Evidence map›Paper›PMID 39795784›Full record

ArticleMaterials (Basel, Switzerland)2025

Prediction of Compressive Strength of Fly Ash-Recycled Mortar Based on Grey Wolf Optimizer-Backpropagation Neural Network.

Jing-Jing Shao, Lin-Bin Li, Guang-Ji Yin, Xiao-Dong Wen, Yu-Xiao Zou, Xiao-Bao Zuo, Xiao-Jian Gao, Shan-Shan Cheng

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 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

8 authors.

Jing-Jing ShaoSchool of Architecture and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China.ORCID 0000-0003-3497-7330
Lin-Bin LiSchool of Architecture and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China.
Guang-Ji YinSchool of Architecture and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China.ORCID 0000-0003-2205-8636
Xiao-Dong WenSchool of Architecture and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China.
Yu-Xiao ZouSchool of Safety Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.ORCID 0000-0003-0364-9887
Xiao-Bao ZuoSchool of Safety Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Xiao-Jian GaoSchool of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China.ORCID 0000-0003-3138-5751
Shan-Shan ChengSchool of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK.ORCID 0000-0001-8905-677X

Funding

Guang-Ji Yin 2023J176Guang-Ji Yin 52308262Guang-Ji Yin HZKY20220199
6 · The paper itself

Abstract

The evaluation of the mechanical performance of fly ash-recycled mortar (FARM) is a necessary condition to ensure the efficient utilization of recycled fine aggregates. This article describes the design of nine mix proportions of FARMs with a low water/cement ratio and screens six mix proportions with reasonable flowability. The compressive strengths of FARMs were tested, and the influence of the water/cement ratio (

Indexed as

backpropagation neural networkcompressive strengthfly ash-recycled mortargrey wolf optimizerlow water/cement ratio

Identifiers

PMID39795784
PMCPMC11722042

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.