Mechanical and Fracture Parameters of Ultra-High Performance Fiber Reinforcement Concrete Cured via Steam and Water: Optimization of Binder Content

Mala, Avan Ahmed; Sherwani, Aryan Far H.; Younis, Khaleel H.; Faraj, Rabar H.; Mosavi, Amir

doi:10.3390/ma14082016

Cited by 14 publications

(10 citation statements)

References 53 publications

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“…Additionally, the squared correlation coefficient (

) between the estimated and experimental datasets, as well as the correlation coefficient (

) between the experimental and estimated values, must approach one [ 66 , 67 , 68 ]. As seen in Table 6 , the suggested MEP model meets nearly all the stated requirements, which is consistent with the findings of existing literature and recommendations [ 69 , 70 , 71 , 72 ].…”

Section: Resultssupporting

confidence: 87%

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

et al. 2022

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Rice husk ash (RHA) is a significant pollutant produced by agricultural sectors that cause a malignant outcome to the environment. To encourage the re-use of RHA, this work used multi expression programming (MEP) to construct an empirical model for forecasting the compressive nature of concrete made with RHA (CRHA) as a cement substitute. Thus, the compressive strength of CRHA was developed comprising of 192 findings from the broad and trustworthy database obtained from literature review. The most significant characteristics, namely the specimen’s age, the percentage of RHA, the amount of cement, superplasticizer, aggregates, and the amount of water, were used as input for the modeling of CRHA. External validation, sensitivity analysis, statistical checks, and Shapley Additive Explanations (SHAP) analysis were used to evaluate the models’ performance. It was discovered that the most significant factors impacting the compressive strength of CRHA are the age of the concrete sample (AS), the amount of cement (C) and the amount of aggregate (A). The findings of this study have the potential to increase the re-use of RHA in the production of green concrete, hence promoting environmental protection and financial gain.

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“…Additionally, the squared correlation coefficient (

) between the estimated and experimental datasets, as well as the correlation coefficient (

Section: Resultssupporting

confidence: 87%

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

et al. 2022

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“…In addition to the application methods, the water to cement ratio, the fresh and hardened characteristics depend on the concentration, composition, and particle dimension of the binder and additive particles. It is possible to use NS in powder form or colloidally [28]. Colloidal NS contains amorphous hydroxylated silica NPs with a dimension varying from 1 to 500 nm in a colloidal suspension form.…”

Section: Introductionmentioning

confidence: 99%

Systematic Multiscale Models to Predict the Compressive Strength of Cement Paste as a Function of Microsilica and Nanosilica Contents, Water/Cement Ratio, and Curing Ages

2022

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Sustainable construction requires high-strength cement materials that additives with silica content could provide the requirements as well. In this study, the effect of the micro and nano-size of silica on the compressive strength of cement paste using different mathematical approaches is investigated. This study compares the strength of preferentially replaced cement pastes with microsilica (MS) and nanosilica (NS) incorporation by proposing several mathematical models. In this study, 205 data were extracted from the literature and analyzed. The modeling processes considered the most significant variables as input variables that influence the compression strength, such as curing time, which ranged between 3 and 90 days, the water-cement ratio, which varied between 0.4 and 0.85, and NS ranged between 0 and 15%. MS ranged between 0 and 40% based on the weight of cement. In this process, the compressive strength of cement paste modified with NS and MS was modeled using four different models, including the Linear Regression Model (LR), Nonlinear Model (NLR), Multi-Logistic Regression Model (MLR), and artificial neural network (ANN). The efficiency of the suggested models was evaluated using different statistical assessments, such as the Root Mean Squared Error (RMES), the Mean Absolute Error (MAE), Scatter Index (SI), Objective value (OBJ), and coefficient of determination (R2). The findings revealed that the ANN model conducted better performance for predicting compressive strength for cement paste than the other models based on the statistical assessment. In addition, based on the statistical assessment of the sensitivity of parameters, NS had more of an effect on the compressive strength of cement paste, with 6.3% more than MS.

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“…He introduced the word geopolymer to refer to these binders [ 8 ]. The most commonly utilized industrialized by-product materials as binder ingredients are FA and slag [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Fresh, Mechanical, and Durability Behavior of Fly Ash-Based Self Compacted Geopolymer Concrete: Effect of Slag Content and Various Curing Conditions

2022

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This investigation evaluates the influence of various curing conditions and slag inclusion on the fresh, mechanical, and durability properties of self-compacting geopolymer concrete (SCGC) based on fly ash (FA). Curing temperature and curing time have a vital role in the strength and microstructure of geopolymer concrete. Therefore, to begin the research, the impacts of different curing conditions (curing temperature and curing time) and slag content on the compressive strength of FA-based SCGC were examined to determine the optimum curing method. A series of four SCGC mixes with a fixed binder content (450 kg/m3) and an alkaline/binder ratio of 0.5 was designated to conduct a parametric study. FA was replaced with slag at four different substitution percentages, including 0%, 30%, 50%, and 100% of the total weight of the binder. The fresh properties of the produced SCGC specimens were investigated in terms of slump flow diameter, T50 flow time, and L-box height ratio. Additionally, the following mechanical properties of SCGC specimens were investigated: modulus of elasticity and fracture parameters. The water permeability and freezing–thawing resistance were studied to determine the durability behavior of SCGC. In this study, the optimum curing temperature was 85 °C for the duration of 24 h, which provided the maximum compressive strength. The results confirmed that adding slag affected the workability of SCGC mixtures. However, the mechanical characteristics, fracture parameters, and durability performance of SCGC were improved for slag-rich mixtures. When using 50% slag instead of FA, the percentage increase in compressive, flexural, elastic module, and fracture energy test values were about 100%, 43%, 58%, and 55%, respectively, whilst the percentage decrease in water permeability was 65% and the resistance to freeze–thaw test in terms of surface scaling was enhanced by 79%.

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Mechanical and Fracture Parameters of Ultra-High Performance Fiber Reinforcement Concrete Cured via Steam and Water: Optimization of Binder Content

Cited by 14 publications

References 53 publications

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

Forecasting Compressive Strength of RHA Based Concrete Using Multi-Expression Programming

Systematic Multiscale Models to Predict the Compressive Strength of Cement Paste as a Function of Microsilica and Nanosilica Contents, Water/Cement Ratio, and Curing Ages

Fresh, Mechanical, and Durability Behavior of Fly Ash-Based Self Compacted Geopolymer Concrete: Effect of Slag Content and Various Curing Conditions

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