Evidence map›Paper›PMID 39962216›Full record

ArticleScientific reports2025

Prediction and assessment of optimal concrete compositions for overall radiation protection and reduced global warming potential.

Sanchit Saxena, Hrishikesh Sharma

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Sanchit SaxenaResearch Scholar, Department of Civil Engineering at IIT Guwahati, Guwahati, Assam, India, 781039.
Hrishikesh SharmaAssociate Professor, Department of Civil Engineering, Indian Institute of Technology (IIT) Guwahati, Guwahati, 781039, Assam, India. shrishi@iitg.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Developing an efficient radiation-shielding concrete composition holds paramount importance for nuclear, medical, and defence facilities. The intricate interactions between various radiation particles and materials across different energy ranges present challenges in designing effective and resilient overall shielding structures. This study presents a novel approach that integrates machine learning and genetic algorithms (GA) to optimize concrete compositions for enhanced radiation shielding against gamma and neutron rays across a wide energy spectrum. By leveraging these advanced techniques, six compositions (concrete_1-concrete_6) spanning different density ranges were derived from an extensive database developed from the previous experimental researches. Subsequently, the shielding effectiveness of these compositions against all radiation particles was evaluated and compared using the OpenMC Code. The findings revealed that the proposed concrete_5 and concrete_6 compositions, comprising iron, boron, nickel, and tungsten at specified weight fractions, outperform other state-of-the-art compositions in overall radiation shielding. Furthermore, the analysis indicated a 65.89% reduction in Global Warming Potential (GWP) with the adoption of concrete_6 composition compared to conventional concrete composition.

Indexed as

Machine learningMonte-Carlo simulationMulti-objective constrained genetic algorithmRadiation attenuating concreteSustainability

Identifiers

PMID39962216
PMCPMC11833061

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.