Restoration of opencast coal sites frequently involves the controlled compaction of heterogeneous arisings, and accurate prediction of the settlements associated with such backfill is difficult. Attention has previously focussed on improving the specifications used to control backfilling as a way of both reducing the magnitude of the settlements and improving their predictability. However, there still exists a large degree of uncertainty about the fundamental particulate interactions that occur within a fill. The range of mechanisms previously considered to be influential on creep behaviour is described.
A current research programme is investigating the use of X-ray computer tomography (CT) as a means of nondestructively observing particle interactions during settlement, in conjunction with both long-term creep tests and short-term compressibility tests. The principles of this radiographic technique, which is relatively new to geotechnical engineering, are described, and findings are presented that illustrate the potential of the method.
Interim findings indicate that large particles are distributed on an apparently random basis within a fill, and the nonuniform distribution of voids is clearly demonstrated. Moreover, the results presented conflict to some degree with the general consensus of opinion that particle crushing is a major mechanism in the settlement process. Rather, local collapse into small voids left by compaction, and relative sliding and rotation of particles, seem to be the dominant factors for a range of compaction efforts. Particle splitting is discernible on some sections, but this mechanism appears to be less significant than others. Research is continuing into the time dependency of the observed mechanisms, the effects of moisture content changes and the effects of heterogeneous initial particle strengths. 相似文献
影响CFG桩(Cem ent F lyash G ravel P ile)复合地基的沉降变形因素较多。本文应用有限差分(FLAC3D)程序,对褥垫层、荷载、桩长、桩土模量比,置换率、桩周土和桩端土模量比以及桩群等影响沉降的因素进行了系统的分析,分析了这些因素与沉降的内在联系,得到减小复合地基沉降的方法和措施,对实际工程优化设计有一定的借鉴作用。 相似文献
Acquiring and formalizing cartographic knowledge still is a challenge, especially when the generalization process concerns small-scale maps. We concentrate on the settlement selection process for small-scale maps, with the aim of rendering it more holistic, and making methodological contributions in four areas. First, we show how written specifications and rules can be validated against the actual published map products, thus pointing to gaps and potential improvements. Second, we use data enrichment based on supplementing information extracted from point-of-interest data in order to assign functional importance to particular settlements. Third, we use machine learning (ML) algorithms to infer additional rules from existing maps, thus making explicit the deep knowledge of cartographers and allowing to extend the cartographic rule set. And fourth, we show how the results of ML can be transformed into human-readable form for potential use in the guidelines of national mapping agencies. We use the case of settlement selection in the small-scale maps published by the Polish national mapping agency (GUGiK). However, we believe that the methods and findings of this paper can be adapted to other environments with minor modifications. 相似文献
