Interpretation and performance in predictive models of concrete strength
DOI:
https://doi.org/10.47842/juts.v8i1.85Keywords:
concrete strength, principal component analysis, accuracy–interpretability trade-off, random forest, predictive modelingAbstract
Predictive modeling of concrete compressive strength presents a fundamental challenge: balancing the accuracy achieved by complex nonlinear models and the transparency offered by lineares approaches. This study addresses this issue through the analysis of a dataset containing 1,030 concrete mixtures. Principal Component Analysis (PCA) was first applied to extract interpretable latent factors. Subsequently, three models were developed and compared: (1) principal component regression using five components explaining 87.3% of the variance, (2) multiple linear regression with the original variables, and (3) a nonlinear Random Forest model. The results show that the Random Forest achieves the highest accuracy (R² = 0.88, RMSE = 5.52 MPa), quantifying the relevance of nonlinear interactions. The principal component regression model (R² = 0.48, RMSE = 11.58 MPa), although less accurate, provides insight into data structure, significant dimensionality reduction, and mitigates multicollinearity present in the multiple linear regression model (R² = 0.63, RMSE = 9.80 MPa). The findings highlight the need to incorporate interpretability techniques to deepen understanding of the key variables governing concrete strength.
Downloads
References
ABRAMS, D. A. Design of concrete mixtures. Chicago: Structural Materials Research Laboratory, Lewis Institute, Bulletin 1, 1919. 20 p. Disponível https://archive.org/details/designofconcrete00abrarich. Acesso em: 6 nov. 2025.
AHMAD, A.; AHMAD, W.; ASLAM, F.; JOYKLAD, P. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques. Case Studies in Construction Materials, v. 16, e00840, 2022. ISSN 2214-5095. DOI: https://doi.org/10.1016/j.cscm.2021.e00840 DOI: https://doi.org/10.1016/j.cscm.2021.e00840
AHMAD, M. R.; FERNÁNDEZ-JIMENEZ, A.; CHEN, B.; LENG, Z.; DAI, J.-G. Low-carbon cementitious materials: Scale-up potential, environmental impact and barriers. Construction and Building Materials, v. 455, art. 139087, 2024. DOI: https://doi.org/10.1016/j.conbuildmat.2024.139087. DOI: https://doi.org/10.1016/j.conbuildmat.2024.139087
ASTERIS, Panagiotis G.; SKENTOU, Athanasia D.; BARDHAN, Abidhan; SAMUI, Pijush; PILAKOUTAS, Kypros. Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research, v. 145, art. 106449, 2021. ISSN 0008-8846. DOI: 10.1016/j.cemconres.2021.106449. DOI: https://doi.org/10.1016/j.cemconres.2021.106449. DOI: https://doi.org/10.1016/j.cemconres.2021.106449
CHI, L. et al. Machine learning prediction of compressive strength of concrete with resistivity modification. Materials Today Communications, v. 36, art. 106470, 2023. DOI: https://doi.org/10.1016/j.mtcomm.2023.106470. DOI: https://doi.org/10.1016/j.mtcomm.2023.106470
ELHISHI, S.; ELASHRY, A. M.; EL-METWALLY, S. Unboxing machine learning models for concrete strength prediction using XAI. Scientific Reports, v. 13, art. 19892, 2023. DOI: https://doi.org/10.1038/s41598-023-47169-7. DOI: https://doi.org/10.1038/s41598-023-47169-7
JACKSON, D. A. Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology, v. 74, n. 8, p. 2204-2214, 1993. DOI: https://doi.org/10.2307/1939574. DOI: https://doi.org/10.2307/1939574
JIANG, Y.; ZHANG, J.; ZUO, W.; XU, G.; YUAN, C.; WANG, L.; DU, Z.; LU, Y.; SHE, W. Prediction of time-dependent concrete mechanical properties based on advanced deep learning models considering complex variables. Case Studies in Construction Materials, v. 21, art. e03629, 2024. DOI: https://doi.org/10.1016/j.cscm.2024.e03629. DOI: https://doi.org/10.1016/j.cscm.2024.e03629
JOLLIFFE, I. T. Principal Component Analysis. 2. ed. New York: Springer Science+Business Media, 2002. DOI: https://doi.org/10.1007/b98835. DOI: https://doi.org/10.1007/b98835
JUENGER, M. C. G.; WINNEFELD, F.; PROVIS, J. L.; IDEKER, J. H. Advances in alternative cementitious binders. Cement and Concrete Research, v. 41, n. 12, p. 1232-1243, 2011. DOI: https://doi.org/10.1016/j.cemconres.2010.11.012. DOI: https://doi.org/10.1016/j.cemconres.2010.11.012
KHODAPARASTI, M.; ALIJAMAAT, A.; POURAMINIAN, M. Prediction of the concrete compressive strength using improved random forest algorithm. Journal of Building Pathology and Rehabilitation, v. 8, art. 92, 2023. DOI: https://doi.org/10.1007/s41024-023-00337-8. DOI: https://doi.org/10.1007/s41024-023-00337-8
KOBAKA, J. Principal Component Analysis as a Statistical Tool for Concrete Mix Design. Materials, v. 14, n. 10, art. 2668, 2021. DOI: https://doi.org/10.3390/ma14102668. DOI: https://doi.org/10.3390/ma14102668
MEHTA, P. K. Concrete technology for sustainable development. Concrete International, Detroit, v. 21, n. 11, p. 47-53, nov. 1999.
