Material design of economical ultra-high performance concrete (UHPC) and evaluation of their properties

Arora, Aashay; Almujaddidi, Asim; Kianmofrad, Farrokh; Mobasher, Barzin; Neithalath, Narayanan

doi:10.1016/j.cemconcomp.2019.103346

Cited by 116 publications

(35 citation statements)

References 42 publications

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“…Figure 3(b) shows that the incorporation of SF significantly reduces the 28 d porosity of UHSC. It is well known that the filling and pozzolanic effects of SF could increase the packing density of cement particles, improve the microstructure, and reduce the porosity of concrete [36,37].…”

Section: Porosity In Uhscmentioning

confidence: 99%

The Relationship between Cementitious Composition and Properties of Ultrahigh Strength Concrete

Wang

Zhang

2021

Advances in Civil Engineering

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It is well known that supplementary cementitious materials (SCMs) have obvious effects on the properties of concrete. In order to understand the relationship between cementitious materials and properties of ultrahigh strength concrete (UHSC), the cementitious compositions of UHSC were designed by the simple-centroid design method. The effects of cementitious compositions on the properties of UHSC were investigated. It was found that the incorporation of silica fume (SF) improved the flowability and strength of UHSC, but it decreased the time of acceleration period, calcium hydroxide (CH) content, and porosity of UHSC at a certain content. The incorporation of fly ash (FA) increased the flowability, time of acceleration period, and porosity of UHSC, but it decreased the strength and CH content of UHSC. The relationships between cement, silica fume, and fly ash and the properties of UHSC were calculated based on the simple-centroid design method.

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Section: Porosity In Uhscmentioning

confidence: 99%

The Relationship between Cementitious Composition and Properties of Ultrahigh Strength Concrete

Wang

Zhang

2021

Advances in Civil Engineering

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“…These various aspects enable it to be applied in the construction of infrastructures, dams, tunnels, bridges, and reservoirs [ 5 ]. The local availability of ingredients, such as coarse aggregate, fine aggregate, water, and binding material, significantly influences the economical factor [ 6 ]. In comparison, other building materials such as steel also possess many properties but cannot be cheaper than concrete.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

et al. 2021

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High temperature severely affects the nature of the ingredients used to produce concrete, which in turn reduces the strength properties of the concrete. It is a difficult and time-consuming task to achieve the desired compressive strength of concrete. However, the application of supervised machine learning (ML) approaches makes it possible to initially predict the targeted result with high accuracy. This study presents the use of a decision tree (DT), an artificial neural network (ANN), bagging, and gradient boosting (GB) to forecast the compressive strength of concrete at high temperatures on the basis of 207 data points. Python coding in Anaconda navigator software was used to run the selected models. The software requires information regarding both the input variables and the output parameter. A total of nine input parameters (water, cement, coarse aggregate, fine aggregate, fly ash, superplasticizers, silica fume, nano silica, and temperature) were incorporated as the input, while one variable (compressive strength) was selected as the output. The performance of the employed ML algorithms was evaluated with regards to statistical indicators, including the coefficient correlation (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual models using DT and ANN gave R2 equal to 0.83 and 0.82, respectively, while the use of the ensemble algorithm and gradient boosting gave R2 of 0.90 and 0.88, respectively. This indicates a strong correlation between the actual and predicted outcomes. The k-fold cross-validation, coefficient correlation (R2), and lesser errors (MAE, MSE, and RMSE) showed better performance than the ensemble algorithms. Sensitivity analyses were also conducted in order to check the contribution of each input variable. It has been shown that the use of the ensemble machine learning algorithm would enhance the performance level of the model.

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“…To compensate for the very high production cost of UHPCs, Karim et al [33] used masonry sand in place of expensive quartz sand and compared the results with those of the proprietary UHPCs. Further, Arora et al [34] combined coarse and fine aggregates using a compressible packing model to achieve a compressive strength of 150 MPa. To reduce the total cost, Yang et al [35] utilized supplementary cementitious material such as fly ash and slag as a partial replacement of cement.…”

Section: Introductionmentioning

confidence: 99%

Abrasion Resistance of Ultra-High-Performance Concrete for Railway Sleepers

Hasnat

Ghafoori

2021

Urban Rail Transit

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This study aimed to determine the abrasion resistance of ultra-high-performance concretes (UHPCs) for railway sleepers. Test samples were made with different cementitious material combinations and varying steel fiber contents and shapes, using conventional fine aggregate. A total of 25 UHPCs and two high-strength concretes (HSCs) were selected to evaluate their depth of wear and bulk properties. The results of the coefficient of variation (CV), relative gain in abrasion, and abrasion index of the studied UHPCs were also obtained and discussed. Furthermore, a comparison was made on the resistance to wear of the selected UHPCs with those of the HSCs typically used for prestressed concrete sleepers. The outcomes of this study revealed that UHPCs displayed excellent resistance against abrasion, well above that of HSCs. Amongst the utilized cementitious material combinations, UHPCs made with silica fume as a partial replacement of cement performed best against abrasion, whereas mixtures containing fly ash showed the highest depth of wear. The addition of steel fibers had a more positive influence on the abrasion resistance than it did on compressive strength of the studied UHPCs.

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Material design of economical ultra-high performance concrete (UHPC) and evaluation of their properties

Cited by 116 publications

References 42 publications

The Relationship between Cementitious Composition and Properties of Ultrahigh Strength Concrete

The Relationship between Cementitious Composition and Properties of Ultrahigh Strength Concrete

Comparative Study of Supervised Machine Learning Algorithms for Predicting the Compressive Strength of Concrete at High Temperature

Abrasion Resistance of Ultra-High-Performance Concrete for Railway Sleepers

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