ANN trained by BBO for modeling of fly ash cementitious systems with high range water reducing admixtures.

Naz Mardani, Ramin Kazemi, Murteda Unverdi, Ali Mardani, Seyedali Mirjalili

Abstract read

In one paragraph

Article in Scientific reports, 2026. 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

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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

1 citing paper in PubMed.

Prediction and Regulation of SCC's Shrinkage Using the PSO-BPNN Model.Materials (Basel, Switzerland) · 2026
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

5 authors.

Naz MardaniMathematics Education Department, Bursa Uludag University, Bursa, Turkey.

Ramin KazemiIndependent Researcher, Sabzevar, Iran.

Murteda UnverdiCivil Engineering Department, Bursa Uludag University, Bursa, Turkey.

Ali MardaniCivil Engineering Department, Bursa Uludag University, Bursa, Turkey.

Seyedali MirjaliliCentre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia. ali.mirjalili@torrens.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to develop artificial intelligence (AI) models for predicting the compressive strength and flow value of cementitious systems containing fly ash, influenced by various high-range water-reducing admixtures (HRWRAs) that differ in molecular weight and chain length. A database comprising 180 mixes was created, encompassing cement and fly ash dosages, HRWRA characteristics (including molecular weight, main and side chain lengths) curing period, and flow time. Two AI-based modelling approaches were employed: a classical artificial neural network (ANN) and a new hybrid model that integrates ANN with biogeography-based optimisation (ANN-BBO). The modeling results showed that the hybrid model achieved a compressive strength performance with an R

Indexed as

Artificial intelligenceArtificial neural networkBiogeography-based optimisationCementitious systemsFly ashHigh-range water-reducing admixture

Identifiers

PMID41622225

DOI10.1038/s41598-025-32972-1

PMCPMC12868753

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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.