一、Analysis of Seismic Risk at an Engineering Site from Site Effect Seismic Intensity Data Using the Seismotectonic Method——Taking Six Reservoir Dam Sites in Western Anhui as an Example（论文文献综述）
Shahzada Khurram,Perveiz Khalid,Jahanzeb Qureshi,Zia Ud Din[1]（2021）在《Assessment of seismic hazard of roller compacted concrete dam site in Gilgit-Baltistan of northern Pakistan》文中指出The proposed site of the Diamer Bhasha Dam in northern Pakistan is situated in an active tectonic zone with intensive seismicity, which makes it necessary for seismic hazard analysis（SHA）. Deterministic and probabilistic approaches have been used for SHA of the dam site. The Main Mantle Thrust（MMT）, Main Karakaram Thrust（MKT）, Raikot-Sassi Fault（RKSF） and Kohistan Fault（KF） have been considered as major seismic sources, all of which can create maximum ground shaking with maximum potential earthquake（MPE）. Deterministically estimated MPE for magnitudes of 7.8, 7.7, 7.6, and 7.1 can be produced from MMT, MKT, RKSF and KF, respectively. The corresponding peak ground accelerations（PGA） of 0.07, 0.11, 0.13 and 0.05 g can also be generated from these earthquakes, respectively. The deterministic analysis predicts a so-called floating earthquake as a MPE of magnitude = 7.1 as close as 10 km away from the site. The corresponding PGA was computed as 0.38 g for a maximum design earthquake at the project site. However, the probabilistic analysis revealed that the PGA with 50% probability of exceedance in 100 years is 0.18 g. Thus, this PGA value related to the operational basis earthquake（OBE） is suggested for the design of this project with shear wave velocity(Vs30) equal to 760 m/s under dense soil and soft rock conditions.
Yao Wang,Chi-hui Guo,Shu-rong Zhuang,Xi-jie Chen,Li-qiong Jia,Ze-yu Chen,Zi-long Xia,Zhen Wu[2]（2021）在《Major contribution to carbon neutrality by China’s geosciences and geological technologies》文中研究指明In the context of global climate change, geosciences provide an important geological solution to achieve the goal of carbon neutrality, China’s geosciences and geological technologies can play an important role in solving the problem of carbon neutrality. This paper discusses the main problems, opportunities, and challenges that can be solved by the participation of geosciences in carbon neutrality, as well as China’s response to them. The main scientific problems involved and the geological work carried out mainly fall into three categories:（1） Carbon emission reduction technology（natural gas hydrate, geothermal, hot dry rock, nuclear energy, hydropower, wind energy, solar energy, hydrogen energy）;（2） carbon sequestration technology（carbon capture and storage, underground space utilization）;（3） key minerals needed to support carbon neutralization（raw materials for energy transformation, carbon reduction technology）.Therefore, geosciences and geological technologies are needed: First, actively participate in the development of green energy such as natural gas, geothermal energy, hydropower, hot dry rock, and key energy minerals, and develop exploration and exploitation technologies such as geothermal energy and natural gas; the second is to do a good job in geological support for new energy site selection, carry out an in-depth study on geotechnical feasibility and mitigation measures, and form the basis of relevant economic decisions to reduce costs and prevent geological disasters; the third is to develop and coordinate relevant departments of geosciences, organize and carry out strategic research on natural resources, carry out theoretical system research on global climate change and other issues under the guidance of earth system science theory, and coordinate frontier scientific information and advanced technological tools of various disciplines. The goal of carbon neutrality provides new opportunities and challenges for geosciences research. In the future, it is necessary to provide theoretical and technical support from various aspects, enhance the ability of climate adaptation, and support the realization of the goal of carbon peaking and carbon neutrality.
Qaiser Mehmood[3]（2021）在《长江第一湾大同沟、台村沟的泥石流敏感性评估和冲出范围预测》文中指出山区经常发生灾难性泥石流,这对人类社会经济、生命财产和安全都造成了严重的威胁。泥石流通常在暴雨、融雪条件下才会发生,因其没有任何预兆,成为最具破坏性的地质灾害之一。随着中国山区的快速开发,导致了泥石流事件时常发生,使得中国成为世界上泥石流受灾最严重的国家之一,特别是西南山区的泥石流灾害最为严重。因此进行山区泥石流敏感性评价和泥石流冲出范围预测的相关工作具有重要意义。本论文以长江第一湾附近的大同沟和台村沟作为实例,采用现场调查、敏感性评价和数值模拟的综合方法对这两条泥石流沟的敏感性和潜在冲出范围进行了评价和预测。通过野外调查,对泥石流沟的形成,运动和堆积过程机理有了基本的认识与把握。利用遥感影像获取了泥石流沟的基本几何参数信息。通过参考前人研究,并结合研究区泥石流沟的基本特征,选取了坡度、地面湿度指数、输沙指数、地形湿度指数、流域面积、主沟弯曲系数、物源、归一化植被指数共8个影响因子作为泥石流沟敏感性评价指标。采用层次分析法确定了8个影响因子的相对重要程度:坡度（0.314）>地形湿度指数（0.249）>输沙指数（0.168）>地面粗糙度（0.091）>流域面积（0.078）>弯曲系数（0.042）>归一化植被指数（0.025）。根据8个影响因子的综合权重系数和单因子关联度,采用可拓理论分别计算了大同、台村沟泥石流的敏感性,得到两条泥石流沟的敏感性均为中等。通过层次分析法与可拓理论相结合较好的解决了泥石流敏感性评价这一复杂的地质问题。利用吉林大学建设工程学院开发的基于浅水流模型的软件“SFLOW”,对泥石流的演化和冲出过程与范围进行了预测。对大同沟和台村沟分别模拟了以10年、20年、50年和100年为回归周期的泥石流事件。得到这两条泥石流沟的冲出范围最远可达到主干道,甚至可以完全阻断金沙江。上述的危险性评价和冲出范围预测可为决策者提供指导性意见。
