Experimental and analytical study of hybrid fiber reinforced concrete prepared with basalt fiber under high temperature

Khan, Mehran; Cao, Mingli; Xie, Chi; Ali, Majid

doi:10.1002/fam.2968

Cited by 88 publications

(48 citation statements)

References 71 publications

(105 reference statements)

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“…Sensitivity analysis generated outcomes related to the number of input parameters and data points employed to construct the models. The influence of an input parameter on the technique's output was determined using Equations ( 4) and (5).…”

Section: Sensitivity Analysismentioning

confidence: 99%

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang

Ahmad

et al. 2022

Polymers

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Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R2), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R2 values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.

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Section: Sensitivity Analysismentioning

confidence: 99%

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Wang

Ahmad

et al. 2022

Polymers

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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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“…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 ].…”

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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Experimental and analytical study of hybrid fiber reinforced concrete prepared with basalt fiber under high temperature

Cited by 88 publications

References 71 publications

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

Application of Soft Computing Techniques to Predict the Strength of Geopolymer Composites

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

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

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