Evidence map›Paper›PMID 41423490›Full record

ArticleScientific reports2025

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

Siva Shanmukha Anjaneya Babu Padavala, Siva Avudaiappan, Yeswanth Paluri, Ch Naga Bharath, Sri Ram Ravi Teja Prathipati, Adamu Mulatu Kumara

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Siva Shanmukha Anjaneya Babu PadavalaDepartment of Civil Engineering, Gudlavalleru Engineering College, Andhra Pradesh, Gudlavalleru, India.
Siva AvudaiappanDepartamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica Metropolitana, Santiago, Chile.
Yeswanth PaluriDepartment of Civil Engineering, Vishnu Institute of Technology, Bhimavaram, India.
Ch Naga BharathDepartment of Civil Engineering, Gudlavalleru Engineering College, Andhra Pradesh, Gudlavalleru, India.
Sri Ram Ravi Teja PrathipatiDepartment of Civil Engineering, Siddhartha Academy of Higher Education, Deemed to be University, Vijayawada, India.
Adamu Mulatu KumaraDepartment of Civil Engineering , College of Engineering and Technology, Wollega University, Nekemte, Ethiopia. adamum@wollegauniversity.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

DurabilityFly ashMachine learningSelf-compacting concreteSEMSilica fume

Identifiers

PMID41423490
PMCPMC12749484

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.