Kaleem Ullah Jan Khan[4]（2021）在《降雨入渗触发非饱和煤矸石堆积边坡失稳的机理研究》文中研究说明土坡的稳定性与降雨渗透过程有关,其特点是剪切强度降低以及吸力损失,从而最终导致了失稳。土坡失稳主要发生在降雨期间或降雨后,虽然已经被证明为降雨渗透导致土体强度降低以及孔隙水压力迅速增加而造成,但引发斜坡失稳的重要影响机理还没有得到充分的讨论。降雨强度和降雨持续时间对边坡稳定性的影响,但降雨渗透过程对煤矸石堆积边坡稳定性的影响尚需深入研究。为此研究降雨入渗过程对土坡稳定性的影响,尤其对世界范围内广泛分布的矿渣堆积体稳定性及环境安全具有重要意义。降雨特征（降雨强度和持续时间）和土体的导水性影响着边坡的稳定状态及失效类型。一般来说,土坡破坏的机理有两种,即降雨湿润锋的传播导致垫层吸力的丧失以及地下水位的上升。到目前为止,仍然没有明确的指标来确定导致矿渣型边坡失效的主导参数。因此,本研究在开展东北阜新地区一煤矸石堆积边坡失稳机制研究中考虑了降雨渗透过程。通过收集已有资料、现场调查、实验室试验和数值模拟方法,较深入研究了煤矸石堆积边坡降雨诱发的失稳过程及其机理。采用渗流-应力耦合和非耦合有限元分析方法,对煤矸石堆积边坡的失效机理进行评价。采用物理模型测试分析了不同降雨条件下土坡中孔隙水压力的分布特征,采用有限元法数值方法按照渗流-应力耦合和非耦合两张方式研究了不同降雨持续时间（1天、2天、3天、4天和5天）和降雨强度对边坡变形破坏的影响。通过在坡面不同位置（坡顶、坡中、坡尖）设置监测点,观察并比较每次降雨后孔隙水压力的变化、变形规律和稳定性。煤矸石的高渗透性与在坡趾附近观察到的因水流而产生的最大变形进一步解释了坡体向下运动的原因,与现场观测和物理模型的观测结果有较好的一致性。研究结果还表明,渗透率的增加导致孔隙水压力的滞后性增加,土坡稳定系数下降。耦合分析中的安全系数（应力和孔隙水压力的耦合效应）与未耦合分析（水力反应效应）相比显着降低。为防止边坡进一步破坏,防止其对道路交通的影响,采用锚固与水平排水相结合的设计。建议在边坡顶部采用混凝土梁锚杆加固路基,在坡脚附近设置桩排。引入水平排水系统,分流雨水,保护边坡和道路的顶部和底部。采用极限平衡Bishop法分析了桩锚加固的效果,评价了加固方案对边坡安全系数的影响。结果表明:桩锚加固土边坡的安全系数由0.9提高到1.14;利用工程价值研究,在滑坡区设计水平排水系统,控制滑坡体内过量降雨入渗,保护坡顶和坡底道路,分流雨水。
张俊荣[5]（2021）在《三峡库区滑坡多场信息监测与组合式预测模型优化研究》文中研究指明滑坡是自然界中分布最广泛,最频繁发生的地质灾害之一,对环境,自然资源,水利工程等构成了巨大的威胁。以三峡工程为例,自2003年首次蓄水以来,规模较大的黄土坡滑坡、黄蜡石滑坡等古滑坡坡表再次出现明显的变形特征,导致附近城市和120万人被迫搬迁。在三峡水库地区的5000处滑坡或潜在滑坡中,大多数的滑坡以每年6厘米至1.5米的速度移动,有些速度甚至不到6厘米/年。由于其具有数十年乃至几个世纪的缓慢变形趋势,长期对人类生产工程活动造成威胁,开展对这类滑坡的监测方法与位移预测方法的研究具有重要意义。目前,水库滑坡的监测方法与位移预测方法研究主要存在两方面的需求。一方面,由于滑坡变形破坏的呈现是一个时空动态演化过程,常伴随着多场耦合、大变形等特征。然而,目前尚未能够提出针对渐变滑坡演化全过程的监测方法,现有的深部监测技术不能适应滑坡大变形特点,也缺乏在原位条件下实现“一孔多测”的多信息参数监测技术等。另一方面,随着信息时代的算法与模型的大爆炸,针对渐变滑坡变形演化规律,挖掘诱发因素历史信息中的频率成分,大量采用新算法,新的模型架构以发展新的滑坡预测模型更是尤为迫切。本文选取了三峡库区四个典型的慢速滑坡为研究对象,主要研究内容与研究成果如下:（1）三峡库区水库滑坡的变形监测累计位移曲线具有明显的“阶梯状”特征。在变形历史中,该类滑坡会经历多次加速变形。每年的5月至8月,滑坡剧烈变形,而在其余月份较为平缓。该变形模式与水库区域降雨的季节性和库水位的周期性波动具有强烈的关联性。三峡库区的蓄水是造成这些滑坡的重要原因。在库水位与降雨的共同作用下,趋于稳定的古滑坡复活后呈现出缓慢的变形状态,并在局部产生诸如裂缝和塌陷的变形特征。由于地形、地质构造与诱发因素的差异,不同区域位置的滑坡变形速率不同。此类滑坡的体积大,影响范围广,存在造成巨大的经济损失的可能,并有可能造成二次灾害。（2）为解决了滑坡演化过程中的大变形,多场演化的监测的问题。设计了三套滑坡监测装置。基于预制磁场概念设计的装置可以有效地利用磁场来实现滑坡变形的非接触式长期监测。避免了传统深部测斜法在大滑坡体变形的情况下测斜管与土壤耦合引起的变形协调问题,其精度相对于传统测斜法更高。设计了一种滑坡深部孔外多场地质信息参数监测装置。将集成传感器插入原位岩土体中进行钻孔外部环境监测,可以实现对滑体钻孔周围岩土体的多场地质信息参数监测,使监测结果更接近于实际的地下环境。同时,考虑到地下环境的复杂性,避免了由于测斜管的布置引起的原位地下环境的扰动。整个装置采用无线供电方式,结构简单,设计合理,经济高效,推广方便。为了给上述滑坡深部孔外多场地质信息参数监测装置提供安全稳定的工作环境,设计了一种钻孔孔壁加固装置,可以同时解决管土耦合问题和钻孔加固问题。对于地质灾害监测与防治领域的工程应用具有重要的现实意义。（3）在白水河滑坡和树坪滑坡研究的基础上,提出了基于CEEMD-DTW和ACO-SVR的预测方法。具有较高分解精度和较高运算效率的CEEMD可以更好地突出诱发因素时间序列的局部波动特征。DTW可以被用来选择周期性位移的最关联诱发因素。提出的混合模型预测结果表明,在考虑滑坡诱发因素的频率分量后,基于ACO-SVR的位移预测模型的预测精度优于基于SVR和GA-SVR的其他模型。（4）在白水河滑坡研究的基础上,提出了基于CEEMD-LCSS重构和ABC-SVR方法的预测模型。在对诱发因素重构的基础上,使用CEEMD对重构诱发因素数据进行预处理,以识别它们的高频,低频和残留项。LCSS的应用能够识别滑坡位移的最关联诱发因素。基于ABC-SVR的组合预测模型的预测精度超过了基于GA-SVR和PSO-SVR的模型的预测精度。以RMSE指标为衡量标准,与模型一对比最多可以将滑坡位移的预测准确性提高54%。（5）在石榴树包滑坡研究的基础上,提出了基于EDR选择和多个群智能的预测模型。石榴树包滑坡的变形与库水位波动,降雨以及当前位移等相关因素密切相关。EDR方法可以识别影响滑坡运动的最关联诱发因素,以用作SVR模型的输入变量。基于DA和GWO的SVR模型分别提供了周期位移和趋势位移的最佳预测效果。（6）从滑坡位移分解方法、诱发因素频率成分提取、位移最关联诱发因素遴选方法以及群智能优化算法选择等四个方面进行了比较试验,以找到预测库区渐变滑坡位移的组合式预测模型的最佳组合。EMD系列模型可用于位移数据的分解和诱导因子的频率分量提取。其中,EEMD方法在滑坡位移分解中结果离散程度小,基于最小二乘法可以得到最好的预测结果。在诱导因素分解方面,CEEMDAN与t检验相结合可以更好地突出诱导因子的频率成分。因此,与其他方法相比,在预测模型的诱发因素频率成分提取中使用CEEMDAN具有竞争力。GRD和其他轨迹相似性判断模型,例如,DTW,LCSS,EDR,可被用于滑坡预测模型输入变量的选择。其中,LCSS方法的应用可以产生最佳的预测结果,应当作为位移最关联诱发因素遴选方法首选方法,其次依次是GRD,DTW和EDR。在测试的所有9种群智能优化算法中,PSO在基于SVR的预测模型中的应用都能得到最优的预测结果。因此,此类具有四个重要步骤的组合式预测模型的最佳组合为EEMD-CEEMDAN-LCSS-PSO。本文创新点总结如下:（1）针对水库区域滑坡演化过程中的多场耦合、大变形等特征,研发了三个可以高精度,高可靠性和高耦合度捕获滑坡多场信息的演化特征的设备。包括:一种滑体预制磁场及变形响应装置;一种滑体深部孔外多地质信息参数监测装置;一种钻孔孔壁加固装置。（2）以水库区域慢速移动滑坡监测数据为基础,考虑了滑坡诱发因素的频率成分,探讨了CEEMD在滑坡位移分解与诱发因素频率成分提取中的应用;首次将时间序列相似度评价方法（诸如动态时间归整算法DTW、最长公共子序列算法LCSS、实序列编辑距离EDR）作为时间序列最关联评价方法应用;首次将多种群智能优化算法（诸如麻雀搜索SSA、蚁狮优化算法等）应用于滑坡位移预测模型优化,并与常用经典算法的优化性能做比较。基于上述研究,提出了三个组合式预测模型,并通过三峡库区滑坡案例的应用与对比检验了其泛化性能。（3）将组合式预测模型常用的架构概括为四步骤,开展了每一步骤中上述应用方法的对比试验,比较了同一步骤不同方法下的预测模型的泛化性能,获得了基于SVR预测模型在四步骤架构的最优组合。
Xinglin LEI,Jinrong SU,Zhiwei WANG[6]（2020）在《Growing seismicity in the Sichuan Basin and its association with industrial activities》文中指出In the Sichuan Basin, seismic activity has been low historically, but in the past few decades, a series of moderate to strong earthquakes have occurred. Especially since 2015, earthquake activity has seen an unprecedented continuous growth trend, and the magnitude of events is increasing. Following the M5.7 Xingwen earthquake on 18 Dec. 2018, which was suggested to be induced by shale gas hydraulic fracturing, a swarm of earthquakes with a maximum magnitude up to M6.0 struck Changning and the surrounding counties. Questions arose about the possible involvement of industrial actions in these destructive events. In fact, underground fluid injection in salt mine fields has been occurring in the Sichuan Basin for more than70 years. Disposal of wastewater in natural gas fields has also continued for about 40 years. Since 2008, injection for shale gas development in the southern Sichuan Basin has increased rapidly. The possible link between the increasing seismicity and increasing injection activity is an important issue. Although surrounded by seismically active zones to the southwest and northwest, the Sichuan Basin is a rather stable region with a wide range of geological settings. First, we present a brief review of earthquakes of magnitude 5 or higher since 1600 to obtain the long-term event rate and explore the possible link between the rapidly increasing trend of seismic activity and industrial injection activities in recent decades. Second, based on a review of previous research results, combined with the latest data, we describe a comprehensive analysis of the characteristics and occurrence conditions of natural and injection-induced major seismic clusters in the Sichuan Basin since 1700. Finally, we list some conclusions and insights, which provide a better understanding of why damaging events occur so that they can either be avoided or mitigated, point out scientific questions that need urgent research, and propose a general framework based on geomechanics for assessment and management of earthquake-related risks.
