A holistic model incorporating all curing ages outperforms single-age models, and the inclusion of categorical features, particularly mix specification, significantly enhances predictive performance.
The increasing demand for sustainable construction materials has accelerated the incorporation of industrial by-products such as fly ash, ground granulated blast furnace slag (GGBFS), silica fume, rice husk ash, marble dust, and waste glass powder into composite concrete. These supplementary cementitious materials not...
Ruchita K. Ingole· International Journal of Cre...· 0 citations
The rapid rate of urbanization and industrialization has driven the excessive use of natural resources like river sand and gravel, raising significant sustainability concerns. Waste foundry sand (WFS), a discarded by-product of ferrous and nonferrous metal casting industries, offers a promising substitute for natur...
M. H. R. Sobuz, Md. Kawsarul Islam Kabbo, A. Alzlfawi et al.· Journal of Structural Design...· 0 citations
Recycled aggregate concrete (RAC) is a sustainable alternative construction material to reduce natural resource exploitation and manage construction and demolition waste. However, predicting the mechanical performance of RAC remains a challenge due to the high variability of recycled aggregate properties. The purpose o...
Suji’at, Eko Wahyu Abryandoko, Ocha Silvia Kencana et al.· Journal of Novel Engineering...· 0 citations
The increasing demand for sustainable construction materials has led to the selection of supplementary cementitious materials (SCMs) to minimize the environmental burden of Portland cement manufacturing. This paper experimentally investigates the effect of varied volume of fly ash replacement on some fresh, mechanical...
Six machine learning algorithms were employed to construct artificial intelligence models for the precise prediction of self-compacting concrete (SCC) flow properties, and the extreme gradient boosting (XGB) model was identified as exhibiting superior predictive accuracy and generalization performance.
Jinlei Mu, Xin Fang, Ke-qiang Cao et al.· Materials Research Express· 0 citations
Comparison analysis indicates that ensemble tree-based models—particularly random forest and gradient-boosting variants such as XGBoost—together with well-tuned neural networks consistently achieve the highest predictive accuracy across most concrete properties, whereas limited data availability, inconsistent validatio...
Keqing Hu, Yong-Sen Yang, Yanfeng Wang et al.· Buildings· 1 citation
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