Proceedings of the International Conference on Recent Innovations in Sustainable Engineering Solutions 2025 (ICONRISES 2025)

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Analysis of Machine Learning Regression Methods Performance in Optimizing Normal Concrete Mix Design

Authors
Diofani Albir Mochammad1, *, Mohamad Agung Prawira Negara1, Widya Cahyadi1, Nanin Meyfa Utami2, Shafiril Ramdani2
1Electrical Engineering, Universitas Jember , Jl. Kalimantan Tegalboto No.37, Krajan Timur, Sumbersari, Kec. Sumbersari, Kab, Jember, Jawa Timur, 68121, Indonesia
2Civil Engineering, Universitas Jember, Jl. Kalimantan Tegalboto No.37, Krajan Timur, Sumbersari, Kec. Sumbersari, Kab., Jember, Jawa Timur, 68121, Indonesia
*Corresponding author. Email: diofanialbir@gmail.com
Corresponding Author
Diofani Albir Mochammad
Available Online 15 December 2025.
DOI
10.2991/978-94-6463-920-9_22How to use a DOI?
Keywords
Machine Learning; Regression Method; Concrete Prediction
Abstract

This paper examines six regression algorithms, namely Linear Regression, Lasso Regression, Ridge Regression, Decision Tree, Random Forest, and Support Vector Regression, to predict material composition and concrete strength. The data used is from previous research on concrete material variations and concrete strength testing. The prediction process is carried out in two stages, predicting material composition and predicting compressive and tensile strength of the concrete. The results show that the Random Forest and Decision Tree algorithms performed better in predicting compressive strength with RMSE values are relatively small 0.51 and 1.51, while Linear Regression and Support Vector Regression showed unsatisfactory results. The Random Forest model ex-celled in handling the complexity of the concrete data with lower RMSE. This study recommends using more advanced algorithms to improve prediction accuracy, especially for predicting concrete tensile strength, and testing with more homogeneous data and real-world conditions to enhance model reliability.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Innovations in Sustainable Engineering Solutions 2025 (ICONRISES 2025)
Series
Advances in Engineering Research
Publication Date
15 December 2025
ISBN
978-94-6463-920-9
ISSN
2352-5401
DOI
10.2991/978-94-6463-920-9_22How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Diofani Albir Mochammad
AU  - Mohamad Agung Prawira Negara
AU  - Widya Cahyadi
AU  - Nanin Meyfa Utami
AU  - Shafiril Ramdani
PY  - 2025
DA  - 2025/12/15
TI  - Analysis of Machine Learning Regression Methods Performance in Optimizing Normal Concrete Mix Design
BT  - Proceedings of the International Conference on Recent Innovations in Sustainable Engineering Solutions 2025 (ICONRISES 2025)
PB  - Atlantis Press
SP  - 214
EP  - 226
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-920-9_22
DO  - 10.2991/978-94-6463-920-9_22
ID  - Mochammad2025
ER  -