Toward sustainability in optimizing the fly ash concrete mixture ingredients by introducing a new prediction algorithm

Naseri, Hamed; Jahanbakhsh, Hamid; Khezri, Khashayar; Javid, Ali Akbar Shirzadi

doi:10.1007/s10668-021-01554-2

Cited by 31 publications

(8 citation statements)

References 92 publications

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“…It was also mentioned that the mechanical properties of high-strength, high-volume FA in steel-fiber-reinforced concrete offer an alternative sustainable option for the concrete industry. In another study, the optimization of the mixture proportions of green and sustainable concretes was accomplished [38]. In this respect, the model to be optimized took concrete effect into account, as well as the unit cost and environmental impact.…”

Section: Optimization Methodsmentioning

confidence: 99%

Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations

et al. 2022

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Infrastructure design, construction and development experts are making frantic efforts to overcome the overbearing effects of greenhouse gas emissions resulting from the continued dependence on the utilization of conventional cement as a construction material on our planet. The amount of CO2 emitted during cement production, transportation to construction sites, and handling during construction activities to produce concrete is alarming. The present research work is focused on proposing intelligent models for fly ash (FA)-based concrete comprising cement, fine and coarse aggregates (FAg and CAg), FA, and water as mix constituents based on environmental impact (P) considerations in an attempt to foster healthier and greener concrete production and aid the environment. FA as a construction material is discharged as a waste material from power plants in large amounts across the world. Its utilization as a supplementary cement ensures a sustainable waste management mechanism and is beneficial for the environment too; hence, this research work is a multi-objective exercise. Intelligent models are proposed for multiple concrete mixes utilizing FA as a replacement for cement to predict 28-day concrete compressive strength and life cycle assessment (LCA) for cement with FA. The data collected show that the concrete mixes with a higher amount of FA had a lesser impact on the environment, while the environmental impact was higher for those mixes with a higher amount of cement. The models which utilized the learning abilities of ANN (-BP, -GRG, and -GA), GP and EPR showed great speed and robustness with R2 performance indices (SSE) of 0.986 (5.1), 0.983 (5.8), 0.974 (7.0), 0.78 (19.1), and 0.957 (10.1) for Fc, respectively, and 0.994 (2.2), 0.999 (0.8), 0.999 (1.0), 0.999 (0.8), and 1.00 (0.4) for P, respectively. Overall, this shows that ANN-BP outclassed the rest in performance in predicting Fc, while EPR outclassed the others in predicting P. Relative importance analyses conducted on the constituent materials showed that FA had relatively good importance in the concrete mixes. However, closed-form model equations are proposed to optimize the amount of FA and cement that will provide the needed strength levels without jeopardizing the health of the environment.

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Section: Optimization Methodsmentioning

confidence: 99%

Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations

et al. 2022

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“…The data are generally divided into three groups: training, validation, and testing data. The training data are used to train the model ( 28 ). Validation data are employed to tune hyperparameters.…”

Section: Methodsmentioning

confidence: 99%

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

Naseri

Shokoohi

Jahanbakhsh

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Road infrastructures are fundamental parts of peoples’ lives, allowing them to access various destinations and activities. Accordingly, infrastructure should be in an appropriate condition. A pavement maintenance plan should be optimized, and pavement condition should be predicted accurately to obtain optimal pavement maintenance solutions. Therefore, the prediction of pavement conditions with high accuracy has been an immense concern. This study aims to introduce a new approach to accurately predict pavement international roughness index (IRI) over the long term. To this end, all the vital parameters, including initial IRI, pavement age, lane width, traffic loadings, structural characteristics, climatic features, and pavement distresses, are considered. With all the vital parameters, the prediction problem includes 58 variables. Thus, the application of a proper feature-selection technique is vital. To this end, a novel hybrid feature-selection method is introduced by a combination of arithmetic optimization algorithm and stochastic gradient descent regression (AOA-SGDR). Moreover, the performance of the proposed feature-selection method is compared with Lasso and all features. Five machine-learning algorithms, including random forest regression (RFR), support vector machine, multi-layer perceptron, decision-tree regression, and multiple linear regression, are employed for the prediction process. By employing AOA-SGDR, the average testing-data mean absolute error (MAE) reduces by at least 7.92%. Meanwhile, RFR provides the highest accuracy, with average testing-data MAE of 0.095 m/km. Moreover, analyzing the parameters indicates that initial IRI, pavement age, equivalent single axle load (ESAL), and structural number (SN) have the most significant relative influence on IRI.

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“…ANNs can be employed to model complicated problems in a short time. They are good at nonlinear prediction problems in a reasonable amount of time [29]. An MLP generally includes an input layer, some hidden layers, and an output layer.…”

Section: Multi-layered Perceptronmentioning

confidence: 99%

A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

et al. 2021

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Indications of people’s environmental concern are linked to transport decisions and can provide great support for policymaking on climate change. This study aims to better predict individual climate change stage of change (CC-SoC) based on different features of transport-related behavior, General Ecological Behavior, New Environmental Paradigm, and socio-demographic characteristics. Together these sources result in over 100 possible features that indicate someone’s level of environmental concern. Such a large number of features may create several analytical problems, such as overfitting, accuracy reduction, and high computational costs. To this end, a new feature selection technique, named the Coyote Optimization Algorithm-Quadratic Discriminant Analysis (COA-QDA), is first proposed to find the optimal features to predict CC-SoC with the highest accuracy. Different conventional feature selection methods (Lasso, Elastic Net, Random Forest Feature Selection, Extra Trees, and Principal Component Analysis Feature Selection) are employed to compare with the COA-QDA. Afterward, eight classification techniques are applied to solve the prediction problem. Finally, a sensitivity analysis is performed to determine the most important features affecting the prediction of CC-SoC. The results indicate that COA-QDA outperforms conventional feature selection methods by increasing average testing data accuracy from 0.7 to 5.6%. Logistic Regression surpasses other classifiers with the highest prediction accuracy.

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Toward sustainability in optimizing the fly ash concrete mixture ingredients by introducing a new prediction algorithm

Cited by 31 publications

References 92 publications

Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations

Multi-Objective Optimization of Sustainable Concrete Containing Fly Ash Based on Environmental and Mechanical Considerations

Novel Soft-Computing Approach to Better Predict Flexible Pavement Roughness

A Novel Feature Selection Technique to Better Predict Climate Change Stage of Change

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