{
  "abstract": "The subgrade resilient modulus is a crucial parameter in pavement structure design, directly related to the safety and economy of the pavement structure. Currently, the subgrade resilient modulus used in pavement structure design is the static resilient modulus. To address this issue, this paper employs dynamic triaxial tests to analyze the effects of confining stress (σ3), cyclic dynamic stress (σd), wet-dry cycles (N), and cement dosage (C) on the resilient modulus (MR). It explores the relationship among the MR, static resilient modulus, and 7-day unconfined compressive strength (σc), and proposes a predictive model for MR considering C and N. SEM and XRD methods are used to analyze the mechanism of the effect of wet-dry cycles on MR at the microstructural level. The results show that; (1) The MR of the PG mixture increases with the rise of σ3 and decreases with the increase of σd, is positively correlated with C, and negatively correlated with the number of N, with the decay of MR mainly concentrated in the first four wet-dry cycles and gradually stabilizing afterwards, which is explained through SEM and XRD analysis. (2) By comparing MR and the static resilient modulus, it is found that the MR of PG specimens under wet-dry cycles is 1.25–1.30 times that of the static resilient modulus, this ratio can be used to make a preliminary estimation of the MR of C-stabilized PG materials. (3) The model relating σc and MR follows a linear relationship, with correlation coefficients for the fit all exceeding 0.94, indicating a good fit. (4) A model for predicting MR that incorporates N and C was proposed, and the reliability of this model was verified by comparing predicted values with experimental data. The predicted MR provides a reliable basis for pavement design and holds significant engineering value.",
  "abstract_shown": true,
  "affiliations": [
    "College of Civil Engineering, Guizhou University, Guiyang, China",
    "School of Civil Engineering, Southwest Jiaotong University, Chengdu, China",
    "School of Architectural Engineering, Kaili University, Kaili, Guizhou, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wengang Gan"
    },
    {
      "affs": [
        1
      ],
      "name": "Kaisheng Chen"
    },
    {
      "affs": [
        2
      ],
      "name": "Jinxiong Chen"
    },
    {
      "name": "Yu’ang Chen"
    },
    {
      "name": "Shuang Deng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8bxqws8f6tj0h.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0341539",
  "fulltext_url": null,
  "id": "8bxqws8f6tj0h",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0341539",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2205|Geotechnical and construction materials studies"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Network resilience"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Pollution/Water pollution"
    ],
    [
      "plos",
      "/Engineering and technology"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmaceutics/Dose prediction methods"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Binders/Cements"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Porous materials"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Forecasting"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Damage mechanics/Deformation"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Deformation"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Forecasting"
    ]
  ],
  "openalex": "W7211934484",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0341539&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/8bxqws8f6tj0h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0341539",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0341539"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "plos:materials science",
      "subfield": "materials-polymers"
    },
    {
      "field": "earth",
      "origin": "plos:pollution",
      "subfield": "environmental-science"
    },
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    }
  ],
  "title": "Resilient modulus characteristics and model prediction of cement phosphogypsum under dry-wet cycling conditions",
  "title_text": "Resilient modulus characteristics and model prediction of cement phosphogypsum under dry-wet cycling conditions",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/8bxqws8f6tj0h",
  "venue": "PLOS One",
  "versions": []
}