Evidence map›Paper›PMID 39929908›Full record

ArticleScientific reports2025

Analyzing the influence of manufactured sand and fly ash on concrete strength through experimental and machine learning methods.

S Sathvik, Solomon Oyebisi, Rakesh Kumar, Pshtiwan Shakor, Olutosin Adejonwo, Adithya Tantri, V Suma

Abstract read
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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 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

9 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

7 authors.

S SathvikDepartment of Civil Engineering, Dayananda Sagar College of Engineering, Bengaluru, 560111, Karnataka, India.
Solomon OyebisiDepartment of Civil Engineering and Geomatics, Durban University of Technology, Durban, South Africa.
Rakesh KumarDepartment of Civil Engineering, Dayananda Sagar College of Engineering, Bengaluru, 560111, Karnataka, India.
Pshtiwan ShakorTechnical College of Engineering, Sulaimani Polytechnic University, Sulaymaniyah, Iraq.
Olutosin AdejonwoDepartment of Civil Engineering, University of Ibadan, Ibadan, Nigeria.
Adithya TantriDepartment of Civil Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, 576 104, Karnataka, India. tantri.adithya@manipal.edu.
V SumaDepartment of Computer Science and Design, Dayananda Sagar College of Engineering, Bengaluru, 560111, Karnataka, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

River sand supplies are decreasing due to overexploitation and illicit sand mining. One ton of Portland cement production (the main binder in concrete) emits about one ton of carbon dioxide into the atmosphere. Thus, this study replaced conventional cement and river sand (R sand) with recycled waste materials (fly ash and manufactured sand (M sand)). The concrete mix proportions were designed using M40 grade, and the Ordinary Portland cement (OPC) and R sand were replaced with 0-85 wt% of fly ash and 0-100 wt% of M sand. The concrete samples were tested for compressive strength after 3-90 days of curing. Furthermore, machine learning (ML) techniques were engaged to predict the compressive strength of the concrete samples using Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). Besides, the concrete samples containing fly ash, M sand, and R sand were characterized for microstructures and elemental compositions using SEM-EDS. The results revealed improved concrete compressive strength by incorporating fly ash and M sand. After 28 days of curing, OPC and R sand were partially replaced with 25 and 50 wt% of fly ash and M sand attained the designed strength of M 40 grade concrete. XGBoost model yielded the most accurate performance metrics for forecasting the compressive strength in training and testing phases with R

Indexed as

Compressive strengthConcreteFly ashMachine learningManufactured sandSustainable production

Identifiers

PMID39929908
PMCPMC11811028

What OpenQuestion holds

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Registered trials

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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.