丹江口大坝加高对监测系统的影响及对策

丹江口大坝加高施工正在进行,原有监测系统有一部分被拆除或废除,而新的监测系统尚未建立,对监测工作造成一定的影响。针对此影响提出了解决对策,以保证施工期留用监测项目的正常监测,此外,对今后完善监测系统提出设想。     

澳大利亚奥林匹克坝矿床铀成矿地质特征

奥林匹克坝多金属铁氧化物角砾杂岩型(IOCG型)矿床是目前世界上已知最大的铀矿和世界前三大铜矿之一,探明铀资源储量2171359 t,品位为0.020%,目前正在开发中。铀矿化产于一个大型、复杂漏斗状热液角砾岩体—奥林匹克坝角砾杂岩(ODBC)中,后者受构造控制,形成于中元古代早期侵入的罗克斯比A型花岗岩中,形成环境类似于玛珥湖高位潜水岩浆火山机构。铀矿化与富含赤铁矿、氧化的热液蚀变组合空间相关,主要铀矿物为沥青铀矿,成矿年龄可能晚于1590~1580 Ma。伸展运动导致的陷落地块有利于保存在近地表形成的富赤铁矿-富铀IOCG型矿床;而周围上升区域将表现出更深的富磁铁矿-贫铀IOCG型矿床类型特点。     

Porosity and permeability variations of a dam curtain during dissolution

During reservoir operation,the erosion effects of groundwater change the porosity and permeability of the dam curtain,causing changes to the seepage field.To understand where the changes take place and to what degree the porosity and permeability change,a multi-field coupling model was built and solved.The model takes into account seepage,solution concentration,and solid structure.The model was validated using uplift pressure monitoring data.Then,the variations in curtain porosity,seepage flow,and loss quantity of Ca(OIH)2 were calculated.The key time nodes were obtained through curve fitting of the variation of seepage flow with the BiDoseResp function.The results showed that the model could reflect the attenuation trend of curtain performance well.The process and position of the erosion were not homogeneous.Although erosion mainly occurred at the top and bottom of the curtain,it was most developed at the top.The erosion effects developed slowly during the early stage,much fast during the middle and late stages,and culminated in complete dissolution.The model results and the daily monitoring data can provide a scientific basis for the safe operation and management of reservoirs.     

Flood management of Dongting Lake after operation of Three Gorges Dam

Full operation of the Three Gorges Dam (TGD) reduces flood risk of the middle and lower parts of the Yangtze River Basin. However, Dongting Lake, which is located in the Yangtze River Basin, is still at high risk for potentially severe flooding in the future. The effects of the TGD on flood processes were investigated using a hydrodynamic model. The 1998 and 2010 flood events before and after the operation of the TGD, respectively, were analyzed. The numerical results show that the operation of the TGD changes flood processes, including the timing and magnitude of flood peaks in Dongting Lake. The TGD can effectively reduce the flood level in Dongting Lake, which is mainly caused by the flood water from the upper reach of the Yangtze River. This is not the case, however, for floods mainly induced by flood water from four main rivers in the catchment. In view of this, a comprehensive strategy for flood management in Dongting Lake is required. Non-engineering measures, such as warning systems and combined operation of the TGD and other reservoirs in the catchment, as well as traditional engineering measures, should be further improved. Meanwhile, a sustainable philosophy for flood control, including natural flood management and lake restoration, is recommended to reduce the flood risk.     

Analysis of working behavior of Jinping-I Arch Dam during initial impoundment

To study the stress, deformation, and seepage pressure during the initial impoundment of the Jinping-I Arch Dam, monitoring analysis and numerical calculation were used in a dam behavior analysis that focused on the working behavior of the dam during the late period of the initial impoundment up to the end of November 2014. The numerical calculation was performed based on feedback analysis of the deformation and stress of the arch dam through inversion of the elastic moduli(E) of the dam body and foundation, using a three-dimensional finite element model for the linear elastic material of the arch dam. The main monitoring indices presented insignificant changes in the late period of the initial impoundment, and the results of feedback analysis were consistent with monitoring results. Analysis results also show that the deformations of the dam body and dam foundation were within the design range; the dam stress distributions were normal, with values lower than the design control criteria; and the seepage flows through the dam body and dam foundation were lower than the design drainage capacity of the deep-well pump house, demonstrating that the Jinping-I Arch Dam was in good working condition, and the initial impoundment had been successfully completed. The results of the working behavior analysis of the Jinping-I hydropower project during the initial impoundment can provide references for safe operation of similar projects.     

