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。     

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.     

三峡枢纽排漂孔及泄洪孔过流面检修装备方案研究

针对三峡工程排漂孔及泄洪孔过流面墙壁的检修分别提出了多种专用检修装备方案。经过分析比较,得出安全合理的检修装备方案。排漂孔检修方案:闸室段侧墙用升降台车,斜坡段侧墙采用单梁吊篮,排漂孔底板采用双侧单台车;泄洪深孔检修方案:闸室段和斜坡段侧墙采用升降台车检修,底面与排漂孔检修方案相同。     

“网河”非稳定流模型在洪泛区洪流演进中的应用

将宽阔的溃口洪水行进范围概化成由河道水塘和联系河道组成的网河,用一维非恒定流方程组来描述主要的水充运动状态,通过简化和变换,改写成统一的四点线性隐式有限差的方程组形式,再用双消除法求解,全部计算在微机上实现,参数用实测资料率定,成果具有较好的精度,在一定程度上反映出横水流方向的水位流量变化,为防洪水利计算开辟一条新的途径。     

液压卧倒闸坝在拦河坝工程中的应用

简述液压卧倒闸坝在江西省宜春市袁州区温汤镇防洪工程拦河坝中的应用。设计通过对多方案技术经济比选,推荐采用液压卧倒闸坝。可供类似工程参考。     

观音岩水电站右岸大坝首次基础固结灌浆生产性试验成果分析及建议

观音岩水电站右岸混凝土坝段坝基地质条件复杂,通过固结灌浆生产性试验,确定合理的施工参数和施工工艺措施,验证设计指标的合理性。本文对此加以介绍。     

基于加卸载响应比理论的大坝台风作用效应计算方法

台风对大坝的作用包括台风引起的强降雨对大坝的影响、台风引起的库水位波动(波浪)对大坝的影响以及台风风荷载对大坝的影响等。基于加卸载响应比理论,通过对台风作用下的大坝效应增强和维持判据进行深入研究,提出了库水位加卸载响应比和波浪加卸载响应比的计算方法,给出了台风作用下大坝效应增强与维持判据的评判准则。利用上述方法和评价准则,对受2010年10号台风"莫兰蒂"影响的东坞水库进行分析,结果表明,东坞水库大坝受台风影响不大,大坝处于稳定状态。将加卸载响应比理论引入到水利工程领域,是对加卸载响应比理论的应用范围的拓展,也是研究台风作用下大坝稳定性的新思路。     

Predicting dam failure risk for sustainable flood retention basins: A generic case study for the wider Greater Manchester area

This study aims to provide a rapid screening tool for assessment of sustainable flood retention basins (SFRBs) to predict corresponding dam failure risks. A rapid expert-based assessment method for dam failure of SFRB supported by an artificial neural network (ANN) model has been presented. Flood storage was assessed for 110 SFRB and the corresponding Dam Failure Risk was evaluated for all dams across the wider Greater Manchester study area. The results show that Dam Failure Risk can be estimated by using the variables Dam Height, Dam Length, Maximum Flood Water Volume, Flood Water Surface Area, Mean Annual Rainfall (based on Met Office data), Altitude, Catchment Size, Urban Catchment Proportion, Forest Catchment Proportion and Managed Maximum Flood Water Volume. A cross-validation R² value of 0.70 for the ANN model signifies that the tool is likely to predict variables well for new data sets. Traditionally, dams are considered safe because they have been built according to high technical standards. However, many dams that were constructed decades ago do not meet the current state-of-the-art dam design guidelines. Spatial distribution maps show that dam failure risks of SFRB located near cities are higher than those situated in rural locations. The proposed tool could be used as an early warning system in times of heavy rainfall.