Rana Muhammad Ali Washakh[7]（2020）在《喜马拉雅中部阿润河谷高山环境下的冰湖溃决洪水研究》文中进行了进一步梳理冰川湖泊溃决洪水（GLOF）是一种被研究人员广泛认识的自然灾害,其爆发后的巨大破坏能力对自然生态环境和人造基础设施安全等都构成严重威胁。因此,进行GLOF风险评估是非常必要的,特别是在有水电站的流域内,如果不进行GLOF风险评估,可能会造成巨大的社会经济损失。此外,还需要综合汇编水文、地质、地貌和气候数据,以便对水电站等重要项目进行可行性研究和设计研究。值得注意的是,文献中已有很多方法可用于评估冰湖溃决洪水。这些方法通过方法构造的类型、评价特征的数量和选择、评价过程中所需的输入数据和主观性比率来区分,有些是适应环境设计的,有些是设计来适应的。评价程序对输入数据的要求和主观性的要求通常被认为是其重复使用的根本障碍。最近的一项研究考察了这些方法在高山环境中的适用性。结果表明,所采用的方法均不满足所有规定的标准,且需要耗费大量的人力、时间和成本,因此,需要一种新的方法来适用于高山环境中水电项目安全性评估。此外,由于现有的GLOF风险评估方法的主客观局限性,我们提出了一种新的、易于应用的基于历史冰湖溃决洪水流量和影响规模的四步筛选方法,应用范围更广,不需要根据主题区域进行适应变化,这也允许成本效益和时间效益重复使用这个模型,因为它不保证一个大的团队,数月的分析,昂贵的设备和现场旅行和样品收集。在这项研究中,我们的工作重点是在阿伦河流域的上阿伦水电站项目（UAHEP）,除了建立水文、地质、地貌和气候数据库之外,我们还采用了一种新的、更全面的方法来确保水电站的安全项目。在本研究第一阶段,确定了49个面积大于0.1km2的冰川湖,以供进一步分析。对这49个冰川湖进行了1990-2018年间的地理描述和地貌分析,以便在根据本研究提出的新模型进行严格筛选之前,在初步阶段更好地了解在自然和人为环境下冰川湖的性质。在第二阶段,取消或增加气候变化因子及其对研究区冰川湖泊详细风险评估的影响是非常重要的,因此,我们分析了该地区气温、降水趋势与记录的GLOF事件的发生之间的关系,发现GLOF的规模随降水量和温度的增加而增加可能是影响研究区冰川湖泊详细风险评估的主要因素。然而,从1960年到2018年的气候分析表明,气候变化与全球变暖频率之间的关系尚不清楚;在第三阶段,补充编制水文、地质、地貌和气候数据资料,以协助基础设施的设计和建设,具体如下:据此计算,UAHEP坝址处的年平均流量约为217 m3/s。100年一遇的大坝和发电站坝址洪水预计分别为2620 m3/s和2980 m3/s。编制了SWAT模型,并用于模拟大坝和发电站现场的可能最大洪水（PMF）。UAHEP坝址处的PMF洪峰估计为4990 m3/s。UAHEP厂房址处的PMF洪峰估计为6060 m3/s。区域地质研究表明,研究区位于下喜马拉雅中陆单元。这一地区存在诸如MCT、std、MBT、MFT和MHT等冲断层。其中,MCT向北延伸,位于拟建坝址的东侧和西侧。MCT与项目坝址之间的最短距离为3至5公里。根据最新的地震危险性评估,坝址和厂房址的OBE峰值地面加速度（PGA）分别为0.12和0.11g。坝址SEE的PGA为0.74g。储层地质分析表明,储集盆地基岩以片麻岩为主。库区为微风化、新鲜岩体,预计渗透性低,阿伦河谷为区域最低排水点。坝址基岩主要为ZG1区（Ⅱ级）,为微风化新鲜片麻岩。由于该区岩体强度高,通常选作坝址基础。1960-2019年的滑坡分布表明,在273个滑坡中,有15个可能是沿UAHEP主河道的滑坡坝。根据其发生时间进行进一步审查,确定其中5个是相对于2010年的新滑坡,因此,这5个滑坡可能需要进一步研究其对UAHEP的影响。在第四个阶段,基于历史GLOF事件记录的受影响长度和体积,我们提出了一个新的方法来用于识别4个潜在的关键湖泊;并根据历史GLOF事件进行最坏情况的情景模拟,根据受影响的长度范围对湖泊进行筛选,并假设每个湖泊都有爆发的可能性,因为在评估的初步阶段如果多标准评估排除了一些湖泊,但由于这些标准本身的不确定性,导致结果可能仍然是有问题的。另一方面,如果多标准评估确定某些湖泊具有潜在危险,而这些冰川湖泊也不对重点地区构成威胁,这可能是因为它们即使爆发后的洪水路径与大坝/电厂现场之间能达到安全距离。在第五阶段,根据多准则评价、触发因素和破坏机制,确定了爆发概率,继而对湖泊进行了深入细致的研究。通过研究发现在四个冰川湖泊中,下巴伦湖和强中克措湖为临界湖泊。一旦确定易受突发洪水影响的湖泊,洪水模拟和濒危区域的划定是风险管理程序的下一步。在第六阶段,利用MIKE 11软件进行了大坝/电站断面可能流量的数值模拟,专家们认为该方法更适合在喜马拉雅地区应用,并将计算结果与广泛使用的经验方法和历史湖泊观测结果进行了对比分析突出事件,进一步讨论了确定的重点水电站设计推荐临界湖泊的物理性质、触发因素和突出概率。巴伦冰川下湖的GLOF模拟结果表明,该湖的爆发可能引起洪峰高达10144m3/s的洪水流量,洪峰在溃决发生后1小时1分钟到达发电厂址时将衰减到8478m3/s左右。强钟克措冰川湖的GLOF模拟结果表明,强钟克措冰川湖的溃决可能引起8983m3/s的洪峰流量,溃决发生后约1小时14分钟到达坝址时,洪峰将衰减到7576m3/s左右。泄洪发生后约1小时30分钟到达厂址,泄洪流量进一步减弱至6935m3/s左右。两个冰川湖的估计洪峰流量与历史上爆发洪峰流量比较接近。将已确定的两个临界湖泊的突出洪峰估计值与不同经验模型和观测到的历史湖泊突出事件的结果进行比较,结果表明,该估计值与这些结果吻合得很好,事实上,在可接受的范围内,略偏于保守或安全的一面。因此,利用该方法建立的临界湖泊及其溃决洪水的识别模型是合理可行的。此外,虽然本分析所采用的方法和方法是新的,但本研究的结果与世界银行（WB）、尼泊尔电力局（NEA）、Claque和Connor以及ICIMOD一致。2019年10月,我们走访了中国西藏日喀则地方水利局,发现青藏宗阁措浩湖被确定为潜在的危险冰川湖,这与本次研究的结果也是一致的。通过与现有的冰湖溃决洪水敏感性评价方法的比较,我们认为该方法具有以下优点和潜力:基于历史格洛夫影响长度和体积的新模型更适合于工程产品,因为研究单个湖泊的前提条件是最小化,这是由现有文献中的常规方法建议的;可重复性,它允许对冰湖溃决洪水敏感性及其时间演变进行倒退、现在和近期的评价;多重结果原则,允许识别每个湖最可能的GLOF场景,并允许省略在特定情况下不起作用的特征（场景、决策树）;另外,从经济和时间效益上本研究介绍的评估方法并不需要数百万美元的预算、庞大的团队、多年的分析、昂贵的设备以及到冰川湖的现场旅行等,正如在一般大型水电项目的建设,只需要常规的全球环境足迹分析。为了实现这些目标,我们进行了一系列密集和综合的案例研究、数据收集以及GLOF模拟和分析。
Mahmood Ahamd[8]（2020）在《Seismic Soil Liquefaction Hazard Assessment Using Bayesian Belief Networks》文中研究说明地震液化及其引起的侧向位移识别是一个复杂的非线性过程,受到各种不确定性和复杂性因素的影响。然而,随着现场数据可用性的增加,人工智能（AI）技术,如人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）已成功地应用于液化势和液化引起的侧向位移的评估,与现有方法相比,其精度有所提高。然而,大多数人工智能技术都存在一定的局限性,如由于先验知识的使用有限,难以得出评价结果。此外,尽管能够解决地震液化势和液化引起的侧向位移问题,可是预测模型的精度并没有得到很好的说明。因此,地震液化危险性问题仍然是岩土地震专业人员面临的巨大挑战,有必要对液化势的预测及其产生的侧向位移进行更系统、深入的研究。最近,另一种人工智能技术贝叶斯信念网络（BBN）被采用,它允许变量集之间存在概率关系,并提供了一个处理因果关系和不确定性的合适框架。基于可靠的液化后原位试验数据库,利用贝叶斯置信网络建立了新的概率图形模型,用于评价土的液化势和侧向位移。本研究包括以下主要内容:首先,根据影响因素的选择原则,采用系统文献综述法（SLR）确定了地震液化的1 1个重要因素,如震级、离断层破裂面最近距离、土性类型指数、等效净砂贯入阻力等。利用解释结构模型（ISM）建立了多级层次结构,并用MICMAC方法检验了关联度。研究结果为进一步建立概率框架下的地震液化危险性评价模型的贝叶斯置信网络等定量方法提供了更为准确的途径。其次,基于领域知识（DK）,采用解释结构建模（ISM）方法,K2机器学习（ML）算法,建立了 5个地震液化因子的简单概率图形模型,并基于ISM和K2-ML算法对地震液化势进行了评估。利用总体准确度（OA）、准确度、召回率和F-测度等指标来评价所提出模型的性能,为与其他模型进行比较奠定了量化基础。由于混合方法综合了DK和K2-ML算法的优点,克服了用一种方法（DK或ML算法）得出BBN结论的缺点,因此用ISM和K2-ML算法开发的BBN模型比分别用ISM和K2-ML技术开发的BBN模型具有更好的性能。与文献中的ANN模型和C4.5决策树模型相比,BBN-K2模型和DK模型对整个数据集的综合性能评价是一致的,在液化势评价中具有广阔的应用前景。此外,为了扩展以往简单的基于5个因素的概率图形模型的应用范围,基于地震液化的11个重要因素,利用更新的相对较大的静力触探试验（CPT）数据集,提出了一种多因素概率图形模型。通过与C4.5 DT、简化程序和基于进化的性能评估方法的比较,验证了多因素概率图形模型的准确性和鲁棒性。结果表明,多因素概率图形模型优于其它模型。由于多因素概率图形模型具有综合性能好、实用性强、数据驱动性强、能够反映各变量之间的相互作用等优点,在地震液化评价中的应用具有广阔的前景。多因素稳健概率图形模型不仅可以定量预测地震、土壤、场地条件等影响因素下的地震土液化潜在概率,同时也找出主要的诊断原因和故障发现状态的组合,以支持地震土壤液化缓解措施的可持续发展决策。最后,基于解释结构建模技术,提出了的利用贝叶斯信念网络（BBN）方法的液化侧移概率评估框架。所开发的BBN模型通过使用广泛的案例记录数据库进行训练和测试,以预测自由面和倾斜地面条件下的侧向位移,并与常用的多元线性回归（MLR）和遗传规划模型进行了比较。结果表明,BBN模型能够通过一个具有合理精度的因果关系来学习侧向位移及其影响因素之间的复杂关系。在已开发的模型中,不需要添加新的参数（如MLR模型中的R*）或使用影响参数的函数（如对数等）值;所有参数都可以按原样使用到模型中,无需任何规范化或校准。此外,敏感性分析结果表明,“峰值地面加速度amax”和“修正SPT值（N1）60<15的饱和层总厚度内的平均粒径D5015（mm）”是两个BBN模型中最显着的两个因素。
Suresh Chaudhary[9]（2020）在《尼泊尔山区耕地撂荒的社会与生态环境风险研究》文中认为在尼泊尔和世界许多山区国家,撂荒已成为一种普遍现象。耕地撂荒不仅会给国家和地区带来粮食安全风险,同时在高山地区也会产生生态环境安全风险,危及山区生态系统关键服务的能力。本研究以尼泊尔西北高山区为研究对象,对撂荒引发的生态环境风险及其对控制因素（生物物理,社会经济,气候和社区）的依赖性开展研究,以期实现如下目标:（i）揭示撂荒对尼泊尔高山区社会及生态环境带来的影响;（ii）查明尼泊尔西部高山区撂荒的时空变化、原因及相关的生态环境风险;（iii）研发评估撂荒生态环境风险的指数和方法;（iv）提出可持续利用现有撂荒的相关政策和策略建议。通过对尼泊尔撂荒及其生态环境响应的深入研究,为高山区国家和地区景观管理以及国土可持续利用决策提供理论和技术支持。在收集和分析尼泊尔高山区地理特征、耕作制度和社会经济发展的基础上,本研究选取了撂荒现象较为突出的尼泊尔Dordi河流域作为案例研究区域。该研究区位于尼泊尔西部山区的拉姆琼县（28°8′N–28°27′N,84°24′E–84°42′E）,距首都加德满都谷地以西约200公里。研究采取了文献检索、实地调查、无人机及卫星遥感信息提取等方法开展研究。文献检索主要获取有关历史、社会和经济发展等信息信息,同时收集和分析与农业耕地状况和变化指标直接或间接相关的证据;实地调查进行了家庭调查、焦点小组讨论和深入的关键线人访谈,以形象化社会景观及其变化,从而建立山区社会、制度和管理实践的现状和历史。同时开展了地面调查,包括不同时段（1-10年期）撂荒地自然植被恢复、生物多样性、物种入侵、水土流失特征以及撂荒地周边农地及灌溉设施等变化;Google Earth Images和无人机低空遥感勘测（UAV）用于观察流域及撂荒区微地貌与植被变化。在收集和整理数据的基础上,论文首先研究了尼泊尔撂荒地的时空变化,分析了耕地撂荒的驱动因素,并讨论了尼泊尔耕地撂荒的生态环境景观后果。结果表明,尼泊尔撂荒很普遍,但在丘陵和山区更为突出。人口增长、移民、城市化、社会经济发展、自然灾害的发生、土地所有权和分配、土地分割、政治不稳定及其影响是尼泊尔耕地撂荒的主要驱动因素。撂荒导致了社会文化景观和山区生物多样性的变化,加剧了土地退化和自然灾害。这些研究成果可为尼泊尔生态环境管理和社会过程提供有用的信息（第3章）。其次,论文探讨了撂荒的社会影响。研究结果表明,农民的社会习惯包括:（i）本地劳动力交换系统“帕尔马”,（ii）传统管理的灌溉系统,（iii）饮用水供应系统,（iv）社会习惯,仪式,节日活动,（v）土着治理制度,做法和一些现有基础设施（学校、银行、卫生、岗亭、庙宇等）将随着撂荒存在被遗弃的风险。个人和社会参与土地管理做法的减少,增加了撂荒地周边农地撂荒的风险,最终将增加粮食安全风险。这些知识对于理解适当的社会过程,自然资源和环境管理至关重要（第5章）。第三,研究了撂荒地的生态环境变化、土地退化过程及风险。在被调查的全部撂荒耕地中,有92%已经完全不可逆转地受到破坏。破坏始于耕地撂荒后一年内梯田田坎的破坏和冲沟的出现,并进而引起了各种风险,例如滑坡、泥石流、岩石崩塌、沟壑的形成、土壤侵蚀和塌陷的形成,这些都增加了土地资源和植物演替的负面影响。另外研究发现,撂荒后自然恢复的植被难以在短期内阻止梯砍坍塌和冲沟的发育,因此需要进行对撂荒耕地进行管理,以降低水土流失风险。这项研究可以帮助土地规划师和环保主义者制定适当的指南（撂荒前或撂荒后）、计划和立法,以有效解决耕地撂荒的问题（第6章）。第四,研究评估了尼泊尔Dordi河流域的生态环境风险,并提出了基于风险的土地利用规划框架,以减轻风险的影响并加强可持续管理。我们采用层次分析法（AHP）,并在地理信息系统中进行了空间叠加分析,以生成生态环境风险图。Dordi河利用评价结果显示,流域面积22.36%属于高风险水平。极高、极高、中度和低度区域分别占17.38%、7.93%、28.49%和23.81%。高水平的生态环境风险主要发生在流域北部和西北部,而中等风险水平则集中在流域的南部。该风险图经实地检验,具有较高的可靠性。该风险图和基于风险的土地利用规划框架可以为制定合理的发展战略和指导方针提供信息和科学依据。同时,作为一种提高意识的工具,它还可以激活社会流程,使社区能够设计和减轻危险事件的后果（第7章）。最后,本研究为尼泊尔面临的撂荒耕地问题和发展提供了一些建议（第8章）。在尼泊尔的山区,可以采用低成本的做法利用现有的撂荒耕地,如种植经济作物或草药。尽管存在一些挑战,如（i）技术-农场所有权,规模,分布以及难以获得的其他来源（农业投入品、市场和运输）,（ii）环境（严重的水土流失、滑坡和泥石流）,（iii）社会经济挑战（贫困、劳动力短缺、文化和文化障碍）等。但研究提出的一些应对政策和策略建议,如,体制安排、一体化和主流化、资金以及在山区实施能力建设等,以及建立基于环境风险预防和减少的主动撂荒管理系统等。这些针对具体问题和挑战方面的建议,有助于促进政府和社区对山区土地的可持续管理和利用。
