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Modeling for strain-softening rocks with lateral damage based on statistical physics

Statistical physics is widely used to study the nonlinear mechanical behaviors of rock. For the limitations of existing statistical damage models and Weibull distribution, a new statistical damage with lateral damage is established. In addition, by introducing the maximum entropy distribution functi...

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Detalles Bibliográficos
Autores principales: Li, Xiaoming, Wang, Mingwu, Shen, Fengqiang, Zhang, Hongfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062584/
https://www.ncbi.nlm.nih.gov/pubmed/36996233
http://dx.doi.org/10.1371/journal.pone.0283313
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author Li, Xiaoming
Wang, Mingwu
Shen, Fengqiang
Zhang, Hongfei
author_facet Li, Xiaoming
Wang, Mingwu
Shen, Fengqiang
Zhang, Hongfei
author_sort Li, Xiaoming
collection PubMed
description Statistical physics is widely used to study the nonlinear mechanical behaviors of rock. For the limitations of existing statistical damage models and Weibull distribution, a new statistical damage with lateral damage is established. In addition, by introducing the maximum entropy distribution function and the strict constraint on damage variable, a expression of the damage variable matching the proposed model is obtained. Through comparing with the experimental results and the other two statistical damage models, the rationality of the maximum entropy statistical damage model is confirmed. The proposed model can better reflect the strain-softening behavior for rocks and respond to the residual strength, which provides a theoretical reference for practical engineering construction and design.
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spelling pubmed-100625842023-03-31 Modeling for strain-softening rocks with lateral damage based on statistical physics Li, Xiaoming Wang, Mingwu Shen, Fengqiang Zhang, Hongfei PLoS One Research Article Statistical physics is widely used to study the nonlinear mechanical behaviors of rock. For the limitations of existing statistical damage models and Weibull distribution, a new statistical damage with lateral damage is established. In addition, by introducing the maximum entropy distribution function and the strict constraint on damage variable, a expression of the damage variable matching the proposed model is obtained. Through comparing with the experimental results and the other two statistical damage models, the rationality of the maximum entropy statistical damage model is confirmed. The proposed model can better reflect the strain-softening behavior for rocks and respond to the residual strength, which provides a theoretical reference for practical engineering construction and design. Public Library of Science 2023-03-30 /pmc/articles/PMC10062584/ /pubmed/36996233 http://dx.doi.org/10.1371/journal.pone.0283313 Text en © 2023 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Li, Xiaoming
Wang, Mingwu
Shen, Fengqiang
Zhang, Hongfei
Modeling for strain-softening rocks with lateral damage based on statistical physics
title Modeling for strain-softening rocks with lateral damage based on statistical physics
title_full Modeling for strain-softening rocks with lateral damage based on statistical physics
title_fullStr Modeling for strain-softening rocks with lateral damage based on statistical physics
title_full_unstemmed Modeling for strain-softening rocks with lateral damage based on statistical physics
title_short Modeling for strain-softening rocks with lateral damage based on statistical physics
title_sort modeling for strain-softening rocks with lateral damage based on statistical physics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10062584/
https://www.ncbi.nlm.nih.gov/pubmed/36996233
http://dx.doi.org/10.1371/journal.pone.0283313
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