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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
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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。 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 freezingthawing 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.
作者 周游 陈彦龙 ZHOU You;CHEN Yanlong(CCTEG Ecological Environment Technology Co.,Ltd.,Beijing 10013;Tiandi Science and Technology Co.,Ltd.,Beijing 100013;State Key Laboratory for Geomechanics&Deep Underground Engineering,China University of Mining&Technology,Xuzhou,Jiangsu 221116)
出处 《中国煤炭地质》 2024年第1期36-40,共5页 Coal Geology of China
基金 国家自然科学基金项目(51974295)“循环载荷及爆破冲击下露天端帮采硐顶板和煤柱的失稳机理与防控” 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)“煤基固废协同处置利用关键技术及装备研发”。
关键词 冻融循环 力学特性 砂岩 神经网络 本构模型 freeze-thaw cycles mechanical behavior sandstone constitutive model neural network
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