Experimental Evaluation of the Effect of Silica Fume on Compressive, Tensile Strength, Abrasion Resistance, Slump and Impact Test and Water Permability Coefficient of Concrete

Esmailpour, Masoud; Rahmani, Kamal; Piroti, Saber

doi:10.2478/jaes-2018-0004

Cited by 3 publications

(3 citation statements)

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“…The silica fume concrete (SFC) database was built up from 22 internationally published studies available in the literature [ 6 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ]. The frequency distribution and statistical description of the database contain 283 compressive tests (f’c), as shown in Figure 5 .…”

Section: Methods and Modelingmentioning

confidence: 99%

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

et al. 2021

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Silica fume (SF) is a frequently used mineral admixture in producing sustainable concrete in the construction sector. Incorporating SF as a partial substitution of cement in concrete has obvious advantages, including reduced CO2 emission, cost-effective concrete, enhanced durability, and mechanical properties. Due to ever-increasing environmental concerns, the development of predictive machine learning (ML) models requires time. Therefore, the present study focuses on developing modeling techniques in predicting the compressive strength of silica fume concrete. The employed techniques include decision tree (DT) and support vector machine (SVM). An extensive and reliable database of 283 compressive strengths was established from the available literature information. The six most influential factors, i.e., cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume, were considered as significant input parameters. The evaluation of models was performed by different statistical parameters, such as mean absolute error (MAE), root mean squared error (RMSE), root mean squared log error (RMSLE), and coefficient of determination (R2). Individual and ensemble models of DT and SVM showed satisfactory results with high prediction accuracy. Statistical analyses indicated that DT models bested SVM for predicting compressive strength. Ensemble modeling showed an enhancement of 11 percent and 1.5 percent for DT and SVM compressive strength models, respectively, as depicted by statistical parameters. Moreover, sensitivity analyses showed that cement and water are the governing parameters in developing compressive strength. A cross-validation technique was used to avoid overfitting issues and confirm the generalized modeling output. ML algorithms are used to predict SFC compressive strength to promote the use of green concrete.

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Section: Methods and Modelingmentioning

confidence: 99%

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

et al. 2021

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“…4) The pH of the used water is 6 -7, and it is from Local Water Company (PDAM) water. These material characteristics oare important related to the increase in traffic intensity, the level of dynamic loads, and the axle loads of heavy vehicles [2].…”

Section: Materials and Equipmentmentioning

confidence: 99%

“…

with peat, clay, and hard soil.

Material and equipmentIn the production of porous concrete, the following materials are employed: The kind of cement utilized is Portland Composit Cement (PCC).1) Maximum particle size for coarse aggregate is between 0.5 / 0.5 and 1.0 / 1.0 cm.2) Superplasticizer, specifically LN Sikamen, with a maximum dose of 0.1 -1.2 % cement.3) Dosage Silica Fume 3 % cement.4) The pH of the used water is 6 -7, and it is from Local Water Company (PDAM) water. These material characteristics oare important related to the increase in traffic intensity, the level of dynamic loads, and the axle loads of heavy vehicles [2].A compressive testing machine, a Los Angeles machine, a shieve shaker machine, a bearing block, a slump tool, a permeability equipment, cylinder molds, material ovens, mixing machines (mixer), scales, organic plates, loading test equipment, and other auxiliary equipment were used in this test.

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confidence: 99%

The Porous Concrete for Rigid Pavement

Supriyadi

Sutandar

2023

Civil and Environmental Engineering

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Roads play an important role as a connection between locations and can facilitate the efficient mobility of people's economic activities. Five tests are involved in evaluating of the porous concrete, such as the slump test, compressive strength test, permeability test, flexure test, and load capacity test (loading test). Meanwhile, there are three types of soil: peat, clay, and hard. The compressive strength test measured an average compressive strength between 24.17-27.36 MPa. According to the permeability test, the average speed of water infiltration on the porous concrete rigid pavement was 20 seconds on clay soil and 55.344 seconds on peat soil, with large areas drained on porous concrete rigid pavement of 346.185 cm2 with 5 litres of water used, whereas it was 6.554 seconds with a flow area of 288.75 cm2 and 2 litres of water used on hard soil. The Loading Test was conducted with a 10-ton. The maximum difference in deflection was 1-2 cm. In addition, there were no cracks in the porous concrete rigid pavement with a porous concrete rigid pavement thickness of 15 cm in three different soil conditions. A track with porous concrete rigid pavement on clay, hard soil, or peat soil can accommodate a maximum payload of 10 tons.

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Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

et al. 2021

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Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases’ features to promote the usage of green concrete.

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Experimental Evaluation of the Effect of Silica Fume on Compressive, Tensile Strength, Abrasion Resistance, Slump and Impact Test and Water Permability Coefficient of Concrete

Cited by 3 publications

References 20 publications

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques

The Porous Concrete for Rigid Pavement

Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

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