Theoretical models to evaluate the effect of SiO2 and CaO contents on the long-term compressive strength of cement mortar modified with cement kiln dust (CKD)

Abdalla, Aso A.; Mohammed, Ahmed Salih

doi:10.1007/s43452-022-00418-4

Cited by 27 publications

(6 citation statements)

References 41 publications

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“…The ANN model is a machine learning system used in construction engineering for various numerical predictions and challenges [76][77][78][79]. The model is constructed based on three layers, input, hidden, and output, linked by biases and weights [80,81]. Several parameters affect the final model result, such as the number of hidden layers and neurons, the training algorithm, and the transfer function [82].…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Soft Computing and Machine Learning-Based Models to Predict the Slump and Compressive Strength of Self-Compacted Concrete Modified with Fly Ash

Jaf

2023

Sustainability

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Self-compacted concrete (SCC) is a special type of concrete; it is a liquid mixture appropriate for structural elements with excessive reinforcement without vibration. SCC is commonly produced by increasing the paste volume and cement content. As cement production is one of the huge factors in releasing CO2 gas into the atmosphere, by-product materials such as fly ash are utilized as a cement replacement in concrete. In addition to the positive environmental impact, fly ash can maintain an excellent fresh and mechanical property. Incorporating fly ash into self-compacted concrete is widely applied in practice. However, its application is frequently limited by a lack of knowledge about the mixed material gained from laboratory tests. The most significant mechanical property for all concrete types is compressive strength (CS); also, the slump flow diameter (SL) in the fresh state is a crucial property for SCC. Hence, developing an accurate and reliable model for predicting the CS and SL is very important for saving time and energy, as well as lowering the cost. This research study proposed a projection of both the CS and SL of SCC modified with fly ash by three different model approaches: Nonlinear regression (NLR), Multi-Linear regression (MLR), and Artificial Neural Networks (ANN). In this regard, two different datasets were collected and analyzed for developing models: 308 data samples were used for predicting the CS, and 86 data samples for the SL. Each database included the same five independent parameters. The ranges for CS prediction were: cement (134.7–583 kg/m3), water-to-binder ratio (0.27–0.9), fly ash (0–525 kg/m3), sand (478–1180 kg/m3), coarse aggregate (578–1125 kg/m3), and superplasticizer (0–1.4%). The dependent parameter (CS) ranged from 9.7 to 81.3 MPa. On the other hand, the data ranges for the SL prediction included independent parameters such as cement (83–733 kg/m3), water-to-binder ratio (0.26–0.58), fly ash (0–468 kg/m3), sand (624–1038 kg/m3), coarse aggregate (590–966 kg/m3), and superplasticizer (0.087–21.84%). Also, the dependent parameter (SL) ranged from 615 to 800 m. Various statistical assessment tools, such as the coefficient of determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Objective value (OBJ), and Scatter Index (SI), were used to evaluate the performance of the developed models. The results showed that the ANN model best predicted the CS and SL of SCC mixtures modified with fly ash. Furthermore, the sensitivity analysis demonstrated that the cement content is the most effective factor in predicting the CS and SL of SCC mixtures.

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Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Soft Computing and Machine Learning-Based Models to Predict the Slump and Compressive Strength of Self-Compacted Concrete Modified with Fly Ash

Jaf

2023

Sustainability

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“…Artificial intelligence (AI)-based techniques, like supervised machine learning (ML), are among the highly advanced prediction approaches for the stated purpose [ [54] , [55] , [56] , [57] , [58] ]. ML strategies simulate outcomes on the basis of input features, and testing confirms the resultant models [ [59] , [60] , [61] ]. Using ML methods to forecast the characteristics of cementitious composites and bituminous mixtures is gaining popularity [ [62] , [63] , [64] ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods

Khan,

Ahmad,

Amin

et al. 2023

Heliyon

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“…CKD is powdered waste generated under high temperatures, deposited in dust collectors, and transported to landfills [5]. It mainly comprises three components: raw materials, calcined matter, and some clinker granules [6][7][8]. The amount of CKD generated represents 15 to 20% of the clinker produced [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Eco-Sustainable Recycling of Cement Kiln Dust (CKD) and Copper Tailings (CT) in the Cemented Paste Backfill

2023

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Cement global demand shows continued growth and a significant increase in the production volume, which may negatively impact the non-renewable natural resources and the environment, which is incompatible with sustainability goals. Cement kiln dust (CKD) is a primary concern associated with clinker manufacturing as a waste byproduct. Similarly, the mining industry produces copper tailing as unwanted material while beneficiating the ore, creating environmental problems due to difficulty in managing worldwide generated quantities that reach billions of metric tons. This study investigated the beneficial utilization of cement kiln dust and copper tailing as undesirable wastes in industrial applications through underground mines’ cemented paste backfill (CPB). Sixty different mixtures were prepared with three types of CKD collected from various cement manufacturers and were accordingly used with a proportion of 5, 10, and 15% to partially replace ordinary Portland cement (OPC) and pozzolan Portland cement (PPC) binders, represented in hundreds of CPB samples. The hardened specimens were subjected to density, uniaxial compressive strength (UCS), and axial deformation measurements to evaluate the physical and mechanical properties at curing up to 90 days. Meanwhile, X-ray powder diffraction (XRD) was extensively applied to chemically investigate the hydration products of CPB-hardened mixtures. Moreover, we developed a UCS predictive model applying two techniques: multiple variables regression analysis and artificial neural network (ANN). The results showed that the tricalcium silicate (Alite) and dicalcium silicate (Belite) phases form C-S-H upon hydrations and provide high strength in the binary mixtures. Meanwhile, the CKD’s lime saturation factor (LSF) governed the strength value in the ternary mixtures that utilized copper tailings. That makes CKD practical in the CPB mixture when partially replacing the OPC and PPC binders, with a proportion of up to 15%. In addition, the ANN technique’s predictive model exhibited a significant positive correlation with excellent statistical parameters that achieved 0.995, 0.065, and 0.911 for R2, RMSE, and MAE, respectively.

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Theoretical models to evaluate the effect of SiO2 and CaO contents on the long-term compressive strength of cement mortar modified with cement kiln dust (CKD)

Cited by 27 publications

References 41 publications

Soft Computing and Machine Learning-Based Models to Predict the Slump and Compressive Strength of Self-Compacted Concrete Modified with Fly Ash

Soft Computing and Machine Learning-Based Models to Predict the Slump and Compressive Strength of Self-Compacted Concrete Modified with Fly Ash

Evaluating the effectiveness of waste glass powder for the compressive strength improvement of cement mortar using experimental and machine learning methods

Eco-Sustainable Recycling of Cement Kiln Dust (CKD) and Copper Tailings (CT) in the Cemented Paste Backfill

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