Relationship between macro-properties and amorphous gel of FA-based AAMs with different curing conditions after elevated temperature

Zhang, Dawang; Sun, Xue-Mei; Li, Hui

doi:10.1016/j.ceramint.2023.02.113

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…Increasing the curing temperature was found to positively impact the compressive strength (CS) of polymer composites. Similary, Zhang et al 97 also documented a comparable effect. The findings of this study were obtained by analyzing the inputs and dataset size used in the SHAP analysis.…”

Section: Shapley Analysismentioning

confidence: 55%

Estimation of compressive strength of waste concrete utilizing fly ash/slag in concrete with interpretable approaches: optimization and graphical user interface (GUI)

Dodo,

Arif,

Alyami

et al. 2024

Sci Rep

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Geo-polymer concrete has a significant influence on the environmental condition and thus its use in the civil industry leads to a decrease in carbon dioxide (CO2) emission. However, problems lie with its mixed design and casting in the field. This study utilizes supervised artificial-based machine learning algorithms (MLAs) to anticipate the mechanical characteristic of fly ash/slag-based geopolymer concrete (FASBGPC) by utilizing AdaBoost and Bagging on MLPNN to make an ensemble model with 156 data points. The data consist of GGBS (kg/m3), Alkaline activator (kg/m3), Fly ash (kg/m3), SP dosage (kg/m3), NaOH Molarity, Aggregate (kg/m3), Temperature (°C) and compressive strength as output parameter. Python programming is utilized in Anaconda Navigator using Spyder version 5.0 to predict the mechanical response. Statistical measures and validation of data are done by splitting the dataset into 80/20 percent and K-Fold CV is employed to check the accurateness of the model by using MAE, RMSE, and R2. Statistical analysis relies on errors, and tests against external indicators help determine how well models function in terms of robustness. The most important factor in compressive strength measurements is examined using permutation characteristics. The result reveals that ANN with AdaBoost is outclassed by giving maximum enhancement with R2 = 0.914 and shows the least error with statistical and external validations. Shapley analysis shows that GGBS, NaOH Molarity, and temperature are the most influential parameter that has significant content in making FASBGPC. Thus, ensemble methods are suitable for constructing prediction models because of their strong and reliable performance. Furthermore, the graphical user interface (GUI) is generated through the process of training a model that forecasts the desired outcome values when the corresponding inputs are provided. It streamlines the process and provides a useful tool for applying the model's abilities in the field of civil engineering.

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Section: Shapley Analysismentioning

confidence: 55%