连宝琴[10]（2020）在《泾阳南塬渐退式黄土滑坡形成机理研究》文中提出泾阳南塬地区由于灌溉等原因诱发的黄土滑坡具有频发、群发、灾难性等特点,塬边黄土滑坡时有发生,塬面不断退化,具备渐退式滑坡特征,造成的地质灾害和土地资源损失十分严重,严重制约着地区社会经济的可持续发展。对此类滑坡进行深入系统地研究可为类似地区内滑坡预测与治理提供了试验依据,也为黄土塬区滑坡灾害防治提供了理论借鉴,具有重要的工程意义和科学价值。本文选题依托国家973项目“黄土重大灾害及灾害链的发生、演化机制与防控理论”,基于黄土滑坡问题的研究需求与现状,以中国陕西泾河南塬渐退式滑坡为主要研究对象,以揭示此类黄土滑坡的形成机理为研究主旨。首先通过开展滑坡野外调研揭示了研究区渐退式滑坡的发育特征,并总结了影响此类滑坡形成的重要内外因素。通过控制初始含水量、法向应力、剪切速率变化的环剪试验,总结了三种影响因素对黄土剪切行为的影响规律。针对研究区黄土样品,开展了一系列的常规三轴及预制裂隙三轴试验,阐明节理裂隙对泾阳南塬边坡地质结构和滑坡灾害形成和演化过程中的作用。最后结合室外调研成果和室内试验数据从数值模拟角度探讨各因素下泾阳南塬渐退式黄土滑坡的稳定性,最后提出泾阳南塬渐退式黄土滑坡形成机理。为后续渐退式黄土滑坡灾变的研究提供了有效的思路。本文的主要研究成果如下:（1）通过对研究区工程地质条件及地质环境背景的分析,发现塬边的黄土体多数临空,形态近直立,典型的河流阶地地貌,为这个区域滑坡的启动和运动提供了良好的条件。研究区黄土滑坡具有明显的渐退式特征,这与研究区长期灌溉水补给,导致研究区地下水位上升,坡体含水量增大,土体抗剪强度降低,有直接关系。（2）对研究区典型黄土样品,进行了不同法向应力、不同剪切速率和不同含水率下的环剪试验,发现:随着法向应力的增大,试样峰值剪切强度和残余剪切强度均增大,残余摩擦系数随法向应力的增大而减小。剪切速率越大,峰值剪切强度、残余剪切强度和残余摩擦系数减小,速率越大剪切强度-位移曲线显示的波动越剧烈。含水率的增加对黄土剪切强度的弱化效应明显。三种因素对黄土样品的峰值剪切强度,残余剪切强度和残余摩擦系数的影响规律,被进行了定量化的总结归纳。此外,发现在较高的剪切速率下,黄土样品剪切强度的减少可能是由于剪切带大颗粒土破碎产生细颗粒,水和细颗粒在剪面处的积聚产生的液化现象所导致的。环剪试验可以很好的解释研究区滑坡的高流动性的现象。（3）对研究区黄土,进行了不同预制裂隙角度下的三轴试验发现:含裂隙黄土试样在低围压下表现为应变软化,而在高围压下表现应变硬化,表明裂隙黄土试样在低围压下更容易发生破坏。此外,随着裂隙倾角的增大而不断变小,黄土试样强度变小,且当裂隙倾角接近于原状黄土的破坏角45°+?/2（本文中约为60°）时,黄土试样强度达到最小值。当黄土的含水率处于某一临界含水率以下,粘聚力随着含水率的增大而减少,而内摩擦角变化不大,这意味着相比较于内摩擦角,粘聚力受含水率的影响更大;然而当黄土的含水率超过临界含水率后,内摩擦角急剧减少,粘聚力的变化却不大。（4）运用Geostudio数值模拟软件,探讨了研究区典型斜坡在各种因素下（不同地下水位高度、不同裂隙深度（及距塬边距离）、不同灌溉时间）的渗流场和稳定性变化规律,发现斜坡的稳定性系数随着地下水位的上升而逐渐减小;随着裂缝深度的增大或裂隙塬边距离的减小,优势入渗现象变的格外明显;随着灌溉时间增大,斜坡内部饱和区域面积逐渐增大,斜坡同一部位孔隙水压力也逐渐增大,使得坡体内部土体强度的降低效果也愈发明显,从而导致斜坡的稳定性系数呈现递减的趋势。（5）基于野外现场调研、室内试验和数值模拟分析结果,认为泾阳南塬渐退式黄土滑坡的发生与地层岩性、边坡形态、灌溉和降雨等多种因素相关。其中,由于长期的灌溉作用产生的孔隙水压力的变化是导致这一现象的主要原因。具体来说研究区黄土滑坡受灌溉作用影响,斜坡坡脚土体遇水湿陷,孔压逐渐上升导致有效应力降低,从而斜坡稳定性持续降低,当前期依附在坡脚的滑体不足以提供有效的支撑时,斜坡就会再次破坏发生滑动,并进入一个新的发育周期,如此循环,滑坡后缘不断倒退,塬边土体逐渐后退式滑落,使得塬面持续性后退萎缩。同时基于野外现场调研数据和分析结果,把渐退式黄土滑坡的动态演化过程分为:蠕动拉裂阶段、局部液化阶段和滑动阶段。总体来说,塬边裂隙的发展促进了滑坡的发生,而伴随着滑动对坡顶裂隙产生的拉应力以及泾阳南塬边坡自身的高陡后壁又反过来促进了裂隙的发展。如此循环往复,滑坡不断发展演化形成泾阳南塬特有的渐退式黄土滑坡。
二、Analysis of Seismic Risk at an Engineering Site from Site Effect Seismic Intensity Data Using the Seismotectonic Method——Taking Six Reservoir Dam Sites in Western Anhui as an Example（论文开题报告）
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三、Analysis of Seismic Risk at an Engineering Site from Site Effect Seismic Intensity Data Using the Seismotectonic Method——Taking Six Reservoir Dam Sites in Western Anhui as an Example（论文提纲范文）
（1）Assessment of seismic hazard of roller compacted concrete dam site in Gilgit-Baltistan of northern Pakistan（论文提纲范文）
| 1 Introduction |
| 2 Tectonic settings |
| 3 Seismicity of the area |
| 4 Seismic hazard analysis |
| 4.1 Deterministic seismic hazard analysis (DSHA) |
| 4.1.1 Maximum potential earthquake (MPE) |
| 4.1.2 Peak ground acceleration (PGA) of the dam site |
| 4.2 Probabilistic seismic hazard analysis (PSHA) |
| 4.2.1 Seismic source zones |
| 4.2.2 Frequency-magnitude relationship for each seismic source zone |
| 4.2.3 Specification of ground motion model or PGA |
| 5 Conclusions |
（3）长江第一湾大同沟、台村沟的泥石流敏感性评估和冲出范围预测（论文提纲范文）
| 摘要 |
| abstract |
| Chapter 1:Introduction |
| 1.1 Background purpose and meaning of the topic: |
| 1.2 Research status of debris flow: |
| 1.2.1 The current situation of research on debris flow |
| 1.2.2 Research status of debris flow susceptibility assessment: |
| 1.2.3 Research status of Debris flow Runout Prediction: |
| 1.3 Research Contents and Technical Routes |
| Chapter2:Overview of the Geological Environment of the Study Area |
| 2.1 Research area location |
| 2.2 Geographical location |
| 2.3 Meteorological and Hydrological condition |
| 2.3.1 Inner diameter flow |
| 2.3.2 Regional hydrological station |
| 2.3.3 Heavy rains in the area |
| 2.3.4 Regional Meteorology |
| 2.4 Groundwater |
| 2.5 The regional geological formation,tectonics,and earthquake |
| 2.5.1 Profile of the regional geological structures |
| 2.5.2 Stratigraphy and Lithology |
| 2.5.3 Seismic conditions of the study area |
| 2.6 Social environment |
| Chapter 3:Geological Characteristics of Datong and Taicun Gully Debris Flow |
| 3.1 Datong and Taicun gully geographical location |
| 3.2 Datong and Taicun gully overall features |
| 3.3 Geological characteristics of Datong and Taicun gully |
| 3.3.1 Formation area of Debris flow gullies |
| 3.3.2 Circulation area of the debris flow gullies |
| 3.3.3 Accumulation area of debris flow gullies |
| 3.4 Source statistics of debris flow gullies |
| 3.4.1 Source statistics of Datong gully |
| 3.4.2 Source statistics of Taicun gully |
| 3.5 On-site sieve analysis of debris flow material in the accumulated area |
| 3.5.1 Site screening equipment |
| 3.5.2 On-site sampling principle |
| 3.5.3 The analysis method of the particle size distribution of debris flow deposits |
| 3.5.4 Particle size characteristics of early debris flow accumulation in Datong gully |
| 3.6 Material sampling for Optical Luminescence Dating test |
| Chapter4:Debris Flow Susceptibility Assessment |
| 4.1 Field investigation |
| 4.2 Software |
| 4.3 Data used |
| 4.4 Model selection |
| 4.4.1 Analytical hierarchy process |
| 4.4.2 Extension Method |
| 4.5 Assessment Factors |
| 4.6 Modelling Approach |
| 4.7 Assigning weight |
| 4.8 Susceptibility assessment based on Extension method |
| 4.9 Discussion |
| Chapter5:Runout Prediction of Debris Flow |
| 5.1 Runout Prediction of Debris Flow |
| 5.2 Basic Theory of SFLOW |
| 5.3 Control equation: |
| 5.4 Model validation |
| 5.5 Datong and Taicun gully debris flow Runout prediction: |
| 5.5.1 Data pre-processing in SFLOW: |
| 5.5.2 Simulation Results: |
| 5.6 Discussions |
