Effect of Inter-Column Spacing on Soil Stresses due to Vibro-Installed Stone Columns: Interesting Findings

This paper reveals the interesting relation between the inter-column spacing and the corresponding alteration of soil state of stresses due to the vibro-installation technique. This relation is inferred from analyses for load settlement records of various field load tests, performed for stone columns arrangements with different inter-column spacing values. In order to have adequate confidence in the findings, a well-documented case history, involving three columns patterns along with their relevant field and laboratory test results, is utilized for this study. Moreover, a well-tested finite element model, capable of simulating both elasto-plastic and time dependent soil deformations as well as pore water pressure building and dissipation, is employed in the analysis. Instead of determining the soil response to the test load, based on known initial soil stresses and material properties, the analysis is inversely posed to determine the soil initial stresses, based on the recorded settlements and the post-installation material properties. The alteration in the soil state of stress is represented by the increase in the post-installation horizontal to vertical stress ratio, K*, as a function of the inter-column spacing. It is found that this alteration experiences a systematic decrease in its magnitude as the inter-column spacing increases.     

基于模糊神经网络(FNN)的赤潮预警预测研究

为研究各种理化因子与赤潮藻类浓度间的非线性对应规律和有效预测赤潮藻类浓度,构建了基于BP 算法的一个四层模糊神经网络模型。将模糊神经网络(FNN)技术引入赤潮预测研究,并与普通 BP 网络、RBF 网络的结果作比较,结果表明,该模型能够较好地反演出各种理化因子与夜光藻密度的非线性对应变化规律,有更好的预测功能。     

变形分析的神经网络技术应用实例

大型工程施工过程中的变形监测、分析与预报极为重要。变形分析建模的方法很多,神经网络技术的应用是其中之一。文中结合某深基坑工程的监测资料和工作经验,运用神经网络BP算法进行预测分析。简述BP神经网络的基本概念,介绍基坑变形分析的BP神经网络的具体模型结构,将神经网络方法预报结果与实测数据对比效果较好。该成果对生产实践具有参考价值。     

斜坡应力分布的系统测试与反演分析

采用简易、快速、经济的洞壁应力恢复法对某工程边坡勘探平硐洞壁应力场作了系统测试, 并采用FLAC2D软件对测试成果作了斜坡初始应力的反演分析。结果表明, 斜坡应力场随深度变化具有明显分带性, 应力量值随硐深曲线具有典型的驼峰式特征, 与理论分析结果相符, 表明该方法能较好地应用于斜坡应力场的系统测试和研究。成果对认识斜坡岩体的变形破裂及卸荷特征等具有重要意义。     

含矿预测的人工神经网络方法

运用神经网络模型的一典型模型——“反向传播”模型的改进形式,处理矿产资源统计预测问题,得出与数量化理论Ⅱ处理极为相似的结果.     

Evaluation of liquefaction potential using neural-networks and CPT results

In this research, a reliable Cone Penetration Test data set was gathered with a wide range of parameters. This data was incorporated in a Neural-Networks computer software called STATISTICA Neural-Networks. The back propagation algorithm with a multilayer perceptron network is utilized to analyze the liquefaction occurrence in different sites. In this study, different sets of effective parameters for the neural-network analyses are selected such that to reduce the noise and to obtain more accurate results.Considering the relative importance of effective parameters in liquefaction assessment, it is indicated that σ₀, σ′₀ together play a more important role than what previously was assumed and hence the relative importance of the q_c and seismic parameters are decreased compared with the previous works. The results presented here have more accuracy than previous works while at the same time, the range of the parameters used in this study is much wider than what was previously used. This range of parameters makes the proposed method applicable for practical purposes.     

Analysis of the Chiufengershan landslide triggered by the 1999 Chi-Chi earthquake in Taiwan

A disastrous earthquake rocked Taiwan on September 21, 1999, with magnitude M_L=7.3 and an epicenter near the small town of Chi-Chi in central Taiwan. The Chi-Chi earthquake triggered landslide on the dip slope at the Chiufengershan. In this study, a review of the topography and geology of this area was followed by field investigations. Laboratory testing was applied to understand the geomaterial composing the slope. Then, based on a series of limit equilibrium analyses, the failure mechanism of this landslide and the risk of the residual slope were studied.

