Impact of sulfate activation of rice husk ash on the performance of high strength steel fiber reinforced recycled aggregate concrete

Althoey, Fadi; Zaid, Osama; de‐Prado‐Gil, Jesús; Palencia, Covadonga; Ali, Elias; Hakeem, Ibrahim Y.; Martínez‐García, Rebeca

doi:10.1016/j.jobe.2022.104610

Cited by 30 publications

(22 citation statements)

References 55 publications

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“…Simultaneously, natural resources are decreasing because of rapid modern urbanization. Mountains, the principal supply of high-quality aggregates, rapidly diminish [ 17 , 18 ]. As a result, numerous nations worldwide have experienced natural disasters [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

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A considerable amount of discarded building materials are produced each year worldwide, resulting in ecosystem degradation. Self-compacting concrete (SCC) has 60–70% coarse and fine particles in its composition, so replacing this material with another waste material, such as recycled aggregate (RA), reduces the cost of SCC. This study compares novel Artificial Neural Network algorithm techniques—Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB)—to estimate the 28-day compressive strength (f’c) of SCC with RA. A total of 515 samples were collected from various published papers, randomly splitting into training, validation, and testing with percentages of 70, 10 and 20. Two statistical indicators, correlation coefficient (R) and mean squared error (MSE), were used to assess the models; the greater the R and lower the MSE, the more accurate the algorithm. The findings demonstrate the higher accuracy of the three models. The best result is achieved by BR (R = 0.91 and MSE = 43.755), while the accuracy of LM is nearly the same (R = 0.90 and MSE = 48.14). LM processes the network in a much shorter time than BR. As a result, LM and BR are the best models in forecasting the 28 days f’c of SCC having RA. The sensitivity analysis showed that cement (28.39%) and water (23.47%) are the most critical variables for predicting the 28-day compressive strength of SCC with RA, while coarse aggregate contributes the least (9.23%).

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Section: Introductionmentioning

confidence: 99%

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

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“…They are made up of a network of artificial neurons that communicate with one another and analyze data using a connectionist approach to computation. They are primarily employed to simulate complicated input-output interactions or data patterns in data [14]. Training, validation, and testing are the three phases of ANNs.…”

Section: Data Visualizationmentioning

confidence: 99%

“…SCC is considered innovative because it can easily be used in congested areas where concreting is not easy. In SCC, noise pollution reduces and improves the filling capability and enhances the construction speed [13][14][15]. The population is growing at an alarming rate worldwide, along with the adoption and implementation of new concrete design technologies, resulting in increased resource consumption and environmental degradation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

et al. 2022

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The composition of self-compacting concrete (SCC) contains 60–70% coarse and fine aggregates, which are replaced by construction waste, such as recycled aggregates (RA). However, the complexity of its structure requires a time-consuming mixed design. Currently, many researchers are studying the prediction of concrete properties using soft computing techniques, which will eventually reduce environmental degradation and other material waste. There have been very limited and contradicting studies regarding prediction using different ANN algorithms. This paper aimed to predict the 28-day splitting tensile strength of SCC with RA using the artificial neural network technique by comparing the following algorithms: Levenberg–Marquardt (LM), Bayesian regularization (BR), and Scaled Conjugate Gradient Backpropagation (SCGB). There have been very limited and contradicting studies regarding prediction by using and comparing different ANN algorithms, so a total of 381 samples were collected from various published journals. The input variables were cement, admixture, water, fine and coarse aggregates, and superplasticizer; the data were randomly divided into three sets—training (60%), validation (10%), and testing (30%)—with 10 neurons in the hidden layer. The models were evaluated by the mean squared error (MSE) and correlation coefficient (R). The results indicated that all three models have optimal accuracy; still, BR gave the best performance (R = 0.91 and MSE = 0.2087) compared with LM and SCG. BR was the best model for predicting TS at 28 days for SCC with RA. The sensitivity analysis indicated that cement (30.07%) was the variable that contributed the most to the prediction of TS at 28 days for SCC with RA, and water (2.39%) contributed the least.

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“…Concrete is described as a composite material that can last for many decades, if not hundreds, with little or no maintenance (Saeed et al, 2022a;Hakeem et al, 2022b;Qaidi et al, 2022b;Zeybek et al, 2022). Standard concrete is composed of cement, coarse aggregate, and fine aggregate without reinforcing steel (Hakeem et al, 2022a;Althoey et al, 2022b). Modifications to the components and mixture design of ordinary cement concrete can be made to develop several forms of concrete that are acceptable for a variety of environmental circumstances and structural loads (Anas et al, 2022c;Anas et al, 2022f).…”

Section: Introductionmentioning

confidence: 99%

Effect of thermal cycles on the engineering properties and durability of sustainable fibrous high-strength concrete

Hakeem

Hosen²,

Alyami³

et al. 2023

Front. Mater.

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In this research, the effect of heat–cool cycles (HCCs) on high-strength concrete (HSC) containing steel fibres (SFs), polypropylene fibres (PPFs), and date palm fibres (DPFs), which were named fibrous high-strength concrete (FHSC), was studied. To produce FHSC, three doses of 0.2, 0.6, and 1 percent of each fibre were used. All samples were tested after 28 days of normal water curing and 270 days of exposure to HCCs (continuing the authors’ project and research published at 28 and 180 days). This entails heating for 2 days at 60 C in the oven and cooling for another 2 days at room temperature for 270 days. The experiment’s findings revealed that fibre reinforcement in concrete enhances its strength and durability. By incorporating the three types of fibres into high-strength concrete, with and without HCCs, the modulus of rupture was significantly increased. In both conditions, including with or without the implementation of HCCs, incorporating the three fibre types into the HSC showed a significant increase in toughness. As a result, natural date palm fibres can produce sustainable FHSC that can withstand harsh environmental conditions. Moreover, compared to the previous study conducted by the authors at 180 days, there is a slight severity in both the pattern of decrease and increase of the studied characteristics at 270 days caused by the effect of thermal cycles and fibres.

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Impact of sulfate activation of rice husk ash on the performance of high strength steel fiber reinforced recycled aggregate concrete

Cited by 30 publications

References 55 publications

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

A Study on the Prediction of Compressive Strength of Self-Compacting Recycled Aggregate Concrete Utilizing Novel Computational Approaches

Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods

Effect of thermal cycles on the engineering properties and durability of sustainable fibrous high-strength concrete

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