Uniaxial Tensile Behavior, Flexural Properties, Empirical Calculation and Microstructure of Multi-Scale Fiber Reinforced Cement-Based Material at Elevated Temperature

Li, Li; Khan, Mehran; Bai, Chengying; Shi, Ke

doi:10.3390/ma14081827

Cited by 62 publications

(46 citation statements)

References 44 publications

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“…Concrete is one of the most frequently used materials in construction worldwide [ 1 , 2 , 3 , 4 , 5 , 6 ]. The universality of concrete is due to a number of factors, including their ease of acquisition, resistance to water and heat, and adaptability to a variety of sizes and shapes [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. However, the production of concrete and its ingredients require a considerable amount of energy, consume natural resources, and release CO 2 [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

Ahmad

Ostrowski

et al. 2021

Materials

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The casting and testing specimens for determining the mechanical properties of concrete is a time-consuming activity. This study employed supervised machine learning techniques, bagging, AdaBoost, gene expression programming, and decision tree to estimate the compressive strength of concrete containing supplementary cementitious materials (fly ash and blast furnace slag). The performance of the models was compared and assessed using the coefficient of determination (R2), mean absolute error, mean square error, and root mean square error. The performance of the model was further validated using the k-fold cross-validation approach. Compared to the other employed approaches, the bagging model was more effective in predicting results, with an R2 value of 0.92. A sensitivity analysis was also prepared to determine the level of contribution of each parameter utilized to run the models. The use of machine learning (ML) techniques to predict the mechanical properties of concrete will be beneficial to the field of civil engineering because it will save time, effort, and resources. The proposed techniques are efficient to forecast the strength properties of concrete containing supplementary cementitious materials (SCM) and pave the way towards the intelligent design of concrete elements and structures.

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Section: Introductionmentioning

confidence: 99%

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

Ahmad

Ostrowski

et al. 2021

Materials

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“…This indicated that the mechanism of the PP fibre action in geopolymer and Portland cement-based composites may be different. In addition, the absolute values of slope of Equations ( 8) and ( 9) were higher than those of Equations ( 6) and (7), respectively. This indicated that in a high w/b matrix, the fibres were more effective in decreasing the flowability of the geopolymer.…”

Section: Workabilitiesmentioning

confidence: 79%

“…Nevertheless, as Portland cement-based composites, geopolymers display brittle failure resulting from their low tensile strength that could enforce some restrictions in potential engineering application. Conventionally, Portland cement-based composites are reinforced by the inclusion of fibres manufacturing concrete with a toughness performance [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Hence, different kinds of fibres have been used to reinforce geopolymers [ 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Influence of Polypropylene Fibre Factor on Flowability and Mechanical Properties of Self-Compacting Geopolymer

Liu

et al. 2021

Materials

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The possibility of using geopolymer instead of Portland cement could effectively reduce carbon dioxide emissions from cement manufacturing. Fibre-reinforced self-compacting geopolymers have great potential in civil engineering applications, such as chord member grouting for concrete-filled steel tubular truss beams. However, to the best of the authors’ knowledge, the quantitative relationship between FF and the properties of the fibre-reinforced geopolymer has been rarely reported. In this research, 26 groups of mixtures were used to study the influence of the polypropylene fibre factor (FF) on the flowability and mechanical properties and also the compactness of the fibre-reinforced self-compacting geopolymer. At the same volume fraction, geopolymers with long fibres present worse flowability than those having short fibres due to the easier contacting of long fibres. By growing the FF the influence of fibre addition on the V-funnel flow rate is more significant than the slump spread. This can be ascribed to the consequence of fibre addition and friction by the V-funnel which estimates the restrained deformability. For FF lesser than critical factor Fc = 100, influence of fibres is negligible and fibres are far apart from each other and, thus, they cannot restrict cracking under load and transfer the load to improve the mechanical properties. For FF between the Fc = 100 and density factor Fd = 350, a noteworthy enhancement of mechanical properties was obtained and the geopolymer was still adequately workable to flow by weight of self, without any symbols of instability and fibre clumping. Under this condition, better fibre dispersal and reinforcing productivity can lead to better hardened properties. For FF higher than Fd = 350, fibres tend to come to be entwined together and form clumping resulting from the fibre balling, resulting in worse hardened properties. This research offers a sensible basis for the application of the workability regulator of the fresh properties of fibre-reinforced geopolymer as an operative way to basically obtain ideal mechanical properties.

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“…The development of new materials and methods for protecting against high temperatures has gained more importance in the field of research due to the increased number of incidents caused by fire [ 13 , 14 ]. The effect of fire is considered as a high frequency disaster, which not only causes the deterioration of cement composites, but also plays a role in the spalling of such material [ 15 , 16 ]. The paper [ 17 ] indicated that the resistance of a structure against the impact of high temperature caused by fire is one of the critical factors that influence the safety of using structures.…”

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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Uniaxial Tensile Behavior, Flexural Properties, Empirical Calculation and Microstructure of Multi-Scale Fiber Reinforced Cement-Based Material at Elevated Temperature

Cited by 62 publications

References 44 publications

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

Influence of Polypropylene Fibre Factor on Flowability and Mechanical Properties of Self-Compacting Geopolymer

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

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