Sulfate Attack-Induced C-S-H Gel Degradation Mechanism and Machine Learning-Based Strength Prediction of Coal Gangue Aggregate Concrete.

Shuanghua He, Ruicong Han, Junfeng Guan, Ying Hao, Li Zhao, Yafei Jing

Abstract read

In one paragraph

Article in Gels (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Shuanghua HeSchool of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Ruicong HanSchool of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Junfeng GuanSchool of Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou 450046, China.ORCID 0000-0003-2709-6527

Ying HaoSchool of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Li ZhaoSchool of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Yafei JingSchool of Civil Engineering and Transportation, North China University of Water Resources and Electric Power, Zhengzhou 450045, China.

Funding

National Natural Science Foundation of China 52479124, 52378237, 52309155

6 · The paper itself

Abstract

Coal gangue concrete (CGC) is an effective green building material that can promote the resource utilization of solid waste. To study its durability performance and degradation mechanism under sulfate attack with dry-wet cycles, and to realize the intelligent prediction of mechanical properties, this study prepared CGC specimens with a water-to-binder ratio of 0.4, a fine aggregate replacement rate of 20%, and coarse aggregate replacement rates of 0%, 20%, 50%, 80%, and 100%. The specimens were tested under 30, 60, 90, and 120 dry-wet cycles in 10% MgSO

Indexed as

coal gangue aggregate concretecorrosion resistance coefficientC-S-H gel degradationmachine learningmechanical propertiesprediction modelsulfate attack

Identifiers

PMID42644957

DOI10.3390/gels12080712

PMCPMC13512141

What OpenQuestion holds

Textmetadata

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.