岩质边坡稳定性分析的探讨

通过最大剪应力等值线来确定边坡的滑动面,并与其结构面抗剪强度相比较,给出了确定边坡的稳定系数的方法。     

南京地铁联络通道冻结法施工措施分析

对南京地铁一期工程TA4标联络通道冻结法施工的成功经验进行了总结。指出地铁联络通道冻结法施工中,必须采取必要的施工技术措施,才能保证施工安全、顺利的进行。本工程的成功经验可供其他工程参考。     

高产高效综放工作面端面顶板稳定性监测

通过对支架与围岩关系的研究,分析了支架与顶板的相互作用机理,得出支架支护阻力与端面顶板下沉量p–△ld具有类双曲线关系,这种类双曲线关系是高产高效综放工作面端面顶板稳定性控制的基础。根据高产高效综放工作面的开采特点提出了端面顶板监测控制的特点和要求,开发建立了在线监测体系,现场应用取得了良好的技术经济效益。     

基桩缺陷逐步能量恢复递推定量分析

通过大量正演计算,对不同桩周土条件下缺陷程度与时域速度响应波形中入射波波幅与反射波波幅的比值进行了相关性分析,拟合出桩侧土为粘土、砂土、粉土条件下桩径变化程度与波幅比之间的关系式。通过理论分析,在基桩存在多缺陷时,导出了缩颈与扩颈对下一个缺陷引起的反射波幅值的影响系数,进而,提出了逐步能量恢复递推缺陷程度的理论与方法,在时间域中,实现了基桩存在多缺陷时的低应变动测缺陷量化分析。模型桩试验分析结果表明,缺陷量化分析方法可取得显著效果。     

废弃防空洞及软弱地基的加固处理

针对工程填土地层及废弃防空洞,拟采用锤击沉管挤密碎石桩法进行加固处理。但在处理部分防空洞后,因震动大、噪音高,对周边居民和民居影响严重而停工。通过多次试验,摸索出钻孔夯填法并结合灌浆法回填防空洞,喷浆桩法加固软弱地基等经验,并对废弃防空洞的回填加固机理进行了简要分析。经检测,加固处理效果显著。至今,住宅楼建成4年使用正常。     

济宁二号矿孤岛工作面冲击矿压危险及其控制

孤岛工作面及其周围巷道附近应力集中程度高,顶板运动剧烈,再加上地质构造的影响,采深较大时,冲击危险程度就高,很容易引发冲击矿压。《煤矿安全规程》要求孤岛工作面按冲击矿压危险工作面来管理。孤岛工作面冲击矿压危险检测预报及控制的技术是先分析冲击危险程度,确定冲击危险指数,提出早期预报;采用电磁辐射和钻屑法进行及时预报;采用卸压爆破进行处理,并采用电磁辐射和钻屑法检验防治措施的效果。实践证明,这套技术安全、可靠、有效,能够保证工作面的安全高效生产。     

土体颗粒破碎研究进展

在打入桩的桩端以及大型土石坝、海洋平台等的地基中常常会出现高应力区。在这些高应力区中,作为地基的粒状土的颗粒会发生不同程度的破碎。对高应力水平下粒状土体的破碎特性进行深入研究有助于解决这一类型的岩土工程问题。因此近年来土体颗粒破碎逐渐引起人们的重视。本文对土体颗粒破碎研究现状进行了详尽的阐述,并指出研究中存在的问题及今后研究发展方向。     

桩基模型试验研究现状

简要地介绍了桩基现场模型试验、桩基渗水力模型试验、桩基室内普通模型试验的研究成果及其模拟桩基特性的局限性,并着重分析了桩基离心模型试验的研究成果及其模拟桩基特性时的优点和局限性。     

Regional Fault Systems of Qaidam Basin and Adjacent Orogenic Belts

The purpose of this paper is to analyze the regional fault systems of Qaidam basin and ad-jacent orogenic belts. Field investigation and seismic interpretation indicate that five regional fault sys-tans occurred in the Qaidam and adjacent nment.qin belts, controlling the development and evolution of the Qaidam basin. These fault systems are: (1)north Qaidam-Qilian Mountain fault system; (2) south Qaidam-East Kunhm Mountain fault system;（3）Altun strike-slip fault system; (4) Elashan strike-slip fault system, and (5) Gansen-Xiaochaidan fault system. It is indicated that the fault systems controlled the orientation of the Qaidam basin, the formation and distribution of secondary faults within the basin,the migration of depocenters and the distribution of hydrocarbon accmnulation belt.     

Development of Geological Data Warehouse

Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in fifferent levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.