Evidence map›Paper›PMID 41681235›Full record

ArticleMaterials (Basel, Switzerland)2026

Steel Slag-Enhanced Cement-Stabilized Recycled Aggregate Bases: Mechanical Performance and PINN-Based Sulfate Diffusion Prediction.

Guodong Zeng, Hao Li, Yuyuan Deng, Xuancang Wang, Yang Fang, Haoxiang Liu

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 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

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

6 authors.

Guodong ZengFoshan Transportation Science and Technology Co., Ltd., Foshan 528041, China.
Hao LiFoshan Transportation Science and Technology Co., Ltd., Foshan 528041, China.
Yuyuan DengSchool of Highway, Chang'an University, Xi'an 710064, China.ORCID 0000-0001-6023-2573
Xuancang WangSchool of Highway, Chang'an University, Xi'an 710064, China.ORCID 0000-0002-3439-5691
Yang FangFoshan Transportation Science and Technology Co., Ltd., Foshan 528041, China.
Haoxiang LiuSchool of Highway, Chang'an University, Xi'an 710064, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of cement-stabilized recycled aggregate (CSR) in pavement bases is constrained by the high porosity and low strength of recycled aggregate (RA), whereas sulfate transport and durability mechanisms are less reported. To address this issue, this study incorporated high-strength and potentially reactive steel slag aggregate (SSA) into CSR to develop steel slag-enhanced cement-stabilized recycled aggregate (CSRS). The mechanical performance of the mixtures was evaluated through unconfined compressive strength (UCS) and indirect tensile strength (ITS) tests, and their durability was assessed using thermal shrinkage and sulfate resistance tests. In addition, a sulfate prediction model based on a physics-informed neural network (PINN) was developed. The results showed that, compared with CSR, the 7-day and 28-day UCS of CSRS increased by 6.7% and 16.0%, respectively, and the ITS increased by 4.3% and 5.9%. Thermal shrinkage tests indicated that CSR and CSRS, incorporating RA and SSA, exhibited slightly higher thermal shrinkage strain than cement-stabilized natural aggregate (CSN). During sulfate attack, SSA significantly improved the sulfate resistance of CSR, with the sulfate resistance coefficient of CSRS increasing by 18.8% compared to CSR. Furthermore, the PINN model predicted that, in 3%, 5%, and 7% sodium sulfate solutions, the sulfate concentration at a 1 mm depth in CSRS was reduced by 35.6%, 21.8%, and 29.4%, respectively, compared to CSR, with an average relative error below 14%, confirming its reliability. Therefore, these findings demonstrate that the incorporation of SSA markedly enhances the mechanical properties and sulfate resistance of CSR, and that the PINN model provides an effective tool for accurate simulation and prediction of sulfate diffusion.

Indexed as

cement-stabilized recycled aggregatemechanical performancepavement basephysics-informed neural network modelsteel slag aggregatesulfate diffusionsulfate resistance

Identifiers

PMID41681235
PMCPMC12897973

What OpenQuestion holds

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Registered trials

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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.