Optimization of Concrete Mix Composition Containing Fly Ash and Slag Using a Machine Learning Algorithm for Compressive Strength Prediction

Ichwan Hadi Saputra; Eko wahyu Abryandoko; Moh. Nurudduja

doi:10.56741/IISTR.esl.002300

Authors

Ichwan Hadi Saputra
ichwanhs@gmail.com
Universitas Bojonegoro https://orcid.org/0009-0004-6790-2364
Eko wahyu Abryandoko Universitas Bojonegoro https://orcid.org/0000-0003-0480-6135
Moh. Nurudduja Universitas Bojonegoro https://orcid.org/0009-0007-0140-3310

Keywords:

artificial neural network, blast furnace slag, concrete compressive strength, fly ash, machine learning

Abstract

The modern construction industry faces significant challenges in developing sustainable concrete materials while maintaining structural quality requirements. Conventional trial-and-error methods for concrete mix design are time-consuming, costly, and often result in high variability in concrete quality. This study presents an integrated framework that combines machine learning techniques for concrete compressive strength prediction with genetic algorithm optimization to determine optimal mix compositions containing fly ash and blast furnace slag. Two predictive models were developed using the UCI Machine Learning Repository concrete dataset comprising 1,030 samples: Artificial Neural Network (ANN) Ensemble and Support Vector Regression (SVR). The ANN model demonstrated superior performance, achieving R² values ranging from 0.7475 to 0.8372, RMSE values between 6.11 and 7.94 MPa, and classification accuracy of 86.92% for concrete quality categorization across three classes (Class I: <20 MPa, Class II: 20-35 MPa, Class III: >35 MPa). In comparison, the SVR model achieved competitive but slightly lower performance with R² values of 0.7491-0.8378 and classification accuracy of 80.37%. The stability and generalizability of both models were confirmed through five-fold cross-validation. Subsequently, genetic algorithm optimization was applied to determine optimal mix compositions for each quality class while ensuring compliance with Indonesian National Standards (SNI 2847:2019, SNI 2461:2011, and SNI 8297:2016). The optimization process successfully produced concrete mix designs that achieved target compressive strengths of 14.95 MPa for Class I, 27.48 MPa for Class II, and 59.99 MPa for Class III. This framework demonstrates significant potential for developing sustainable concrete with optimal performance while meeting applicable technical standards, thereby contributing to a reduced carbon footprint in the construction industry through strategic utilization of supplementary cementitious materials.

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Author Biographies

Ichwan Hadi Saputra, Universitas Bojonegoro

Ichwan Hadi Saputra is a researcher and academic in the Department of Civil Engineering, Faculty of Science and Engineering, Universitas Bojonegoro, Indonesia. His research interests include sustainable concrete materials, supplementary cementitious materials, machine learning applications in civil engineering, structural materials, and optimization methods for concrete mix design.

Eko wahyu Abryandoko, Universitas Bojonegoro

Eko Wahyu Abryandoko is a lecturer and researcher in the Department of Industrial Engineering, Faculty of Science and Engineering, Universitas Bojonegoro, Indonesia. His research interests include machine learning applications, optimization algorithms, sustainable engineering systems, rehabilitation robotics, and intelligent decision support systems.

Moh. Nurudduja, Universitas Bojonegoro

Moh. Nurudduja is a student in the Department of Industrial Engineering, Faculty of Science and Engineering, Universitas Bojonegoro, Indonesia. His research interests include machine learning applications, optimization methods, sustainable engineering systems, and data-driven approaches in industrial engineering.

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