Evidence map›Paper›PMID 41303924›Full record

ArticleMaterials (Basel, Switzerland)2025

Effect of Waste Tire Particle Content on the Compressive Behavior and Pore Structure of Loess Subgrade Materials.

Xueyu Cao, Yang Liu, Xun Wu, Meng Han, Xiaoyan Liu

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. 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

5 authors.

Xueyu CaoSchool of Highway, Henan College of Transportation, Zhengzhou 451460, China.
Yang LiuSchool of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Xun WuCollege of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China.ORCID 0000-0002-9440-3120
Meng HanSchool of Civil Engineering, Dalian University of Technology, Dalian 116024, China.ORCID 0000-0002-4635-2357
Xiaoyan LiuSchool of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, China.

Funding

National Natural Science Foundation of China 42207180 and 42577173
6 · The paper itself

Abstract

In response to the challenges of low recycling rates of waste tires and their underutilization in loess subgrades, this study systematically investigates the compression deformation characteristics of tire particle (4-6 mm)-modified loess through comprehensive laboratory testing. Using one-dimensional compression tests and cyclic loading-unloading tests, the effects of different tire particle contents (0% to 100%) on pore structure evolution, compression parameters-including the compression coefficient, compression modulus, and volumetric compression coefficient-and deformation mechanisms were thoroughly analyzed. The study reveals critical state characteristics and deformation mechanisms of tire-derived aggregate-loess mixtures (TDA-LMs) and establishes a predictive model for their compression behavior. The research results indicate the following: (1) The compression behavior of TDA-LM exhibits a distinct dosage threshold and stress dependence: the critical blending ratio is 30% under stresses below 100 kPa, increasing to 40% at higher stresses (≥100 kPa); (2) Mixtures with medium to low tire content display strain hardening, whereas pure tire specimens show approximately 10% modulus softening within the 200-300 kPa range. Stress- and content-dependent models for the compression modulus and volumetric compression coefficient were developed with high accuracy (

Indexed as

compression characteristicsconsolidation testloesstire-derived aggregate-loess mixture (TDA-LM)void ratiowaste tires

Identifiers

PMID41303924
PMCPMC12654275

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.