中国煤炭地质2024,Vol.36Issue(1) :36-40.DOI:10.3969/j.issn.1674-1803.2024.01.06

冻融循环次数对饱水砂岩力学特性的影响分析及预测

Analysis and Prediction of the Influence of the Number of Freeze-thaw Cycles on the Mechanical Properties of Saturated Sandstone

周游 陈彦龙
中国煤炭地质2024,Vol.36Issue(1) :36-40.DOI:10.3969/j.issn.1674-1803.2024.01.06

冻融循环次数对饱水砂岩力学特性的影响分析及预测

Analysis and Prediction of the Influence of the Number of Freeze-thaw Cycles on the Mechanical Properties of Saturated Sandstone

周游 1陈彦龙2
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作者信息

  • 1. 中煤科工生态环境科技有限公司,北京 100013;天地科技股份有限公司,北京 100013
  • 2. 中国矿业大学深部岩土力学与地下工程国家重点实验室,江苏徐州 221116
  • 折叠

摘要

为了研究高寒地区冻融循环次数对饱水砂岩物理力学特性的影响,开展了冻融0次、20次、40次和60次四种不同冻融循环次数下西部某露天矿砂岩单轴压缩试验.基于试验结果,分析了冻融循环对砂岩质量、变形及强度等物理力学特性的影响规律.通过分析得到以下结果:随着冻融次数的增加,饱水砂岩质量不断增加,到冻融40次后,砂岩质量趋于稳定;砂岩的应力-应变曲线呈现出四个阶段,峰值强度和弹性模量随着冻融次数增多而呈线性减少的变化趋势.建立了以应变和冻融次数为输入层,应力为输出层,含一个隐含层的神经网络本构模型.通过对比分析冻融60次试验结果,验证了该模型具有良好的适用性.

Abstract

In order to study physical and mechanical properties of saturated sandstone under freeze-thaw cycles in the alpine region,uniaxial compression tests under four different freeze-thaw cycles of 0 times,20 times,40 times and 60 times were designed.And then based on the test results,this article analyzes the effect of freeze-thaw cycles on quality,deformation and the other mechanical properties such as strength.According to the results,with the increasing of freezing-thawing times,the mass of saturated sandstone increased continuously.After 40 freezing-thawing times,the mass of saturated sandstone tended to be stable.The stress-strain curve of sandstone can be divided into four stages.The peak strength and elastic modulus decrease linearly with the increasing of freezing-thawing times.Finally,the constitutive model of a neural network with a hidden layer is established with strain and freeze-thaw times as input layer and stress as output layer.By comparing and analyzing the freeze-thaw test results of 60 times,the model is proved to have good applicability.

关键词

冻融循环/力学特性/砂岩/神经网络/本构模型

Key words

freeze-thaw cycles/mechanical behavior/sandstone/constitutive model/neural network

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基金项目

国家自然科学基金项目(51974295)

中国煤炭科工集团有限公司科技创新创业资金专项重点项目(2022-2-ZD004)

出版年

2024
中国煤炭地质
中国煤炭地质总局

中国煤炭地质

CSTPCD
影响因子:0.652
ISSN:1674-1803
参考文献量7
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