| Chapter6:Conclusion and recommendations |
| References |
| 作者简介及在学期间所取得的科研成果 |
| 致谢 |
| DEDICATIONS |
（4）降雨入渗触发非饱和煤矸石堆积边坡失稳的机理研究（论文提纲范文）
| 摘要 |
| Abstract |
| LIST OF ABBREVIATIONS |
| CHAPTER 1 Introduction |
| 1.1 Concept of slope failure |
| 1.2 Mechanism of slope instability and types of failures |
| 1.3 Factors affecting slope failure |
| 1.4 Background of study |
| 1.5 Problem statement and content of research |
| 1.6 Research route model |
| CHAPTER 2 Concepts and methods in soil slope failure analysis |
| 2.1 Unsaturated soil mechanics |
| 2.2 Stability of Slope |
| 2.3 Permeability |
| 2.3.1 Applications of permeability in slope engineering |
| 2.3.2 Permeability usage |
| 2.4 Hydraulic conductivity (K) |
| 2.4.1 Darcy’s Law |
| 2.4.2 Permeability test (Constant-Head) |
| 2.4.3 Permeability test (Falling-Head) |
| 2.5 LEM for analyzing slope stability |
| 2.6 Finite element method (FEM) |
| 2.7 Slope reinforcement techniques |
| CHAPTER 3 Engineering Geological conditions of coal gangue accumulated landslide |
| 3.1 Geography |
| 3.2 Hydrometeorological conditions |
| 3.3 Geology |
| 3.3.1 Stratigraphic distribution |
| 3.4 Characteristics of Landslide |
| CHAPTER 4 Numerical simulations of landslide by considering rainfall infiltration as atriggering factor |
| 4.1 Methodology |
| 4.1.1 Testing of soil properties |
| 4.1.2 Numerical simulations by finite element method (FEM) |
| 4.1.3 Governing equations |
| 4.2 Results |
| 4.2.1 Pore-water pressure |
| 4.2.2 Strain generation |
| 4.2.3 Response of deformation |
| 4.2.4 Safety factor |
| 4.3 Discussion |
| 4.3.1 Influence of rainfall infiltration in the change of pore-water pressure |
| 4.3.2 Mechanism in the distribution of strain |
| 4.3.3 Effect of rainfall infiltration process on deformation response |
| 4.3.4 Influence of rainfall infiltration process on safety factor |
| CHAPTER 5 Control scheme and stabilization of landslide |
| 5.1 Introduction |
| 5.2 Control scheme for Landslide |
| 5.2.1 Horizontal drainage in landslide |
| 5.2.2 Main components of drainage design |
| 5.3 Stabilization of Landslide |
| 5.3.1 Ground Anchorages |
| 5.3.2 Piles |
| 5.3.3 Anchorages and beam coupled with piles stabilization |
| CHAPTER 6 Conclusions and Recommendations |
| 6.1 Conclusions |
| 6.2 Recommendations |
| LITERATURE CITED |
| Self Introduction and Scientific Research Achievements During Master Degree |
| ACKNOWLEDGEMENTS |
（5）三峡库区滑坡多场信息监测与组合式预测模型优化研究（论文提纲范文）
| 作者简历 |
| 摘要 |
| Chapter 1 Introduction |
| 1.1 Research Background and Significance |
| 1.2 Literature Review |
| 1.2.1 Latest Achievements on Multi-field Monitoring Methods |
| 1.2.2 Existing Forecasting Models |
| 1.2.3 Optimization of SVR-based Ensemble Prediction Model |
| 1.3 Shortcoming and Prospects in Research |
| 1.4 The Scope of Research |
| 1.4.1 Research Content |
| 1.4.2 Innovation Points |
| 1.4.3 Research Flowchart |
| Chapter 2 Slow-moving Landslide in the Three Gorges Reservoir area |
| 2.1 Slow-moving Landslides |
| 2.2 Baishuihe Landslide |
| 2.2.1 Geological Conditions |
| 2.2.2 Surface Displacements |
| 2.2.3 Shear Zone Displacements |
| 2.3 Shuping Landslide |
| 2.3.1 Geological Conditions |
| 2.3.2 Monitoring Data and Deformation Characteristics of the Landslide |
| 2.3.3 Influence of Inducing Factors |
| 2.4 Shiliushubao Landslide |
| 2.4.1 Geological Conditions |
| 2.4.2 Rainfall and Reservoir Levels |
| 2.4.3 Deformation Characteristics |
| 2.4.4 Landslide Monitoring |
| 2.4.5 Analysis of Monitoring Data |
| 2.5 Outang Landslide |
| 2.5.1 Geological Conditions |
| 2.5.2 Deformation Characteristics |
| 2.5.3 Analysis of Monitoring Data |
| 2.6 Chapter Summary |
| Chapter 3 Development of Multi-field Monitoring Devices for Reservoir Landslide |
| 3.1 Device for Deformation Monitoring inside the Sliding Mass |
| 3.1.1 Background of the Study |
| 3.1.2 A Detailed Description of the Device |
| 3.1.3 Advantages of Proposed Devices |
| 3.2 Devices for Multi-field Monitoring in Sliding Mass |
| 3.2.1 Background of the Invention |
| 3.2.2 A Detailed Description of the Device |
| 3.2.3 Advantages of Devices Designed |
| 3.3 Device for Borehole Reinforcement |
| 3.3.1 Background of the Study |
| 3.3.2 A Detailed Description of the Device |
| 3.3.3 Advantages of Devices Designed |
| 3.4 Chapter Summary |
| Chapter 4 A Novel Hybrid Prediction Model based on CEEMD-DTW and ACO-SVR |
| 4.1 Proposed Methodology |
| 4.1.1 Time Series Theory of Landslide Displacement |
| 4.1.2 EMD Series Model |
| 4.1.3 Support Vector Regression |
| 4.1.4 Identify Methods for Optimal Input Parameter Selection |
| 4.1.5 Ant Colony Optimization |
| 4.1.6 The Framework of the Proposed Hybrid Algorithm |
| 4.2 Data Preprocessing |
| 4.2.1 Displacement Decomposition by EEMD |
| 4.2.2 CEEMD Decomposition of Inducing Factors |
| 4.2.3 DTW Analysis of Inducing Factors |
| 4.3 Predict and Comparative Analysis |
| 4.4 Chapter Summary |
| Chapter 5 Forecasting Model with CEEMD-LCSS Reconstruction and ABC-SVRMethod |
| 5.1 Proposed Methodology |
| 5.1.1 Identification of Dominant Inducing Factors with the LCSS |
| 5.1.2 Optimization Algorithm |
| 5.1.3 Schematic Diagram of the Proposed Method |
| 5.2 Data Preprocessing |
| 5.2.1 Decomposition of the Landslide Displacement |
| 5.2.2 Reconstructed of Inducing Factor Analysis |
| 5.2.3 CEEMD Reconstruction of the Inducing Factors |
| 5.2.4 Identification of Dominant Inducing Factors by the LCSS |
| 5.3 Prediction and Results |
| 5.3.1 Predicted Trend Component of the Landslide Displacements |
| 5.3.2 Predicted Periodic Component of the Landslide Displacements |
| 5.3.3 Predicted Cumulative Landslide Displacement |
| 5.4 Chapter Summary |
| Chapter 6 An Ensemble Prediction Model based on EDR Selection and MultipleSwarm Intelligence |
| 6.1 Proposed Methodology |
| 6.1.1 Selection of Optimal Related Factors via EDR |
| 6.1.2 Multiple Swarm Intelligence |
| 6.1.3 The Framework of the Proposed Novel Forecasting Model |
| 6.2 Data Pre Processing and Statistical Analysis |
| 6.2.1 CEEMD Decomposition of Landslide Displacement Vs Time Data |
| 6.2.2 CEEMD Decomposition of Related Factors |