According to the stability analyses, the pre-quake slope is quite stable, with factor of safety of 1.77 (dry) to 1.35 (full groundwater level); explaining why there is no written record of a landslide here for the past 100 years. In contrast, a back analysis shows that the Chi-Chi earthquake-induced dynamic loading is far more than the dip slope can sustain, due in part to the short distance to the epicenter. A Monte Carlo type probability analysis suggests that the residual slope is more dangerous than the pre-quake slope and needs more attention.     

Slope stability evaluation using Back Propagation Neural Networks

The Yudonghe landslide, located in western Hubei Province of China, consists of eastern and western subunits as well as a main landslide mass with upper and lower slip surfaces. As an important landslide close to Shuibuya Dam on the Qing River, its stability is crucial, as the slide might reactivate because of a change in ground-water level caused by filling of the Shuibuya Reservoir. Existing weakness zones, growth of ruptures, the downslope attitude of geologic strata, and water infiltration, which reduced the strength of rocks and soils, have been found to be the most important factors contributing to the Yudonghe landslide. With regard to the landslide processes, it can be noted that the original large-scale slide activity was due to erosion by the Qing River, the second sliding resulted from the fall of blocks from the head scarp, and the final activity was the growth of the eastern and western secondary slides. A base failure was the main type of slope movement, however, it was obvious that more than one sliding event occurred, as inferred from striations and fractures detected by microstructure analysis of soils along the failure surfaces. Slope instability was evaluated by the method of Back Propagation Neural Networks (BPNN), in which a four-layer BPNN model with five input nodes, two hidden layers, and two output nodes was constructed using a training data set of landslide samples throughout the Qing River area. The predicted results of this analysis showed that the factor of safety was 1.10, which indicates that the Yudonghe landslide is currently in a marginally stable condition.     

天山南麓雷暴日短期预报研究

利用阿克苏及邻近地区12个气象站1980—2013年雷暴资料,以及同期高空资料,统计了各站年均雷暴日数,对发生区域雷暴天气的环流形势进行分类,归纳出各型的入型指标。通过逐步回归法,建立阿克苏及邻近地区区域雷暴概率回归预报模型,并对2013年进行试预报。结果表明:(1)阿克苏及邻近地区区域雷暴的影响系统主要分为4类:巴湖低槽型、急流型、西北气流型和温度槽型。(2)对2002—2012年5—9月(共1683 d)历史资料进行判别,满足入型条件的样本数为876 d,消空率为48%;对2013年5—9月(共153 d)历史资料进行判别,入型样本数为80 d,消空率为48%。(3)对2002—2012年5—9月所有入型样本进行回代检验,平均准确率为72.0%(平均TS评分为30.1%);对2013年5—9月所有入型样本进行试预报,平均准确率为63.2%(平均TS评分为28.2%)。     

腰椎管侧隐窝分型及影响侧隐窝狭窄症诊断的几个因素（附120例CTM分析）

作者对１２０例因下腰痛手术治疗在术前进行过ＣＴＭ检查的患者作了影像学及临床分析．按形态不同，侧隐窝可分为五种类型：（１）三角型，即整个椎管呈倒三角形，其侧隐窝完全开放；（２）猫耳型，因后关节和椎体后缘轻度增生，形成底宽顶尖的侧隐窝，整个椎管形如猫头，侧脸窝为猫耳，基本开放；（３）牛角型，因关节突向椎体方向过度增生使侧隐窝呈横向伸开的带状，中央椎管不窄，横断面形如牛头，侧阴窝为牛角，严重者侧隐窝可近于闭塞；（４）三叶草型，极度增生的关节突加上向后实入的椎体后缘使整个椎管呈三叶草状，侧隐窝和中央椎管均狭窄；（５）混合型，多为上述形状的混合或两侧侧隐窝发育不对称．前二型不易形成对神经的压迫，后三型易造成压迫．但即使最狭窄的侧隐窝也不一定产生症状，因为关键在于神经根和侧隐窝的宽度的相对关系．因此作者提出单纯依靠侧隐窝的宽度不能诊断侧隐窝狭窄症，而是要判断神经是否被侧隐窝压迫．因此ＣＴＭ在侧隐窝狭窄症的诊断中有突出意义．