三峡大坝应用碾压混凝土的研究

三峡工程具有工程量大、工期长的特点,为加快施工进度、缩短工期、节约水泥用量,拟在大坝的部分部位采用碾压混凝土。根据研究,考虑到坝体结构布置、构造要求、ＲＣＣ施工方法等因素,各坝段采用ＲＣＣ的部位将有所不同,总的采用原则是在坝体多钢筋的大孔口以下和结构布置简单而较规则的部位应用ＲＣＣ,但在基岩面上仍铺设１．０－１．５米厚的常态混凝土垫层,按上述原则,各坝段应用ＲＣＣ混凝土的总量约占大坝总混凝土量的１     

大坝变形预测的最优因子长短期记忆网络模型

面对海量的大坝安全监测数据,快速合理地确定大坝变形预测模型的变量因子能够有效提高模型预测的效率和精度。为此,本文提出一种基于最小绝对值收缩和选择算子（least absolute shrinkage and selection operation,LASSO）变量选择和长短期记忆（long short-term memory,LSTM）网络的大坝变形预测模型。首先,通过大坝变形机理分析确定影响大坝变形的相关影响因子集。然后,通过LASSO算法剔除不显著的因子,筛选出最优影响因子作为模型输入变量,并利用LSTM网络建立大坝变形预测模型。最后,以皂市水利枢纽工程的碾压混凝土重力坝为例,对本文方法进行了验证和讨论。结果表明,本文方法具有较高的预测精度,其平均绝对误差（MAE）、均方误差（MSE）与均方根误差（RMSE）均相对较小;与常规预测模型相比,基于LASSO算法的变量选择使模型建立过程更加简单高效,有利于海量监测数据的处理分析。     

Intelligent analysis method of dam material gradation for asphalt-core rock-fill dam based on enhanced Cascade Mask R-CNN and GCNet

Reasonable dam materials’ gradation design for asphalt-core rock-fill dams is one of the main ways to control permeability. It is a challenge to test whether it can meet the requirements of dam construction. The computer vision method provides a new idea for asphalt-core rock-fill dam material gradation testing. However, due to the characteristics of densely overlapping and multi-scale sizes of dam material particles, the traditional image segmentation methods and algorithms cannot achieve accurate segmentation of dam materials’ images, and it is hard to apply the segmentation result to quantify the gradation curve. In this research, the enhanced Cascade Mask R-CNN with ResNet and PAFPN (Path Aggregation Feature Pyramid Networks) is proposed. Multi-scale features extracted by ResNet and feature ensemble can be realized using PAFPN. Data augmentation (DA) and online hard example mining (OHEM) are also applied in segmentation model training. Moreover, the GCNet is proposed to calibrate the gradation curve. The nonlinear relationship between the real gradation and the one based on the segmentation results can be revealed and the model of dam materials’ gradation analysis can be established. In the research, the enhanced Cascade Mask R-CNN can achieve 84.2 mAP, which is higher than that of Cascade Mask R-CNN with 74.9 mAP. The effectiveness of the proposed module and training strategies is proved using ablation experiments. The average error of each level for the gradation calibration using GCNet is 0.55%, 1.87%, 2.22%, 1.18%, and 2.42% respectively. The accuracy can meet the requirements of hydraulic engineering construction, which verifies the effectiveness of the GCNet network for gradation calibration, and the research provides a new method and technology for intelligent gradation testing of the asphalt-core rock-fill dam.     

联合聚类方法和深度学习的混凝土坝变形预测

混凝土大坝变形预测对其安全运行具有重要意义,针对传统分析方法难以捕捉长期序列时序特征从而导致预测精度较低的问题,本文采用麻雀优化算法（SSA）和K调和均值算法（KHM）相结合对监测值进行聚类以捕捉序列时序特征,然后采用自适应噪声完备集合经验模态分解（CEEMDAN）等方法对聚类结果进行降噪处理,最后采用长短期记忆（LSTM）模型对序列进行预测。分析结果表明,本文所提出的聚类方法具有较好的长序列特征识别能力,结合基于CEEMDAN分解方法去除序列中存在的冗余信息,从而使LSTM模型能够更好地捕捉变形值的时序特性,进而提高预测精度。所提模型具有较好的精度和适应性,可为大坝变形预测提供一种有效方法。     

