Self-compacting concrete with fly ash and silica fume: experimental evaluation, microstructural analysis, and machine learning modeling

This study comprehensively evaluates the fresh, mechanical, durability, and microstructural performance of self-compacting concrete (SCC) incorporating fly ash (FA) and silica fume (SF) as supplementary cementitious materials (SCMs). Cement was partially replaced with FA at 20%, 30%, and 40% and SF at 5%, 7.5%, and 10%, forming both binary and ternary combinations. The optimum FA content was established at 30%, beyond which early strength decreased due to dilution effects. Ternary blends with 30% FA + 5–10% SF were further developed to examine synergistic effects. Fresh property tests (slump flow, T₅₀₀, V-funnel, and L-box) confirmed all SCC mixes satisfied EFNARC criteria, with ternary blends exhibiting the best balance between flowability (720 mm slump flow), viscosity (T₅₀₀ = 3.5 s), and passing ability (H₂/H₁ ≥ 0.9). Mechanical performance improved consistently with SCM incorporation; at 180 days, the FA30SF7.5 mix attained 68 MPa compressive strength, 6.3 MPa split tensile, and 8.9 MPa flexural strength, surpassing the control by 17–22%. Durability tests demonstrated marked improvement, sorptivity reduced by 34%, rapid chloride penetration test (RCPT) charge fell from 3600 C (Moderate) to 800 C (Very Low), and ultrasonic pulse velocity (UPV) exceeded 4.75 km/s, confirming a highly dense matrix. Microstructural analysis revealed a compact C–S–H gel network and diminished portlandite content in ternary mixes, evidencing improved hydration and pozzolanic activity. Machine learning (ML) models (KNN, SVM, DT, and RF) successfully predicted compressive strength, with the Random Forest model achieving R2 of 0.97. The combined experimental–ML approach demonstrates that SCC with 30% FA and 7.5% SF offers optimal performance, coupling sustainability with superior mechanical and durability characteristics.

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Publication Details

Journal
Scientific Reports
Published
2025-12-22
DOI
https://doi.org/10.1038/s41598-025-33180-7
Citations
1
Primary Topic
Concrete and Cement Materials Research
Type
article
Field-Weighted Citation Impact
0.95
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article

Self-compacting concrete with fly ash and silica fume: experimental evaluation, microstructural analysis, and machine learning modeling

Siva Avudaiappan, Adamu Mulatu Kumara, Yeswanth Paluri, Siva Shanmukha Anjaneya Babu Padavala et al.
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article

Self-compacting concrete with fly ash and silica fume: experimental evaluation, microstructural analysis, and machine learning modeling

Siva Avudaiappan, Adamu Mulatu Kumara, Yeswanth Paluri, Siva Shanmukha Anjaneya Babu Padavala, S. R. R. Teja Prathipati, Ch Naga Bharath
article en
1 citations

Abstract

This study comprehensively evaluates the fresh, mechanical, durability, and microstructural performance of self-compacting concrete (SCC) incorporating fly ash (FA) and silica fume (SF) as supplementary cementitious materials (SCMs). Cement was partially replaced with FA at 20%, 30%, and 40% and SF at 5%, 7.5%, and 10%, forming both binary and ternary combinations. The optimum FA content was established at 30%, beyond which early strength decreased due to dilution effects. Ternary blends with 30% FA + 5–10% SF were further developed to examine synergistic effects. Fresh property tests (slump flow, T₅₀₀, V-funnel, and L-box) confirmed all SCC mixes satisfied EFNARC criteria, with ternary blends exhibiting the best balance between flowability (720 mm slump flow), viscosity (T₅₀₀ = 3.5 s), and passing ability (H₂/H₁ ≥ 0.9). Mechanical performance improved consistently with SCM incorporation; at 180 days, the FA30SF7.5 mix attained 68 MPa compressive strength, 6.3 MPa split tensile, and 8.9 MPa flexural strength, surpassing the control by 17–22%. Durability tests demonstrated marked improvement, sorptivity reduced by 34%, rapid chloride penetration test (RCPT) charge fell from 3600 C (Moderate) to 800 C (Very Low), and ultrasonic pulse velocity (UPV) exceeded 4.75 km/s, confirming a highly dense matrix. Microstructural analysis revealed a compact C–S–H gel network and diminished portlandite content in ternary mixes, evidencing improved hydration and pozzolanic activity. Machine learning (ML) models (KNN, SVM, DT, and RF) successfully predicted compressive strength, with the Random Forest model achieving R2 of 0.97. The combined experimental–ML approach demonstrates that SCC with 30% FA and 7.5% SF offers optimal performance, coupling sustainability with superior mechanical and durability characteristics.

Scientific ReportsVol. 15(1)
Siddhartha Medical College (IN), Guizhou University (CN), Wollega University (ET), Metropolitan University of Technology (CL)
Life in Land, Responsible consumption and production
Openalex Percentile: Top 18%
Concrete and Cement Materials Research
0.95
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