| 6.2.3 Factors Affecting Landslide Displacement Selected by EDR |
| 6.3 Prediction Results |
| 6.3.1 Parameter Optimization |
| 6.3.2 Prediction of Periodic and Trend Displacements |
| 6.3.3 Prediction of the Cumulative Displacement by the Proposed Framework |
| 6.4 Chapter Summary |
| Chapter 7 Comparative Study of the Proposed Ensemble Prediction Model |
| 7.1 Design of the Comparative Study |
| 7.2 Results and Analysis |
| 7.2.1 Analysis of Displacement Decomposition |
| 7.2.2 Analysis of Inducing Factors Decomposition |
| 7.2.3 Analysis of Input Variable Selection |
| 7.2.4 Analysis of Optimization Algorithm |
| 7.3 Chapter Summary |
| Chapter 8 Conclusion and Discussion |
| 8.1 Conclusion |
| 8.2 Discussion |
| Acknowledgements/致谢 |
| References |
（6）Growing seismicity in the Sichuan Basin and its association with industrial activities（论文提纲范文）
| 1. Introduction |
| 2. Geological setting and fault stability analysis |
| 2.1 Geological setting |
| 2.2 Fault stability analysis |
| 3. Major earthquakes within Sichuan Basin |
| 3.1 Major earthquakes of MS≥5 |
| 3.2 Natural origin earthquakes |
| 3.2.1 MS5.1 Suining-Tongnan earthquake |
| 3.2.2 MS5.5 Jitian earthquake and MS5.1 Qingbai-Jiang earthquake |
| 3.2.3 Tongjing MS5.2 and MS5.4 earthquake sequence |
| 3.2.4 M5 Wulong earthquake on 23 Nov.2017 |
| 3.2.5 MW4.1 Dianjiang earthquake on 11 Aug.2016 |
| 3.2.6 Seismicity along south segment of Fangdoushan fault |
| 3.3 Earthquakes associated with long-term injection for salt mining |
| 3.3.1 Ziliujing M4.6–M5.0 earthquakes |
| 3.3.2 Seismicity in Shuanghe salt mine area |
| 3.3.3 Earthquake sequence in Luocheng and Changshan salt mine area |
| 3.4 Earthquakes associated with long-term injection for disposal of wastewater |
| 3.4.1 Kongtan M5.4 earthquake sequence |
| 3.4.2 Earthquakes in boundary area of Jiang-an,Nanxi,Yibin,and Changning counties |
| 3.4.3 Induced earthquakes in Rongchang gas field |
| 3.4.4 Induced earthquakes in Huangjiachang gas field,Zigong |
| 3.5 Earthquakes associated with short-term injection for shale gas—Shangluo site |
| 4. Seismicity in Weiyuan-Rongxian shale gas demonstration block |
| 5. Lessons and opportunities from the Sichuan Basin |
| 5.1 Injection-induced seismicity in south Sichuan Basin |
| 5.2 Role of overpressured fluid in natural and induced earthquakes |
| 5.3 Impact of 2008 Wenchuan earthquake on the Si-chuan Basin |
| 5.4 Features of injection-induced seismicity |
| 5.5 Conditions of high-level injection-induced seismi-city in Sichuan Basin |
| 5.6 Are injection-induced earthquakes as large as ex-pected? |
| 6. Insights and challenges |
| 6.1 Detecting early signs of fault reactivation |
| 6.2 General framework for assessment and manage-ment of earthquake-related risks |
（7）喜马拉雅中部阿润河谷高山环境下的冰湖溃决洪水研究（论文提纲范文）
| 摘要 |
| ABSTRACT |
| Chapter1 Introduction |
| 1.1 Background and Significance |
| 1.2 Study Area |
| 1.2.1 Justification of Site Selection |
| 1.2.2 Justification and Aims of the Work in Arun |
| 1.3 Research Gap and Problem Statement |
| 1.4 Novelty |
| 1.5 Scope and Objectives |
| 1.5.1 Main Objectives |
| 1.5.2 Sub Objectives |
| 1.6 Research Outline |
| Chapter2 Literature Review |
| 2.1 GLOFs in the Context of High Mountain Environments |
| 2.2 Previous research and existing methods for assessing the susceptibility of glacial lakes to outburst floods |
| 2.2.1 The Qualitative Approach |
| 2.2.2 The Semi-Quantitative Approach |
| 2.2.3 The Quantitative Approach |
| 2.3 Flood Risk Management |
| 2.4 Previous studies related to UAHEP |
| 2.4.1 General |
| 2.4.2 Feasibility Study Phase I in1987 |
| 2.4.3 Feasibility Study(1991) |
| 2.4.4 Feasibility Review Study(2011) |
| 2.5 Drawbacks and Limitations of Existing Methods and Database |
| Chapter3 Materials and Research Methodology |
| 3.1 Introduction |
| 3.2 Data Collection |
| 3.2.1 Hydrological and Meteorological Data |
| 3.2.2 Weather Data |
| 3.2.3 Remote Sensing Data |
| 3.3 Field Survey |
| 3.4 Estimation of Probable Maximum Flood |
| 3.4.1 Selection of Model |
| 3.4.2 Model Simulation |
| 3.5 Establishment of Glacier and Glacial Lake Databases |
| 3.6 The New Model for GLOF Risk Assessment |
| 3.7 GLOF Hydrological Evolution |
| 3.7.1 Approaches followed for GLOF Modeling |
| Chapter4 Data Information System for Arun Valley |
| 4.1 Regional Overview |
| 4.1.1 Location& Access |
| 4.2 Geological and Geomorphological Study |
| 4.2.1 Regional Geology |
| 4.2.2 Geology of the Reservoir |
| 4.2.3 Geological Risk |
| 4.2.4 Land Cover |
| 4.2.5 Slope Characteristics and Steepness Factor |
| 4.2.6 Soil Erosion Processes |
| 4.3 Hydrological Study related to GLOFs |
| 4.3.1 River Systems |
| 4.3.2 Drainage Characteristics |
| 4.3.3 Results of Runoff Study |
| 4.3.4 Results of Flood Study |
| 4.4 Climatic Correlation with GLOFs |
| 4.4.1 Introduction |
| 4.4.2 Location Specific Details |
| 4.4.3 Climatic Correlation with GLOFs |
| 4.5 Natural Hazards in Arun Basin |
| 4.5.1 Landslides in Arun River Basin |
| 4.5.2 Fires Incidents in Arun Basin |
| 4.5.3 Earthquakes in Arun Basin |
| 4.5.4 GLOFs in the Arun River Basin |
| 4.6 The Distribution and Quantification of Glaciers and Glacial Lakes in Arun Valley |
| Chapter5 The New Model for GLOF Risk Assessment |
| 5.1 The New Model |
| 5.1.1 Justification for the Assumed Depth of the Lakes |
| 5.2 Determination of the Critical Lakes using the New Model |
| 5.2.1 Multi-criterion outburst probability and failure mechanisms of the glacial lakes |
| 5.2.2 Preliminary Discharge Profiles and Outburst Probability of the Critical Lakes |
| 5.3 GLOF Modeling and Simulation of GLOFs in the Upstream of Arun River |
| 5.3.1 The Two Critical Lakes |
| 5.3.2 GLOF Modelling |
| 5.4 Suggested Mitigation for the potential GLOF from the two critical lakes |
| Chapter6 Conclusions and Recommendations |
| 6.1 Summary,Comparison and Contribution of the novel method |
| 6.1.1 Comparison with the State of the Art |
| 6.2 Conclusion of the Data Information System |
| 6.3 Discussion and Limitations in Relation to the Novel Model |
| 6.4 Recommendations and Way Forward |
| References |
| Appendix1 中文简本 |
| Appendix2 List of Figures |
