Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming

Iqbal, Muhammad Farjad; Javed, Muhammad Faisal; Rauf, Momina; Azim, Iftikhar; Ashraf, Muhammad; Yang, Jian; Liu, Qingfeng

doi:10.1016/j.scitotenv.2021.146524

Cited by 120 publications

(51 citation statements)

References 66 publications

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“…The MAE, RMSE, RRMSE, RSE, and ρ for the training set are 3.33, 4.45, 0.017, 0.16, and 0.1, respectively. The MAE is less than the MAE, showing the smaller percentage of larger error values [85]. In addition, the percentage of RRMSE is less than 10% along with ρ less than 0.2 [86].…”

Section: Ann Prediction Modelmentioning

confidence: 85%

“…The performance index is the ultimate metric for performance evaluation, as it combines the effect of both error statistics and goodness of fit. It is recommended in the literature that the performance index and RRMSE% should be less than 0.2 and 10%, respectively, to classify the model as good [85,86]. The formulas for calculating the mentioned metrics of performance assessment are as follows.…”

Section: Performance Evaluation Of Prediction Modelsmentioning

confidence: 99%

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Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

et al. 2021

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Forecasting the compressive strength of concrete is a complex task owing to the interactions among concrete ingredients. In addition, an important characteristic of the concrete failure surface is its six-fold symmetry. In this study, an artificial neural network (ANN) and adaptive neuro fuzzy interface system (ANFIS) were employed to model the compressive strength of natural volcanic ash mortar (VAM) by using the six-fold symmetry of concrete failure. The modeling was correlated with four parameters. To train and test the projected models, data for more than 150 samples were collected from the literature. Furthermore, mortar samples with varying proportions of volcanic ash were prepared in the laboratory and tested, and the results were used to validate the models. The performance of the developed models was assessed using numerous statistical measures. The results show that both the ANN and ANFIS models accurately predict the compressive strength of VAM with R-square above 0.9 and lower error statistics. The permutation feature analysis confirmed that the age of specimens affects the strength of VAM the most, followed by the water-to-cement ratio, curing temperature, and percentage of volcanic ash.

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Section: Ann Prediction Modelmentioning

confidence: 85%

Section: Performance Evaluation Of Prediction Modelsmentioning

confidence: 99%

Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

et al. 2021

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“…The properties of CAs, such as apparent density, particle size distribution and appearance shape, may all have a certain influence on the degree of settlement [12,38,[59][60][61][62][63][64][65][66]. Considering that the laboratory could offer additional CAs with apparent densities of 2520 kg/m 3 and 2790 kg/m 3 , a numerical model of the influence of apparent density on settlement was carried out, and the vibration time was set as 25 s. From Fig.…”

Section: Influence Of Ca Apparent Densitymentioning

confidence: 99%

An experimental and numerical investigation of coarse aggregate settlement in fresh concrete under vibration

Cai¹,

Liu²,

Yu³

et al. 2021

Cement and Concrete Composites

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“…The final datasets included a total of 132, 125, and 128 records of compressive strength

, splitting tensile strength

and flexural strength

respectively, for concrete incorporated with bagasse ash (BA). As some researchers follow the British standard during experimental testing of the compressive strength (

) for concrete, the cube strength data was converted to cylindrical strength to make the data uniform [ 4 , 68 ]. Once all the data was collected and properly arranged, statistical analysis was applied to identify the most important and effective parameters that considerably influence the performance of BAC.…”

Section: Modeling Techniques and Databasementioning

confidence: 99%

“…The construction sector uses a third of the total energy production and emits a large amount of greenhouse gases into the atmosphere [ 1 ]. Concrete is a widely used material that emits 0.13 tons of CO 2 per ton of concrete produced [ 2 , 3 , 4 ]. The idea of green concrete is gaining popularity as a way to diminish the harmful impacts of concrete while still addressing the underlying problem.…”

Section: Introductionmentioning

confidence: 99%

Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete

et al. 2021

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The application of multiphysics models and soft computing techniques is gaining enormous attention in the construction sector due to the development of various types of concrete. In this research, an improved form of supervised machine learning, i.e., multigene expression programming (MEP), has been used to propose models for the compressive strength (f'c), splitting tensile strength (fSTS), and flexural strength (fFS) of sustainable bagasse ash concrete (BAC). The training and testing of the proposed models have been accomplished by developing a reliable and comprehensive database from published literature. Concrete specimens with varying proportions of sugarcane bagasse ash (BA), as a partial replacement of cement, were prepared, and the developed models were validated by utilizing the results obtained from the tested BAC. Different statistical tests evaluated the accurateness of the models, and the results were cross-validated employing a k-fold algorithm. The modeling results achieve correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE) above 0.8 each with relative root mean squared error (RRMSE) and objective function (OF) less than 10 and 0.2, respectively. The MEP model leads in providing reliable mathematical expression for the estimation of f'c, fSTS and fFS and of BA concrete, which can reduce the experimental workload in assessing the strength properties. The study’s findings indicated that MEP-based modeling integrated with experimental testing of BA concrete and further cross-validation is effective in predicting the strength parameters of BA concrete.

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Sustainable utilization of foundry waste: Forecasting mechanical properties of foundry sand based concrete using multi-expression programming

Cited by 120 publications

References 66 publications

Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

Modeling Compressive Strength of Eco-Friendly Volcanic Ash Mortar Using Artificial Neural Networking

An experimental and numerical investigation of coarse aggregate settlement in fresh concrete under vibration

Multigene Expression Programming Based Forecasting the Hardened Properties of Sustainable Bagasse Ash Concrete

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