基于BIM的水工结构设计合规性检查方法研究

结构设计合规性检查是确保建筑信息模型（BIM）符合设计要求的有效手段,提高规范检查的自动化程度与准确度对于实现结构全寿命周期管理具有重要意义。基于自动化规则检查理论,采用可视化编程与C#编程结合开发的方式,提出了基于BIM水工结构设计合规性检查框架。基于Civil3D创建水工结构初级模型,并进行相应属性拓展。通过Dynamo对模型细化并提取模型信息以创建模型信息库。对相关规范进行解析,并依据标准对构件分类编码,储存在SQL Server数据库中以创建规则知识库。最后将规则知识库与模型信息库连接交互,从而实现水工BIM模型合规性检查。该方法极大地提高了基于BIM水工结构设计合规性检查效率和准确度,降低了开发难度,为促进水工结构设计合规性检查奠定了基础。     

引汉济渭工程三河口水利枢纽BIM设计应用

三河口水利枢纽由大坝、泄洪消能、坝后供水系统、连接洞等工程组成,大坝为碾压混凝土双曲拱坝,是国内排名第二的高碾压混凝土拱坝。大坝建造难度大,枢纽工程地质、地形条件复杂,厂房电气设备众多,专业协同配合多,工程计算工作量大。本文利用建筑信息模型(Building Information Modeling,BIM)技术进行在线协同设计,通过地质模型、水工模型、水机模型、电气设备模型、建筑结构模型和金属结构模型实现了多专业同平台的设计。基于BIM技术的三河口水利枢纽应用,实现了与业主间的高效沟通,优化了设计方案。通过三维设计成果解决了现场问题,方便快速统计项目进行过程中的工程量,将计算机辅助设计(Computer Aided Design,CAD)技术与计算机辅助工程(Computer Aided Engineering,CAE)仿真相结合,实现了建模和计算的分离,提高了设计效率。     

滩坑水电站混凝土面板堆石坝筑坝施工综述

滩坑面板坝采用了一些新的筑坝施工理念,采用抛石挤淤工法,实现基坑深覆盖层坝基开挖快速施工;汛期适当提高坝体填筑高程,在坝面高差处设置超径石保护,经多次过流坝面冲刷少;坝体填筑超高值,下游坝体反抬加高填筑,较好地控制了坝体变形。大坝安全监测结果表明,坝基沉降量小;施工期及蓄水后坝体沉降均匀,水平位移小,大坝施工质量良好。     

溪洛渡300m级高拱坝坝基的渗流稳定工程措施

扼要介绍了溪洛渡300m级高拱坝的水文地质条件,并结合钻孔注水试验、单洞渗水试验、地下水示踪-连通试验和钻孔压水试验,详述了坝基岩体透水性、坝区岩体渗流特性、两岸地下水位及其动态变化规律、玄武岩的构造特点及其对渗透性的影响、坝区裂隙岩体透水性与风化卸荷的关系、坝区玄武岩的透水性、坝区茅口组灰岩的水文地质特点及渗透特性。根据坝址区水文地质及其渗流特性,制定了溪洛渡高拱坝的渗流稳定的工程措施。简要介绍了采用"复杂三维渗流控制分析计算程序"分析的成果。最后与二滩工程渗流稳定工程措施进行了比较。     

Towards online monitoring of concrete dam displacement subject to time-varying environments: An improved sequential learning approach

Data-based models are widely applied in concrete dam health monitoring. However, most existing models are restricted to offline modeling, which cannot continuously track the displacement behavior with dynamic evolution patterns, especially in time-varying environments. In this paper, sequential learning is introduced to establish an online monitoring model for dam displacement behavior. This approach starts by considering the timeliness difference between old and new data using the forgetting mechanism, and a novel adaptive forgetting extreme learning machine (AF-ELM) is presented. A primary predictor based on AF-ELM is then formulated, which aims to sequentially learn the complex nonlinear relationship between dam displacement and main environmental factors. Considering the chaotic characteristics contained in the residual sequence of the primary predictor, a multi-scale residual-error correction (REC) strategy is devised based on divide-and-conquer scheme. Specifically, time-varying filter-based empirical mode decomposition is adopted to decompose the raw chaotic residual-error series into a set of subseries with more stationarity, which are further aggregated and reconstructed by fuzzy entropy theory and suitable approximation criterion. Finally, the corrected residual sequence is superimposed with the preliminary predictions from AF-ELM to generate the required modeling results. The effectiveness of the proposed model is verified and assessed by taking a real concrete dam as an example and comparing prediction performance with state-of-the-art models. The results show that AF-ELM performs better in displacement prediction compared with benchmark models, and the multi-scale REC can effectively identify the valuable information within the residual sequence. The proposed online monitoring model can more closely track the dynamic variations of displacement data, which provides a fire-new solution for dam behavior prediction and analysis.