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Volume 15
Issue 23
10.3390/app152312383
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

AI-Based Inference System for Concrete Compressive Strength: Multi-Dataset Analysis of Optimized Machine Learning Algorithms

by
Carlos Eduardo Olvera-Mayorga
1,
Manuel de Jesús López-Martínez
1,*,
José A. Rodríguez-Rodríguez
2,
Sodel Vázquez-Reyes
3,4,
Luis O. Solís-Sánchez
4,
José I. de la Rosa-Vargas
3,
David Duarte-Correa
5,
José Vidal González-Aviña
6 and
Carlos A. Olvera-Olvera
1,*
1
Laboratorio de Invenciones Aplicadas a la Industria (LIAI), Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98600, Zacatecas, Mexico
2
Laboratorio de Resistencia de Materiales y Mecánica de Suelos, Unidad Académica de Ingeniería I, Universidad Autónoma de Zacatecas, Zacatecas 98600, Zacatecas, Mexico
3
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98600, Zacatecas, Mexico
4
Posgrados de Ingeniería y Tecnología Aplicada, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98600, Zacatecas, Mexico
5
Tlachia Systems, Av. Felipe Carrillo Puerto 1001, Querétaro 76120, Querétaro, Mexico
6
Escuela de Ingeniería y Ciencias, Tecnologico de Monterrey, Av. General Ramon Corona 2514, Zapopan 45138, Jalisco, Mexico
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(23), 12383; https://doi.org/10.3390/app152312383
Submission received: 21 October 2025 / Revised: 17 November 2025 / Accepted: 20 November 2025 / Published: 21 November 2025
Versions Notes

Abstract

The prediction of concrete compressive strength (CSMPa) is fundamental in experimental civil engineering as it enables the optimization of mix design and complements laboratory testing through predictive tools. This study presents a systematic and reproducible methodology for comparing eight regression algorithms—including linear models, neural networks, and boosting methods—applied to three experimental datasets that represent different types of concrete: high-performance concrete (HPC), conventional concrete, and recycled-aggregate concrete (RAC). In order to make such comparison, some performance metrics were calculated (RMSE, MAE, MAPE, R2, and nRMSE) through hyperparameter optimization using RandomizedSearchCV and homogeneous cross-validation. The boosting methods achieved the best performance, with CatBoost standing out by reaching R2 values between 0.92 and 0.95 and RMSE between 3.4 and 4.4 MPa, confirming its inter-dataset stability and generalization capability. These results indicate consistent predictive accuracy across concretes of different compositions and production contexts. As an applied contribution, three interactive inference systems were developed in Google Colab to estimate CS from mix parameters, promoting reproducibility, open access, and practical use in quality-control processes.
Keywords: concrete compressive strength; machine learning; multi-dataset analysis; hyperparameter optimization; gradient boosting; neural networks; inference system concrete compressive strength; machine learning; multi-dataset analysis; hyperparameter optimization; gradient boosting; neural networks; inference system

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MDPI and ACS Style

Olvera-Mayorga, C.E.; López-Martínez, M.d.J.; Rodríguez-Rodríguez, J.A.; Vázquez-Reyes, S.; Solís-Sánchez, L.O.; de la Rosa-Vargas, J.I.; Duarte-Correa, D.; González-Aviña, J.V.; Olvera-Olvera, C.A. AI-Based Inference System for Concrete Compressive Strength: Multi-Dataset Analysis of Optimized Machine Learning Algorithms. Appl. Sci. 2025, 15, 12383. https://doi.org/10.3390/app152312383

AMA Style

Olvera-Mayorga CE, López-Martínez MdJ, Rodríguez-Rodríguez JA, Vázquez-Reyes S, Solís-Sánchez LO, de la Rosa-Vargas JI, Duarte-Correa D, González-Aviña JV, Olvera-Olvera CA. AI-Based Inference System for Concrete Compressive Strength: Multi-Dataset Analysis of Optimized Machine Learning Algorithms. Applied Sciences. 2025; 15(23):12383. https://doi.org/10.3390/app152312383

Chicago/Turabian Style

Olvera-Mayorga, Carlos Eduardo, Manuel de Jesús López-Martínez, José A. Rodríguez-Rodríguez, Sodel Vázquez-Reyes, Luis O. Solís-Sánchez, José I. de la Rosa-Vargas, David Duarte-Correa, José Vidal González-Aviña, and Carlos A. Olvera-Olvera. 2025. "AI-Based Inference System for Concrete Compressive Strength: Multi-Dataset Analysis of Optimized Machine Learning Algorithms" Applied Sciences 15, no. 23: 12383. https://doi.org/10.3390/app152312383

APA Style

Olvera-Mayorga, C. E., López-Martínez, M. d. J., Rodríguez-Rodríguez, J. A., Vázquez-Reyes, S., Solís-Sánchez, L. O., de la Rosa-Vargas, J. I., Duarte-Correa, D., González-Aviña, J. V., & Olvera-Olvera, C. A. (2025). AI-Based Inference System for Concrete Compressive Strength: Multi-Dataset Analysis of Optimized Machine Learning Algorithms. Applied Sciences, 15(23), 12383. https://doi.org/10.3390/app152312383

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