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该文讨论在软基地区修建高速公路时,用碎石桩进行软基处理效果的检测方法,并对其合理运用提出了建议。 相似文献
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Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (Nc), N values have been corrected (Nc) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function Nc=Nc (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to Nc value, is to be approximated in which Nc value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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Spatial variability of liquefaction potential in regional mapping using CPT and SPT data 总被引:5,自引:0,他引:5
Cone penetration test (CPT) and standard penetration test (SPT) are widely used for the site specific evaluation of liquefaction potential and are getting increased use in the regional mapping of liquefaction hazard. This paper compares CPT and SPT-based liquefaction potential characterizations of regional geologic units using the liquefaction potential index (LPI) across the East Bay of the San Francisco Bay, California, USA and examines the statistical and spatial variability of LPI across and within geologic units. Overall, CPT-based LPI characterizations result in higher hazard than those derived from the SPT. This bias may result from either mis-classifications of soil type in the CPT or a bias in the CPT simplified procedure for liquefaction potential. Regional mapping based on cumulative distribution of LPI values show different results depending on which dataset is used. For both SPT and CPT-based characterizations, the geologic units in the area have broad LPI distributions that overlap between units and are not distinct from the population as a whole. Regional liquefaction classifications should therefore give a distribution, rather than a single hazard rating that does not provide for variability within the area. The CPT-based LPI values have a higher degree of spatial correlation and a lower variance over a greater distance than those estimated from SPTs. As a result, geostatistical interpolation can provide a detailed map of LPI when densely sampled CPT data are available. The statistical distribution of LPI within specific geologic units and interpolated maps of LPI can be used to understand the spatial variability of liquefaction potential. 相似文献
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用标贯击数确定抗液化振动挤密砂桩的间距 总被引:2,自引:0,他引:2
在考虑了抗液化振动挤密砂桩的侧向和竖向加密的同时,又考虑了砂桩的充盈系数对加固作用的影响,从而提出了一种基于抗震规范的液化判别方法,根据标准贯入击数确定抗液化振动挤密砂桩间距的经验关系。 相似文献
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