岩石统计损伤软化模型及其参数反演

为了反映岩石应变软化现象,基于岩石微元强度服从Weibull随机分布的假定,结合连续损伤理论,建立岩石统计损伤软化模型。该模型含有2个待定系数,其中一个可通过岩石应力-应变曲线的峰值点确定,另一个取决于微元强度参数。通过研究微元强度参数对所建模型的影响,采用优化反演分析法确定模型参数,克服传统方法求解模型参数精度较低的难点,并依据微元强度参数选取合理的强度准则。采用YSJ-01-00岩石三轴流变试验机进行红层泥岩常规三轴压缩试验,对比试验曲线和理论曲线,验证了本文模型和方法的正确性与合理性。     

离心场中陡倾顺层岩质边坡振动试验关键技术研究

为解决离心场中陡倾顺层岩质边坡振动试验的关键技术问题,设计了陡倾顺层斜坡的离心振动台模型,通过试验探讨了试验仪器设备的关键技术参数、动力相似关系、相似材料比选以及传感器的安装布置等内容,并确定了地震波的加载方案。同时为了得到可靠的试验结果,分析了离心力场中的误差、传感器的安装与误差问题和边界效应问题,结合以往经验对误差方法进行比选,并提出了相应的减小误差的方法。研究结果表明离心振动台模型试验要进行合理的设计;离心力场中的系统误差可通过设计模型尺寸大小和选择模拟模型和原型应力保持一致的高度来减小;传感器测量误差可采用在传感器周围填充满砂土来减小;多层剪切层铝合金框架模型箱能够最大程度减小边界效应的影响。     

Shaking table test to assess seismic response differences between steep bedding and toppling rock slopes

In order to investigate the seismic response of steep bedding and toppling rock slopes, a large-scale shaking table test was performed taking into consideration a variety of factors such as slope type and input seismic excitation. Diverse seismic responses, including acceleration and earth pressure at several locations, were analyzed in terms of the test results. It was found that the slope type has a significant impact on the failure mechanism and response norm of different kinds of rock slopes. Firstly, the slide surface of the steep bedding rock slope is basically parallel to the slope surface, while that of toppling rock slope skews the rock layer under seismic load. The failure zone area of the toppling rock slope is smaller than that of the bedding rock slope, which is mainly because it consumes plenty of seismic energy to break through the rock layer of the toppling rock slope. In addition, for acceleration along the vertical direction, an abrupt amplifying effect exists at the top slope when the peak input motion acceleration (PIMA) exceeds a certain value: 0.6 g for a bedding rock slope and 0.4 g for a toppling rock slope. Meanwhile, for acceleration along the horizontal direction, the acceleration amplifying factors of toppling rock slopes are larger at the slope surface but smaller at the inner slope portion than that of bedding rock slopes. Furthermore, the acceleration amplifying factor is larger than the earth pressure amplifying factor at the slope surface. The earth pressure amplifying factor at the top surface for a toppling rock slope is close to that of a bedding rock slope with an increase in PIMA. This novel experiment reveals the different failure mechanisms between steep bedding and toppling rock slopes, as well as being of help to the conduct of further study on seismic hazard early warnings.

     

基于EEMD与SE的IPSO-LSSVM模型在坝肩边坡变形预测中的应用

坝肩边坡变形在外部因素影响下呈现出不确定性和随机性,从而不易预测。基于聚类模态分解(EEMD)、样本熵(SE)和改进型粒子群算法优化的最小二乘支持向量机(IPSO-LSSVM)方法,提出一种名为EEMD-SE-IPSO-LSSVM的坝肩边坡变形预测模型。首先,利用EEMD将原始坝肩边坡变形时间序列分解为若干个不同复杂度的子序列,并基于SE判定各子序列的复杂度,将相近的子序列进行合并重组以减少计算规模;然后,分别对各重组子序列建立IPSO-LSSVM预测模型;最后,将各预测分量进行叠加重构,得到最终的大坝变形预测值。以澜沧江苗尾水电站左岸坝肩边坡为例,将BPNN、RBFNN、LSSVM、EEMD-SE-LSSVM与EEMD-SE-PSO-LSSVM进行对比研究。结果表明,该模型的计算精度优于其他神经网络模型,具有较好的适宜性和稳定性,是一种可靠的坝肩边坡变形预测方法,能够为大坝安全监测提供有价值的参考。