Multi-objective optimization of coal waste recycling in concrete using response surface methodology

Amiri, Hamid; Azadi, Sama; Karimaei, Mahmood; Sadeghi, Hamzeh; Dabbaghi, Farshad

doi:10.1016/j.jobe.2021.103472

Cited by 26 publications

(11 citation statements)

References 32 publications

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“…Substantial research has been conducted to evaluate using various supplementary cementitious materials (SCMs) such as fly ash (FA), slag, metakaolin, rice husk ash (RHA), silica fume (SF), and natural pozzolan for the partial replacement of Portland cement, which releases remarkable quantities of CO 2 [1][2][3][4]. Almost 7% of global anthropogenic greenhouse gas emissions originate from cement manufacturing [5][6][7][8]. Coal combustion for energy production is the primary industrial process for generating FA.…”

Section: Introduction 1fly Ashmentioning

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: Introduction 1fly Ashmentioning

confidence: 99%

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

et al. 2022

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“…This allows you to speed up the production of concreting. The increase in strength may be associated with the pozzolanic reaction, the completeness of which increases most at a later age [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. In addition, the microsize of the grains of TFM waste used in this study increases the compressive strength of the mortar, acting as a compactor.…”

Section: Resultsmentioning

confidence: 99%

Technogenic Fiber Wastes for Optimizing Concrete

et al. 2022

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A promising method of obtaining mineral fiber fillers for dry building mixtures is the processing of waste that comes from the production of technogenic fibrous materials (TFM). The novelty of the work lies in the fact that, for the first time, basalt production wastes were studied not only as reinforcing components, but also as binder ones involved in concrete structure formation. The purpose of the article is to study the physical and mechanical properties of waste technogenic fibrous materials as additives for optimizing the composition of raw concrete mixes. To assess the possibility of using wastes from the complex processing of TFM that were ground for 5 and 10 min as an active mineral additive to concrete, their chemical, mineralogical, and granulometric compositions, as well as the microstructure and physical and mechanical characteristics of the created concretes, were studied. It is established that the grinding of TFM for 10 min leads to the grinding of not only fibers, but also pellets, the fragments of which are noticeable in the total mass of the substance. The presence of quartz in the amorphous phase of TFM makes it possible to synthesize low-basic calcium silicate hydrates in a targeted manner. At 90 days age, at 10–20% of the content of TFM, the strength indicators increase (above 40 MPa), and at 30% of the additive content, they approach the values of the control composition without additives (above 35 MPa). For all ages, the ratio of flexural and compressive strengths is at the level of 0.2, which characterizes a high reinforcing effect. Analysis of the results suggests the possibility of using waste milled for 10 min as an active mineral additive, as well as to give better formability to the mixture and its micro-reinforcement to obtain fiber-reinforced concrete.

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“…38,58,59 The data flows through the network from the neurons of the input layer to the outputs through one or more hidden layers of neurons. 44,60 Input, output, weights, sum, and activation functions are five major characteristics that define ANN. The weights are the effect of other neurons or inputs on each processing neuron, which are determined by the sum function.…”

Section: Predictive Modelmentioning

confidence: 99%

Investigation on optimal lightweight expanded clay aggregate concrete at high temperature based on deep neural network

Dabbaghi

Tanhadoust

Nehdi

et al. 2022

Structural Concrete

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This study explores the effects of mixture design parameters on the residual mechanical properties of lightweight expanded clay aggregate (LECA) concrete exposed to elevated temperatures. A total of 30 lightweight concrete mixtures were cast and exposed to three elevated temperatures, namely 250°C, 500°C, and 700°C. The test variables comprised the LECA percentage used as partial volume substitution for natural sand (0%, 25%, 50%, 75%, and 100%), silica fume partial replacement for cement by weight (5%, 7.5%, 10%, 12.5%, and 15%), cement content (300, 400, 500, 600, and 700 kg/m3), and different water‐to‐cement (W/C) ratios (0.25, 0.313, 0.375, 0.438, and 0.5). The compressive and indirect tensile strengths were measured before and after exposure to elevated temperatures. The results indicate that the lightweight concretes incorporating higher contents of cement and silica fume and made with lower W/C ratio exhibited higher initial mechanical properties yet incurred more significant drop in mechanical properties after exposure to 500°C and 750°C.

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Multi-objective optimization of coal waste recycling in concrete using response surface methodology

Cited by 26 publications

References 32 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

Technogenic Fiber Wastes for Optimizing Concrete

Investigation on optimal lightweight expanded clay aggregate concrete at high temperature based on deep neural network

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