Sep 2026· Revista ALCONPAT· 0 citations· 41 references
TL;DR
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.
Abstract
This study aims to develop a method for predicting the 28-day compressive strength of recycled aggregate concrete (RAC) for rigid pavement applications. A hybrid dataset of 385 observations, combining laboratory results and selected literature data, was used to develop and compare machine learning models. The models were assessed using five-fold cross-validation, error measures, bias analysis, multicollinearity assessment, and SHAP interpretation. Lasso Regression provided the best performance, with R² = 0.7713, MAE = 3.51 MPa, and RMSE = 4.47 MPa. The study is limited to five-fold cross-validation without independent external validation. Its originality lies in evaluating a pavement-oriented hybrid RAC dataset using predictive and interpretive analyses. The results show that regularized models can support preliminary RAC mix evaluation and material assessment.
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
Investigation of machine learning models for predicting the compressive strength of concrete using a publicly available experimental dataset reveals that curing age and cement content are the most influential parameters affecting compressive strength, followed by water content, which is consistent with established conc...
S. Rouabah· ITEGAM- Journal of Engineeri...· 0 citations
This study proposes a source-study-aware validation framework to estimate the shear capacity of stirrup-free steel-fiber-reinforced concrete corbels and assess the reliability and generalizability of machine-learning models in structural engineering. A database of 108 specimens, compiled from seven independent studies,...
Accurate prediction of concrete compressive strength is essential for mixture design, quality control, and the broader use of supplementary cementitious materials in low-carbon construction. Fly ash concrete is particularly challenging to model because its strength development is affected by nonlinear interactions amon...
This study developed and evaluated machine-learning models for predicting 28-day cylinder compressive strength of concrete using a literature-derived dataset of 1,456 mixtures collected from 136 source studies. Eight recorded mixture quantities were used as the original inputs. Source-study identity and cross-study dup...
Muhammad Saif Ullah, Laiba Qamar· International Journal of Res...· 0 citations
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 Additiv...
