Development of a hybrid stacked machine learning model for predicting compressive strength of high-performance concrete

Tipu, Rupesh Kumar; Suman, Suman; Batra, Vandna

doi:10.1007/s42107-023-00689-z

Cited by 12 publications

(2 citation statements)

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“…Through training on comprehensive datasets, these models learn complex patterns and relationships between material characteristics and their respective strength [25]. The scientific literature documents cases where ML models achieve coefficients of determination (R 2 ) exceeding 0.90 [23,30,31], mostly using In the case of six predictive variables compared to using eight predictive variables, we observed numerically lower values, highlighting the significance of specific physical characteristics of fly ash and fine aggregate excluded from the analysis. Fly ash plays a crucial role in concrete by enhancing cohesion, reducing exudation and segregation, and extending the setting time of fresh concrete [14].…”

Section: Overall Performance Of Machine Learning Modelsmentioning

confidence: 79%

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Comparing the performance of machine learning models for predicting the compressive strength of concrete

Loureiro,

Stefani

2024

Discov Civ Eng

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This work aimed to investigate and compare the performance of different machine learning models in predicting the compressive strength of concrete using a data set of 1234 compressive strength values. The predictive variables were selected based on their relevance using the SelectKBest method, resulting in an analysis of eight and six predictive variables. The evaluation was conducted through linear correlation studies via simple linear regression and non-linear correlation studies using support vector regression (SVR), random forest (RF), gradient boosting (GB), and artificial neural networks (ANN). The results showed a coefficient of determination (R2) = 0.897 and a root mean square error (RMSE) = 6.535 MPa for SVR, R2 = 0.885 and RMSE = 5.437 MPa for GB, R2 = 0.868 and RMSE = 5.859 MPa for GB and R2 = 0.894 and RMSE = 5.192 MPa for ANN, all for test set and eight predictor variables. The comparison between the machine learning methods revealed significant differences. For instance, ANN stood out with a higher R2 value, demonstrating its remarkable ability to explain the variability in the data. ANN also showed the lowest RMSE value, indicating notable accuracy in the predictions. Although ANN has demonstrated higher performance, GB shows a closer performance, which no differences from a practical application. The choice between these approaches depends on considerations regarding the balance between explainability and accuracy. While GB provides a more in-depth understanding of the relationship between variables, ANN stands out for the accuracy of its predictions.

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Section: Overall Performance Of Machine Learning Modelsmentioning

confidence: 79%

“…| https://doi.org/10.1007/s44290-024-00022-w Research experimental assays found the same influence of concentration and type of additives in compressive strength of concrete [16,21,23,47].…”

Section: Discussionmentioning

confidence: 92%