Explainable machine learning based prediction of 28-day compressive strength of recycled aggregate self-compacting concrete using particle packing indicators.
Sep 2026· Revista ALCONPAT· Vol 16, pp. 411-441· 0 citations· 43 references
Abstract
This study aims to develop an explainable machine learning framework for predicting the 28-day compressive strength of recycled aggregate self-compacting concrete (RA-SCC) using particle packing indicators. A dataset comprising 365 mixtures from 102 studies was analyzed using six regression models, with SHapley Additive exPlanations (SHAP) applied for interpretability. The Extra Trees model achieved the highest accuracy (R2 = 0.9724, Root Mean Square Error (RMSE) = 1.19 MPa). The dataset is limited to 28-day strength and excludes admixture effects. The study introduces a combined approach integrating particle packing concepts, multicollinearity assessment, and explainable AI. The results demonstrate that machine learning can reliably predict strength while implicitly capturing packing-related mechanisms.
Summary Recycled aggregate concrete (RAC) supports more sustainable construction, but recycled concrete aggregate (RCA)'s heterogeneity complicates compressive-strength prediction. This study evaluates the Tabular Prior-data Fitted Network (TabPFN) against nine conventional machine-learning models using a standardized...
Jin Fan, Roshani Bharati, Gang-Yong Zhang et al.· iScience· 0 citations
This study develops an interpretable machine learning framework for predicting the 28-day compressive strength of recycled aggregate concrete containing supplementary cementitious materials and validates it experimentally using metakaolin and spent fluid catalytic cracking catalyst residue. A broad multi-source databas...
Jesús E. Altamiranda-Ramos, L. Castillo-Suárez, J. Abellán-García· Modelling· 1 citation
The results show that regularized models can support preliminary RAC mix evaluation and material assessment and show that regularized models can support preliminary RAC mix evaluation and material assessment.
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Growing concerns about environmental impact and rising construction costs have encouraged the use of recycled aggregates in concrete. This study offers an experimental and AI-driven assessment of the compressive strength of recycled aggregate concrete. Concrete was prepared by varying recycled fine and coarse aggregate...
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This study aims to evaluate the mechanical and durability performance of sustainable concrete incorporating Marble Fine Powder (MFP), Marble Coarse Powder (MCP) and Recycled Concrete Aggregate (RCA), and to develop machine learning (ML) models for accurate prediction of concrete properties.
A total of 250 ex...
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