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A.D.R. Troncoso García¹, S. Czarnecki², E.K. Nyarko³, M. Hadzima-Nyarko⁴ and F. Martinez Alvarez¹

¹, Universidad Pablo de Olavide, Spain
²Department of Materials Engineering and Construction Processes, Wroclaw University of Science and Technology, Poland
³Faculty of Electrical Engineering, Computer Science and Information Technology, University of Osijek, Croatia
⁴Faculty of Civil Engineering and Architecture, Josip Juraj Strossmayer, University of Osijek, Croatia

A.D.R. Troncoso García, S. Czarnecki, E.K. Nyarko, M. Hadzima-Nyarko, F. Martinez Alvarez, "Concrete Mixture Compressive Strength Estimation Using Interpretable Tree-Based Machine Learning Models", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 10, Paper 5.3, 2025,

Keywords: compressive strength, reinforced concrete, waste brick aggregates, explainability, predictive modelling, machine learning.

Abstract

Compressive strength is a critical property of concrete, influencing its performance and durability. Accurate prediction is essential for optimizing mix designs and ensuring construction safety. The aim of this study is to evaluate the effect of varying material compositions on the mechanical performance of concrete, aiming to assess their potential as sustainable alternatives in structural applications. This work focuses on evaluating the 28-day compressive strengths of concrete mixtures that include waste brick aggregates, with particular attention to its compression strength after 28 days of curing. Several machine learning models were used to estimate the strength of a concrete mixture that incorporated crushed brick as a partial or complete replacement for conventional aggregates. Then, the explainable tool SHapley Additive exPlanations was employed to interpret the models and analyze the contribution of each input feature to the predictions.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 10 PROCEEDINGS OF THE EIGHTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: P. Iványi, J. Kruis and B.H.V. Topping Paper 5.3 Concrete Mixture Compressive Strength Estimation Using Interpretable Tree-Based Machine Learning Models A.D.R. Troncoso García¹, S. Czarnecki², E.K. Nyarko³, M. Hadzima-Nyarko⁴ and F. Martinez Alvarez¹ ¹, Universidad Pablo de Olavide, Spain ²Department of Materials Engineering and Construction Processes, Wroclaw University of Science and Technology, Poland ³Faculty of Electrical Engineering, Computer Science and Information Technology, University of Osijek, Croatia ⁴Faculty of Civil Engineering and Architecture, Josip Juraj Strossmayer, University of Osijek, Croatia Full Bibliographic Reference for this paper A.D.R. Troncoso García, S. Czarnecki, E.K. Nyarko, M. Hadzima-Nyarko, F. Martinez Alvarez, "Concrete Mixture Compressive Strength Estimation Using Interpretable Tree-Based Machine Learning Models", in P. Iványi, J. Kruis, B.H.V. Topping, (Editors), "Proceedings of the Eighteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 10, Paper 5.3, 2025, Keywords: compressive strength, reinforced concrete, waste brick aggregates, explainability, predictive modelling, machine learning. Abstract Compressive strength is a critical property of concrete, influencing its performance and durability. Accurate prediction is essential for optimizing mix designs and ensuring construction safety. The aim of this study is to evaluate the effect of varying material compositions on the mechanical performance of concrete, aiming to assess their potential as sustainable alternatives in structural applications. This work focuses on evaluating the 28-day compressive strengths of concrete mixtures that include waste brick aggregates, with particular attention to its compression strength after 28 days of curing. Several machine learning models were used to estimate the strength of a concrete mixture that incorporated crushed brick as a partial or complete replacement for conventional aggregates. Then, the explainable tool SHapley Additive exPlanations was employed to interpret the models and analyze the contribution of each input feature to the predictions. download the full-text of this paper (PDF, 12 pages, 371 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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