MONTEIRO, H.; MOURA, B.; SOARES, N. Advancements in nano-enabled cement and concrete: Innovative properties and environmental implications. Journal of Building Engineering, v. 56, art. 104736, 2022. DOI: https://doi.org/10.1016/j.jobe.2022.104736. DOI: https://doi.org/10.1016/j.jobe.2022.104736
MURDOCH, W. J.; SINGH, C.; KUMBIER, K.; ABBASI-ASL, R.; YU, B. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America, v. 116, n. 44, p. 22071-22080, 2019. DOI: https://doi.org/10.1073/pnas.1900654116. DOI: https://doi.org/10.1073/pnas.1900654116
NIKOOPAYAN TAK, M. S.; FENG, Y.; MAHGOUB, M. Advanced Machine Learning Techniques for Predicting Concrete Compressive Strength. Infrastructures, v. 10, n. 2, art. 26, 2025. DOI: https://doi.org/10.3390/infrastructures10020026. DOI: https://doi.org/10.3390/infrastructures10020026
RIBEIRO, M. T.; SINGH, S.; GUESTRIN, C. "Why Should I Trust You?": Explaining the Predictions of Any Classifier. In: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (KDD '16), 2016, San Francisco. Anais... New York: ACM, 2016. p. 1135-1144. DOI: https://doi.org/10.1145/2939672.2939778. DOI: https://doi.org/10.1145/2939672.2939778
SNELLINGS, R.; SURANENI, P.; SKIBSTED, J. Future and emerging supplementary cementitious materials. Cement and Concrete Research, v. 171, art. 107199, 2023. DOI: https://doi.org/10.1016/j.cemconres.2023.107199. DOI: https://doi.org/10.1016/j.cemconres.2023.107199
UNIVERSITY OF CALIFORNIA, IRVINE. UCI Machine Learning Repository. Irvine: School of Information and Computer Sciences, 1987-. Disponível em: http://archive.ics.uci.edu/ . Acesso em: 6 nov. 2025.
WAN, Z.; XU, Y.; ŠAVIJA, B. On the use of machine learning models for prediction of compressive strength of concrete: influence of dimensionality reduction on the model performance. Materials, v. 14, n. 4, art. 713, 2021. DOI: https://doi.org/10.3390/ma14040713. DOI: https://doi.org/10.3390/ma14040713
YEH, I. C. Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research, v. 28, n. 12, p. 1797-1808, 1998. DOI: https://doi.org/10.1016/S0008-8846(98)00165-3. DOI: https://doi.org/10.1016/S0008-8846(98)00165-3
ZHANG, Y.; REN, W.; CHEN, Y.; MI, Y.; LEI, J.; SUN, L. Predicting the compressive strength of high-performance concrete using an interpretable machine learning model. Scientific Reports, v. 14, art. 28346, 2024. DOI: https://doi.org/10.1038/s41598-024-79502-z. DOI: https://doi.org/10.1038/s41598-024-79502-z
ZHU, J.; FANG, S.; YANG, Z.; QIN, Y.; CHEN, H. Prediction of concrete strength based on random forest and gradient boosting machine. In: IEEE 3RD INTERNATIONAL CONFERENCE ON POWER, ELECTRONICS AND COMPUTER APPLICATIONS (ICPECA), 2023, Shenyang, China. Anais... [S. l.]: IEEE, 2023. p. 306-312. DOI: https://doi.org/10.1109/ICPECA56706.2023.10075839. DOI: https://doi.org/10.1109/ICPECA56706.2023.10075839
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Sandro Martini, Lara Kühl Teles
This work is licensed under a Creative Commons Attribution 4.0 International License.
- O(s) autor(es) autoriza(m) a publicação do artigo na revista;
O(s) autor(es) autoriza(m) a publicação do texto na da revista;
O(s) autor(es) garantem que a contribuição é original e inédita e que não está em processo de avaliação em outra(s) revista(s);
A revista não se responsabiliza pelas opiniões, ideias e conceitos emitidos nos textos, por serem de inteira responsabilidade de seu(s) autor(es);
É reservado aos editores o direito de proceder a ajustes textuais e de adequação às normas da publicação.
Autores mantém os direitos autorais e concedem à revista o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Creative Commons Atribuição-NãoComercial 4.0 Internacional que permite o compartilhamento do trabalho com reconhecimento da autoria e publicação inicial nesta revista.
Autores têm autorização para assumir contratos adicionais separadamente, para distribuição não-exclusiva da versão do trabalho publicada nesta revista (ex.: publicar em repositório institucional ou como capítulo de livro), com reconhecimento de autoria e publicação inicial nesta revista.
Autores têm permissão e são estimulados a publicar e distribuir seu trabalho online (ex.: em repositórios institucionais ou na sua página pessoal) a qualquer ponto antes ou durante o processo editorial, já que isso pode gerar alterações produtivas, bem como aumentar o impacto e a citação do trabalho publicado (Veja O Efeito do Acesso Livre)
