卤水井测试工艺研究

卤水井测试的最大技术难题是井筒结晶堵塞问题。为解决这个问题,以四川邛崃平落坝构造高浓度卤水井测试为例,从卤水组分、温度、流速及管材等方面,分析卤水井开采测试中结晶产生的影响因素;从卤水的化学性质出发,探讨工具防腐技术;结合现场应用效果,进一步优化管柱结构。     

故县大坝变形监测存在的问题及自动化改造初步设想

介绍了故县大坝变形监测系统的构成及存在的问题，并根据故县大坝的实际情况，提出了实施自动化改造的初步设想。     

Uncertainty of Predictions of Embankment Dam Breach Parameters

Risk assessment studies considering the failure of embankment dams often require the prediction of basic geometric and temporal parameters of a breach, or the estimation of peak breach outflows. Many of the relations most commonly used to make these predictions were developed from statistical analyses of data collected from historic dam failures. The prediction uncertainties of these methods are widely recognized to be very large, but have never been specifically quantified. This paper presents an analysis of the uncertainty of many of these breach parameter and peak flow prediction methods. Application of the methods and the uncertainty analysis are illustrated through a case study of a risk assessment recently performed by the Bureau of Reclamation for a large embankment dam in North Dakota.     

沙牌RCC拱坝坝肩稳定三维地质模型试验研究

通过三维地质力学模型试验，研究了沙牌RCC拱坝坝体和坝肩的变形规律、破坏机理，综合评价了坝肩的超载安全度，为工程设计提供依据。     

大坝变形自动监测仪器

混凝土大坝的变形,尤其是坝基和拱座的变形,是衡量工程安全与否的一个重要指标。变形自动监测仪器与人工测读的仪器相比,具有精度高、时间快的优点。本文参考国外资料,提出测斜仪、钻孔变位计、垂线自动监测仪等三种国内外正在试用的电子仪器。将有助于提高并改善大坝设计的经济性和安全性。     

三门峡大坝温度观测资料分析

三门峡水利枢纽大坝为混凝土重力坝,原设计选择其溢流坝和安装场坝为重点观测坝段,因此,在施工中埋设了各种观测仪器1000多支。经对1958～1988年气温、坝体混凝土温度、基岩温度及水库水温观测资料分析表明:三门峡大坝温度变化规律虽与一般重力坝不同,但它却比较全面地反映了防洪与径流发电具体运用的特性。     

竹坑水库大坝安全鉴定分析

在竹坑水库大坝安全鉴定中,认真分析了该工程存在的主要问题,并针对大坝的结构稳定和渗流稳定作了详细的分析论证.     

强震区土石坝筑坝材料动力特性试验研究(投稿研究生论坛)

土石坝筑坝材料的动力特性是进行坝工设计和动力评价的基础。以西部强震区某土石坝工程为研究对象,采用室内动三轴试验,对其坝壳料、心墙料和坝基断层料进行动力特性试验研究,并以Hardin-Drnevich模型为基础,确定筑坝材料的动弹模、阻尼比等特性参数,试验表明：在动应变在10-6 ～ 10-5 范围内,动模量与围压、固结比成正相关;随动模量的增大,相关性降低;动应变和振级的拟合指数模型则表明不同围压、固结比的心墙料的应变-振级曲线趋势一致,坝壳料较为离散;试验中固结围压对粗骨料阻尼比-应变曲线的影响较固结比大,可为强震区土石坝的动力稳定评价提供可靠的理论依据。     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

锦屏一级拱坝黏弹性工作性态反演分析

针对锦屏一级拱坝历年1 880 m高水位稳定期坝体向下游侧位移持续增大的异常变形性态,基于现场位移监测数据和坝体混凝土徐变试验数据,对该拱坝当前运行性态进行了反演分析。结果表明:坝体目前处于黏弹性工作状态,监测发现的短期趋势性变形是由水压荷载作用下黏弹性滞后变形效应和年周期环境温降效应所共同引起。混凝土黏弹性力学参数之间具有较好的线性关系,基于弹性水压分量反演得到的坝体混凝土弹性模量实际是瞬时弹性模量与延迟弹性模量的等效值,对于锦屏一级拱坝,该反演值为35.5 GPa,与前期反演值和试验值较为接近;而基于黏弹性水压分量反演得到的广义Kelvin模型中瞬时弹性模量、2延迟弹性模量和2黏滞系数分别为47.3 GPa、189.4 GPa、132.3 GPa、383.8 GPa·d和20 574.6 GPa·d。建议今后对锦屏一级拱坝建立变形监控模型时,应在HST模型中引入黏弹性滞后水压分量。