| Appendix3 List of Tables |
| Acknowledgements |
| CV of author and Research Contribution |
（8）Seismic Soil Liquefaction Hazard Assessment Using Bayesian Belief Networks（论文提纲范文）
| Abstract |
| 摘要 |
| Table of Major Symbols and Units |
| 1 Introduction |
| 1.1 Soil Liquefaction |
| 1.1.1 Approaches for Evaluation of Seismic Soil Liquefaction Potential |
| 1.2 Soil Liquefaction-Induced Lateral Displacement |
| 1.2.1 Approaches for Estimating Liquefaction-Induced Lateral Displacement |
| 1.3 Motivation for Research and Problem Statement |
| 1.4 Research Aims and Scope |
| 1.5 Organization of the Dissertation |
| 2 Bayesian Belief Networks-As An Analysis Tool |
| 2.1 Fundamentals of Bayesian Belief Networks |
| 2.1.1 Structure Learning |
| 2.1.2 Parameter Learning |
| 2.2 Applications in Civil Engineering |
| 2.3 Advantages and Challenges Using Bayesian Belief Networks |
| 2.4 Software to Deal with Graphical Models |
| 2.5 Summary |
| 3 Interpretive Structural Modeling and MICMAC Analysis for Identifying and Benchmarking Significant Factors:An Application in the CPT-based Seismic Soil Liquefaction |
| 3.1 Introduction |
| 3.2 Methodology |
| 3.2.1 Interpretive Structural Modeling-A Qualitative Technique |
| 3.2.2 MICMAC Analysis |
| 3.3 Application to the Case of Illustration |
| 3.3.1 Interpretive Structural Model of Seismic Soil Liquefaction Significant Factors |
| 3.3.2 MICMAC Analysis-Classification of CPT-based Seismic Soil Liquefaction Significant Factors |
| 3.4 Results and Discussion |
| 3.5 Summary |
| 4 Probabilistic Graphical Models For Evaluation of Liquefaction Potential |
| 4.1 Introduction |
| 4.2 Evaluation Measures of Model's Performance |
| 4.3 Probabilistic Graphical Models Using Five Factors of Seismic Soil Liquefaction |
| 4.3.1 Database,Predictor Variables and Preprocessing |
| 4.3.2 Development of BBN-based Models Using Five Factors of Seismic Soil Liquefaction |
| 4.3.3 Results and Discussion |
| 4.4 Probabilistic Graphical Model Using Multi-factor of Seismic Soil Liquefaction |
| 4.4.1 Database, Predictor Variables and Preprocessing |
| 4.4.2 Development of BBN-based Model Using Multi-factor of Seismic Soil Liquefaction |
| 4.4.3 Results |
| 4.4.4 Discussion |
| 4.5 Summary |
| 5 Evaluation of Liquefaction-Induced Lateral Displacement Using Bayesian Belief Networks |
| 5.1 Introduction |
| 5.2 Evaluation Measures |
| 5.3 Development of Liquefaction-Induced Lateral Displacement Modeling |
| 5.3.1 Database,Predictor Variables,and Preprocessing |
| 5.3.2 Model Development Using Bayesian Belief Network Based on Interpretive Structural Modeling Technique |
| 5.4 Results and Discussion |
| 5.4.1 Training Performance of the BBN-based Lateral Displacement Models |
| 5.4.2 Predictive Performance Comparison with Most Frequently Used MLR and GP Models of Lateral Displacement |
| 5.4.3 Sensitivity Analysis |
| 5.5 Summary |
| 6 Conclusions and Future Horizons |
| 6.1 Conclusions |
| 6.2 Future Horizons |
| Abstract of Innovation Points |
| References |
| Appendix A Summary of CPT-based Liquefaction Case Histories |
| Appendix B Summary of Liquefaction-Induced Lateral Ground Deformation Database |
| Research Projects and Publications during PhD Period |
| Acknowledgement |
| Curriculum Vitae |
（9）尼泊尔山区耕地撂荒的社会与生态环境风险研究（论文提纲范文）
| 摘要 |
| abstract |
| Chapter 1 Introduction |
| 1.1 Background and significance |
| 1.1.1 Mountain eco-environment- global perspectives |
| 1.1.2 Statement of problems |
| 1.2 Singnificance of the study |
| 1.3 Objectives of the study |
| 1.4 Research contents and organisation of the study |
| Chapter 2 Review on farmland abondonment and its driving factors |
| 2.1 Global studies on farmland abondonment |
| 2.2 Global persepective on driving factors of farmland abondonment |
| Chapter 3 Research methodology;materials and methods |
| 3.1 Introduction |
| 3.2 Key terms-definition |
| 3.2.1 Farmland abandonment |
| 3.2.2 Hazard |
| 3.2.3 Vulnerability |
| 3.2.4 Exposure |
| 3.2.5 Risk |
| 3.2.6 Social and eco-environmental risk |
| 3.2.7 Eco-environmental risk assessment(ERA) |
| 3.3 Conceptualizing and theorizing farmland abandonment landscape and assessment of social/eco-environmental risks |
| 3.4 Study framework |
| 3.5 Research activities |
| 3.5.1 Pre field |
| 3.5.2 Field activities |
| 3.5.3 Post field activities |
| 3.6 Conclusions |
| Chapter 4 Charecteristics of the case study area |
| 4.1 Introduction |
| 4.2 Overview of Nepal |
| 4.2.1 Mountain context and social vulnerability in Nepal |
| 4.2.2 Mountainous farmland and farming system in Nepal |
| 4.3 Dordi river basin |
| 4.3.1 Administrative and bio-physical setting |
| 4.3.2 Socio-economic attributes |
| 4.4 Status of farmland degradation in Nepal |
| 4.5 Conclusions |
| Chapter 5 Farmland abondonment driving factors and its socio-ecoenvironmental consequences in Nepal |
| 5.1 Introduction |
| 5.2 Materials and methods |
| 5.2.1 Sources of data |
| 5.2.2 Methods for data analysis |
| 5.3 Results |
| 5.3.1 The spatiotemporal distribution of abondoned farmland in Nepal |
| 5.3.2 Driving factors of farmland abandonment |
| 5.3.3 Eco-environmental and social consequences of farmland abondonment |
| 5.4 Discussions |
| 5.5 Conclusions |
| Chapter 6 Social risks of farmland abondonment |
| 6.1 Introduction |
| 6.2 Materials and methods |
| 6.2.1 Household data and sampling |
| 6.2.2 Focus group discussion(FGD)and key informants interview(KII) |
| 6.2.3 Selection of site specific driving factors and multivariate regression analysis |
| 6.2.4 Framing of social risk and analysis through hazards of place-model of vulnerability |
| 6.3 Results and discussion |
| 6.3.1 Determining site specific driving factors of farmland abandonment |
| 6.3.2 Risks on social system |
| 6.4 Conclusions |
| Chapter 7 Eco-environmental vulnerability and associated risks |
| 7.1 Introduction |
| 7.2 Materials and methods |
| 7.2.1 Data acquisition and processing |
| 7.2.2 Delineation and characterization of abandoned farmland |
| 7.2.3 Assessment of spatiotemporal degradation of farmland |
| 7.2.4 Selection of causes of farmland degradation |
| 7.2.5 Analysis of hazard/risk |
| 7.3 Results |
