Estimating Compressive Strength of Concrete Using Neural Electromagnetic Field Optimization.

Mohammad Reza Akbarzadeh, Hossein Ghafourian, Arsalan Anvari, Ramin Pourhanasa, Moncef L Nehdi

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing 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

2 citing papers in PubMed.

Experimental Investigation on the Bending Performance of Steel-Concrete Composite Beams After Creep.Materials (Basel, Switzerland) · 2025
Article
Experimentally and Modeling Assessment of Parameters Affecting Grinding Aid-Containing Cement-PCE Compatibility: CRA, MARS and AOMA-ANN Methods.Polymers · 2025
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.

Mohammad Reza AkbarzadehDepartment of Civil Engineering, Sharif University of Technology, Tehran 1136511155, Iran.ORCID 0009-0000-2271-1530

Hossein GhafourianDepartment of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA.

Arsalan AnvariDepartment of Construction Engineering and Management, Faculty of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran.

Ramin PourhanasaDepartment of Civil Engineering, College of Engineering, Shahrekord University, Shahrekord 64165478, Iran.ORCID 0000-0003-3391-7985

Moncef L NehdiDepartment of Civil Engineering, McMaster University, Hamilton, ON L8S 4L7, Canada.ORCID 0000-0002-2561-993X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Concrete compressive strength (CCS) is among the most important mechanical characteristics of this widely used material. This study develops a novel integrative method for efficient prediction of CCS. The suggested method is an artificial neural network (ANN) favorably tuned by electromagnetic field optimization (EFO). The EFO simulates a physics-based strategy, which in this work is employed to find the best contribution of the concrete parameters (i.e., cement (

Indexed as

civil engineeringconcrete compressive strengthelectromagnetic field optimizationmetaheuristic strategies

Identifiers

PMID37297334

DOI10.3390/ma16114200

PMCPMC10254481

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LicenceCC BY

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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.