Prediction of the self-healing properties of concrete modified with bacteria and fibers using machine learning

Pessoa, Carolina Luiza Emerenciana; Peres Silva, Victor Hugo; Stefani, Ricardo

doi:10.1007/s42107-023-00878-w

Cited by 5 publications

(2 citation statements)

References 46 publications

(39 reference statements)

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“…Hence, to determine the influence of each concrete component (variable) in the compressive strength, we used the permutation_importance function at the sklearn.inspection package. Details on the methodology are described elsewhere [22].…”

Section: Influence Of Independent Variable Sensivitymentioning

confidence: 99%

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: Influence Of Independent Variable Sensivitymentioning

confidence: 99%

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

Loureiro,

Stefani

2024

Discov Civ Eng

Self Cite

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show abstract

“…Moreover, to determine the in uence of each concrete component (variable) on the self-healing capacity, we used the permutation_importance function in the sklearn.inspection package. Details on the methodology are described elsewhere (Pessoa et al 2024). Finally, each model was tested with real data to ensure that each model correctly predicted the concrete self-healing capacity.…”

Section: Performance Comparison Of ML Modelsmentioning

confidence: 99%

On the use of Synthetic Data for Machine Learning prediction of Self-Healing Capacity of Concrete

de Oliveira,

Stefani

2024

Preprint

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This work investigated the use of synthetic data to overcome the limitations of scarce experimental data in predicting the self-healing capacity of bacteria-driven concrete. We generated a synthetic dataset based on real-world data, significantly expanding the original dataset and then trained and compared machine learning models, including probabilistic and ensemble methods, to predict the concrete self-healing capacity. The results demonstrate that the ensemble methods, particularly the random forest (RF) method (accuracy = 0.863 and F1-score = 0.863), outperformed the probabilistic models and achieved high accuracy in predicting self-healing capacity. The trained models were further applied to real-word data examples, showing high accuracy. This research validates the utility of synthetic data in predicting modelling accuracy and reliability in civil engineering, particularly in areas with limited experimental data. The findings contribute to the growing use of ML and AI in concrete research and demonstrate the transformative potential of synthetic data in addressing challenges in civil engineering.

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Predicting the crack repair rate of self-healing concrete using soft-computing tools

Lou,

Wang,

Amin

et al. 2024

Materials Today Communications

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Prediction of the self-healing properties of concrete modified with bacteria and fibers using machine learning

Cited by 5 publications

References 46 publications

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

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

On the use of Synthetic Data for Machine Learning prediction of Self-Healing Capacity of Concrete

Predicting the crack repair rate of self-healing concrete using soft-computing tools

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