破坏准则模糊性对心墙堆石坝地震易损性影响北大核心CSCD

心墙堆石坝地震破坏是渐变的过程,目前地震易损性研究均采用确定性的破坏准则;然而由于对破坏等级认知的局限性,导致地震破坏等级的划分具有模糊性。针对上述问题,本文提出考虑破坏准则模糊性的心墙堆石坝地震易损性分析方法。首先,采用模糊集理论将大坝破坏等级界限值模糊化,并提出能够反映隶属度函数形式和模糊区间大小的模糊度系数来量化地震模糊破坏等级;其次,依据地震模糊破坏等级推导地震易损性函数模糊-概率积分计算公式,弥补目前地震易损性函数计算公式依据确定性破坏准则的不足,并结合概率地震需求模型,获得破坏准则模糊条件下的大坝地震易损性曲线,得到坝体结构在不同地震强度作用下产生各个等级破坏的概率。将所提方法应用于糯扎渡心墙堆石坝地震易损性分析中,结果表明:考虑破坏准则模糊性获得的地震易损性曲线与传统易损性曲线趋势一致;在破坏准则模糊性条件下,相同坝顶震陷率对于发生高等级破坏的隶属度增加,导致在一定的地震动强度下,坝体发生高等级破坏的概率有所增加;此外,模糊度系数越大,破坏准则模糊性对地震易损性的影响越大。     

白鹤滩特高拱坝层间性能监控系统研究与应用

恶劣的环境会对特高拱坝层间结合性能产生不利影响。本研究测试了大坝混凝土在不同温度下的层间劈裂抗拉强度,建立了成熟度与强度系数的关系。同时,基于成熟度与强度系数及含水量的关系提出了以成熟度为主含水量为辅的大坝层间性能三级九梯度预警体系。结果表明,随着温度的升高,大坝混凝土层间力学性能呈下降趋势。同时,成熟度与混凝土的强度系数及含水量线性相关,相关系数分别为0.9985和0.9964。此外,根据成熟度与层间性能参数之间的关系所建立的预警体系,可以对大坝层间结合质量进行有效的控制。     

白鹤滩首蓄期变形预测混合模型方法北大核心CSCD

首蓄期准确预测坝体变形,合理安排蓄水计划对于特高拱坝安全进入运行期具有重要意义。为解决特高拱坝首蓄期坝体变形预测较难的问题,本文提出了一种特高拱坝首蓄期坝体变形预测混合模型方法,并结合白鹤滩特高拱坝首蓄期坝体变形监测资料进行工程实例验证。该模型结合白鹤滩特高拱坝首蓄期三阶段蓄水计划的背景,结合三个蓄水目标下白鹤滩拱坝拱冠梁坝段正垂测点的监测数据量大小,在首蓄期初期采用多元回归模型,在首蓄期中后期对PLdb18-2到PLdb18-6五个测点采用优化的LSTM模型,对于坝顶的PLdb18-1采用多元回归模型。本文针对混合模型及全过程采用单一模型的预测结果和实测结果进行对比,本文提出的混合模型方法精度最高,误差率不超过4%,且具有较好鲁棒性。     

Firm diversity and data breach risk: A longitudinal study

Research has extensively investigated the rationale of firm diversity from the economic perspective, but little is known about how such a strategy may affect information security. The present study is the first to examine how firm diversity is relevant to firms’ likelihood to experience data breaches (i.e., data breach risk). Drawing from the strands of literature on information security, diversification, and resource-based view, we propose hypotheses on the relationship between firm diversity and data breach risk, as well as the boundary conditions of this relationship. On the basis of a twelve-year sample of publicly-listed firms, our analysis provides evidence to support the negative association between firm diversity and data breach risk. Our analysis also delineates conditions under which the effects of firm diversity can intervene to reduce the data breach risk invoked, such as under related diversity and when managers are managerially capable. For academics, our research accentuates an intriguing but unexamined benefit of firm diversity because it relates to information security. For practicing professionals, this research highlights the significant impact of firms’ operational structure on information security.     

水工混凝土大坝表面防护涂层材料研究进展

由于水利工程的特殊性,水工混凝土大坝长期处于含砂高速水流冲刷侵蚀、气温变化大、冻融频繁等恶劣环境下,其耐久性直接影响着水利工程的安全性和服役寿命。在混凝土表面涂覆防护涂层可有效阻止有害介质渗入混凝土毛细孔内部,从而达到提高混凝土耐久性的目的,被广泛应用于大坝表面的防护领域。简要综述了近年来发展起来的浸渍型有机硅、氟碳、环氧树脂、丙烯酸酯、聚氨酯、聚脲和清水混凝土复合涂料体系等多种新型坝面防护涂层材料的特点及最新研究成果,并总结了混凝土防护涂层材料的发展方向。     

泽雅水库导流洞混凝土堵头爆破拆除设计及振动影响

邻近建构筑物爆破中振动控制对爆破设计有较高要求。以泽雅水库导流洞改建泄洪放空洞工程为例,研究了面板堆石坝坝肩正下方的导流洞混凝土堵头爆破拆除方案及振动影响。基于短进尺、多循环的爆破设计理念,提出了断面分上下两部分分部开挖的爆破拆除方案及最大孔深为1.0 m的初步钻爆参数,并通过现场爆破试验确定了最大孔深可加大到1.5 m的钻爆设计参数。爆破施工过程中的爆破振动跟踪监测结果表明,混凝土堵头爆破拆除未影响水库大坝及其他建构筑物的安全。