Prediction of mechanical properties of rubberised concrete exposed to elevated temperature using ANN

Gupta, Trilok; Patel, K. A.; Siddique, Salman; Sharma, Ravi Kumar; Chaudhary, Sandeep

doi:10.1016/j.measurement.2019.106870

Cited by 107 publications

(36 citation statements)

References 47 publications

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“…Sensitive analysis is a type of uncertainty analysis employed to explain the results of ML according to the analysis of the impact of changed inputs on the outputs. It is significant for sensitive analysis to explore the relationship between the changes in the number of inputs and the output [ 57 ]. In this paper, the impact of 16 inputs on HP is analysed utilising ML models with GSA-GBR.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

et al. 2021

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Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

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Section: Resultsmentioning

confidence: 99%

Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

et al. 2021

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“…The gathered database of the considered 302 cases is first examined using Principal Components Analysis to test the variability of the parameters, and then, the input and output parameters (as previously shown in Table I) were selected for ANN modeling. Artificial Neural Networks were built following the procedure which was the most efficient as per the previous studies on a similar subject [13,14,[32][33][34][35]. A multilayer perceptron model consisting of three layers was used for building the network, while constantly minimizing the differences between the network predicted and experimental results.…”

Section: Statistical Analysis and Artificial Neural Network Modelingmentioning

confidence: 99%

An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

et al. 2021

Self Cite

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This study analyzed the last 20 years` data available on power plant coal ashes used in clay brick production. The statistical analysis has been carried out for a total of 302 cases based on the relevant parameters reported in the literature. The chemical composition of the clays and coal ashes, percentage incorporation and maximum particle size of ash, size of fired samples, peak firing temperature, and the corresponding soaking time were selected as inputs for modeling. The product characteristics i.e. open porosity, water absorption, and compressive strength was taken as output parameters. An artificial neural network model has been developed and showed a satisfactory fit to experimental data and predicted the observed output variables with the overall coefficient of determination (r2) of 0.972 during the training period. Besides, the reduced chi-square, mean bias error, root mean square error, and mean percentage error were utilized to check the correctness of the obtained model, which proved the network generalization capability. The sensitivity analysis of the model suggested that the quantity of Na2O coming from brick clays, the percentages of SiO2 and K2O coming from ashes, and MgO coming from clays were the most influential parameters in descending order for the ash-clay composite bricks` quality, mostly owing to the influence of fluxes during firing.

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“…Vivian W. Y. Dantas, et al [1] 0.9710 Hadzima-Nyarko, et al [49] 0.9779 Ghorbani, et al [30] 0.9900 Deshpande, et al [16] 0.9500 Gupta, et al [50] 0.9924 Ray, et al [43] 0.9763…”

Section: Declarations Author Contributionsmentioning

confidence: 99%

A Prediction Model for CO 2 Concrete using Regression Analysis and Artificial Neural Networks

Tam¹,

Butera²,

Lê³

et al. 2021

Preprint

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Concrete is a very effective material for the construction of buildings and infrastructure around the world. Unfortunately, typical concrete is a large contributor to CO2 emissions and consumption of natural reserves. CO2 Concrete allows the mitigation of these downfalls by carbonating recycled aggregate, reducing CO2 emissions, reusing crushed masonry materials and conserving virgin aggregate. CO2 Concrete can also be considered reliable as its compressive strength can be accurately predicted by both regression analysis and artificial neural networks. The artificial neural network created for this paper allow accurate prediction of the compressive strength for CO2 Concrete. The artificial neural network exhibited a strong relationship with the experimental specimens, revealing a multiple R of 0.98 and an R square of 0.95. The artificial neural network was also validated by 22 laboratory validation concrete mixes. The artificial neural network displayed an average error of 1.24 MPa or 3.43% in the validation mixes with 59% of concrete samples within 3% error and 77% being within 5% error. The successful prediction of compressive strength of CO2 Concrete can help a greater mainstream use of the green material.

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Prediction of mechanical properties of rubberised concrete exposed to elevated temperature using ANN

Cited by 107 publications

References 47 publications

Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

An artificial neural network-based prediction model for utilization of coal ash in production of fired clay bricks: A review

A Prediction Model for CO 2 Concrete using Regression Analysis and Artificial Neural Networks

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