. ST is modeled as a function of water-to-cement ratio, percentage of FS, and FS-to-cement content ratio. The reliability of the proposed expression is validated by conducting several statistical and parametric analyses. The modeling results depicted that the prediction model is robust and accurate with a high generalization capability. The availability of reliable formulation to predict strength properties can promote the utilization of foundry industry waste in the construction sector, promoting green construction and saving time and cost incurred during experimental testing.

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“…To date, various ML techniques have been used to simulate the mechanical characteristics of concretes, including multivariate adaptive regression splines (MARS) [11], genetic expression programming (GEP) [12], artificial neural network (ANN) [13], adaptive neuro-fuzzy inference systems (ANFIS) [14], and support vector machines (SVM) [15]. For instance, Ashrafian et al developed an evolutionary method based on a MARS-integrated water cycle algorithm to propose a nonlinear relationship between mixture components and the compressive strength of foamed cellular lightweight concrete [16].…”

Section: Introductionmentioning

confidence: 99%

Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement

Ashrafian¹,

Amiri

Masoumi

et al. 2020

Applied Sciences

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In the field of pavement engineering, the determination of the mechanical characteristics is one of the essential processes for reliable material design and highway sustainability. Early determination of the mechanical characteristics of pavement is essential for road and highway construction and maintenance. Tensile strength (TS), compressive strength (CS), and flexural strength (FS) of roller-compacted concrete pavement (RCCP) are crucial characteristics. In this research, the classification-based regression models random forest (RF), M5rule model tree (M5rule), M5prime model tree (M5p), and chi-square automatic interaction detection (CHAID) are used for simulation of the mechanical characteristics of RCCP. A comprehensive and reliable dataset comprising 621, 326, and 290 data records for CS, TS, and FS experimental cases was extracted from several open sources in the literature. The mechanical properties are determined based on influential input combinations that are processed using principle component analysis (PCA). The PCA method specifies that volumetric/weighted content forms of experimental variables (e.g., coarse aggregate, fine aggregate, supplementary cementitious materials, water, and binder) and specimens’ age are the most effective inputs to generate better performance. Several statistical metrics were used to evaluate the proposed classification-based regression models. The RF model revealed an optimistic classification capacity of the CS, TS, and FS prediction of the RCCP in comparison with the CHAID, M5rule, and M5p models. Monte-Carlo simulation was used to verify the results in terms of the uncertainty and sensitivity of variables. Overall, the proposed methodology formed a reliable soft computing model that can be implemented for material engineering, construction, and design.

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Soft computing based formulations for slump, compressive strength, and elastic modulus of bentonite plastic concrete

Cited by 104 publications

References 56 publications

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

Investigating the Ultrasonic Pulse Velocity of Concrete Containing Waste Marble Dust and Its Estimation Using Artificial Intelligence

Evolutionary Algorithm-Based Modeling of Split Tensile Strength of Foundry Sand-Based Concrete

Classification-Based Regression Models for Prediction of the Mechanical Properties of Roller-Compacted Concrete Pavement

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