| 7.3.1 Assessment of abandoned farmland spatiotemporal degradation |
| 7.3.2 Identification of major causes of degradation and statistical analysis |
| 7.3.3 Eco-environmental vulnerability associated with abondoned farmlands |
| 7.3.4 Eco-environmental risks associated with the abandoned farmlands |
| 7.4 Discussions |
| 7.5 Conclusions |
| Chapter 8 Development of evluation index and risk assessment |
| 8.1 Introduction |
| 8.2 Materials and methods |
| 8.2.1 Data collection and processing |
| 8.2.2 Selection of criteria and construction of assessment indicator system |
| 8.2.4 Determine relative importance of different criteria |
| 8.2.5 Risk calculation and classification of results |
| 8.2.6 Development of a framework for landuse planning |
| 8.3 Result and discussions |
| 8.3.1 Spatial Distribution of Eco-environmental Risk |
| 8.3.2 Landuse planning framework |
| 8.4 Conclusions |
| Chapter 9 Summary, conclusion,recommendations, challenges and policy messages |
| 9.1 Summary and conclusion |
| 9.2 Recommendations |
| 9.2.1 Introduction of abandoned farmland for low cost practices and eco-friendly farming |
| 9.2.2 Adoptation for cash crops or as sources of medicinal herbs |
| 9.3 Challenges |
| 9.3.1 Technical challenges |
| 9.3.2 Environmental challenges |
| 9.3.3 Socio-economic challenges |
| 9.4 Policy messages |
| 9.4.1 Institutional arrangement,integration and mainstreaming |
| 9.4.2 Finance |
| 9.4.3 Capacity building |
| References |
| Appendix 1 中文简本 |
| Appendix 2 Survey Questionaire Form |
| Appendix 3–PUBLISHED PAPER/CONTRIBUTION |
| Acknowledgements |
| CV of author and Research Contribution |
（10）泾阳南塬渐退式黄土滑坡形成机理研究（论文提纲范文）
| Abstract |
| 摘要 |
| Chapter 1 Introduction |
| 1.1 Rearch background and significance |
| 1.2 Research background |
| 1.2.1 The loess landslides in South Jingyang Plateau, Shaanxi |
| 1.2.2 Study on the retrogressive landslides |
| 1.2.3 Review on characteristics of slip zone soils |
| 1.2.4 The external influencing factors affecting loess landslide |
| 1.2.5 Study on the joints and fissures of loess |
| 1.2.6 Approaches for interpreting failure mechanism of loess landslides |
| 1.3 Research objectives and content |
| 1.3.1 Reserch objective |
| 1.3.2 Reserch content and technical rout |
| Chapter 2 Geological setting of study area and the characteristics of the gradualretrogressive loess landslide |
| 2.1 Study area |
| 2.2 Geological condition |
| 2.2.1 Meteorology and hydrology |
| 2.2.2 Topography and geomorphology |
| 2.2.3 Strata lithology |
| 2.2.4 Hydrogeology |
| 2.2.5 Neotectonic movement and earthquakes |
| 2.2.6 Human activities |
| 2.3 Environmental geological problems and geological hazards |
| 2.4 Analysis of the characteristics of retrogressive landslides in the study area |
| 2.5 Summary |
| Chapter 3 Experimental study on the shear characteristics of loess in South Jingyangplatform |
| 3.1 Introduction |
| 3.2 Summary of experimental methods for determining residual strength of soils |
| 3.2.1 Reversal direct shear tests |
| 3.2.2 Triaxial shear test |
| 3.2.3 Ring shear test |
| 3.3 Physical indexes of loess |
| 3.3.1 Grain size distribution of loess |
| 3.3.2 Physical parameters of loess |
| 3.4 Ring shear test on loess |
| 3.4.1 Testing apparatus |
| 3.4.2 Testing procedures |
| 3.4.3 Results |
| 3.5 Discussion |
| 3.5.1 Effect of influence factors on the shear behavior of loess |
| 3.5.2 The brittleness index |
| 3.5.3 Surface morphology of the shear surface |
| 3.5.4 On the mechanism of landslides in the study area based on ring shear tests |
| 3.6 Summary |
| Chapter 4 Experimental study on the fissured loess by triaxial shear test in SouthJingyang platform |
| 4.1 Introduction |
| 4.2 The effect of loess joints development on the stability of loess slope |
| 4.2.1 The type of loess joints |
| 4.2.2 The effect of cracks on loess slopes |
| 4.3 Investigation on the joint cracks in study area |
| 4.4 Triaxial shear test on intact loess |
| 4.4.1 Testing apparatus |
| 4.4.2 Testing procedure |
| 4.4.3 Experimental results |
| 4.5 Triaxial shear test on fissured loess |
| 4.5.1 The preparation of fissured loess specimen |
| 4.5.2 Testing procedure |
| 4.5.3 Test results for fissured loess |
| 4.6 Summary |
| Chapter 5 Numerical simulation on the stability of gradual retrogressive loess slopes |
| 5.1 Introduction |
| 5.2 Geostudio software |
| 5.2.1 SEEP/W module |
| 5.2.2 SLOPE/W module |
| 5.2.3 SIGMA/W module |
| 5.3 Numerical modeling |
| 5.3.1 Geometry of the numerical model |
| 5.3.2 Numerical simulation cases |
| 5.3.3 Boundary condition and initial condition of the numerical model |
| 5.3.4 Model calculation parameters |
| 5.4 Numerical simulation results |
| 5.4.1 The effect of groundwater depth on the seepage and stability of the slope |
| 5.4.2 The effect of crack depth on the seepage and stability of the slope |
| 5.4.3 The effect of the distance of the crack from the edge on the seepage and stability of the slope |
| 5.4.4 The effect of the irrigation time on the seepage and stability of the slope |
| 5.5 Summary |
| Chapter 6 Formation mechanism of the gradual retrogressive loess landslides andprevention methods |
| 6.1 Introduction |
| 6.2 Influence factors of the gradual retrogressive loess landslides (GRLL) in Jingyangtableland |
| 6.3 Formation mechanism and evolution of GRLL in Jingyang tableland |
| 6.4 Landslides prevention and control methods |
| 6.4.1 Prevention and control principles of loess landslides |
| 6.4.2 Treatment measures for retrogressive loess landslides in the south Jingyang plateau |
| 6.4.3 Preventive measures for retrogressive loess landslides and loess slopes in the South Jingyang plateau |
| 6.5 Summary |
| Chapter 7 Conclusions and recommendations |
| 7.1 Summary and conclusions |
| 7.2 Recommendations for future research |
| References |
| Related publications |
| 致谢 |
| Acknowledgements |
四、Analysis of Seismic Risk at an Engineering Site from Site Effect Seismic Intensity Data Using the Seismotectonic Method——Taking Six Reservoir Dam Sites in Western Anhui as an Example（论文参考文献）
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