梯级水电枢纽群巨灾风险分析与防控研究综述

伴随超标准巨震、极端暴雨洪水、巨型滑坡等灾害频繁发生,极端载荷作用下梯级水电枢纽群的灾害风险分析与防控等问题成为当前水利工程领域的研究热点。为分析梯级水电枢纽群巨灾风险研究现状,绘制了国内外水库大坝溃坝事件的时间序列图,分析了梯级水电枢纽群的风险特征,总结评述了梯级水电枢纽群巨灾风险分析和巨灾防控研究进展,主要结论如下：1)梯级水电枢纽群巨灾风险是我国水利水电工程风险防控面临的主要问题;2)梯级水电枢纽群风险分析方面主要聚焦于梯级水库连溃概率的分析和计算,对于溃决可能产生的巨灾损失的量化研究不足,缺乏对巨灾因子及其相关影响作用下巨灾风险的评估;3)缺乏对梯级枢纽群灾害链的阻断技术研究和应急避险方案设计研究。为此提出了梯级水电枢纽群可能最大灾难(Probable Maximum Disaster, PMD)的科学内涵,考虑可能遭遇的多种致灾因子和承灾体特征,分析相互因果关系和极端荷载组合情况,初步建立了PMD评估的理论模型,为绘制梯级水电枢纽群在巨灾情景下的灾难空间外包线和估算PMD损失上限值提供科学依据,为梯级水电枢纽群巨灾风险分析和防控提供理论基础和技术支持。     

一种配电网投资项目的综合评价与优选排序方法

配网投资项目具有项目众多、规模相对小、项目繁杂、时间紧迫等配电网建设和运维自身的特点,在投资需求远大于投资能力的情况,基于项目综合评价实现优选排序是实现配电网精准投资的关键手段.为此,提出了一种计及指标约束同时兼顾投资效益的中压配电网项目优选方法.以配电网投资效果为指标构建评价体系,并利用建设目标对项目属性进行分类,基于灰色关联度法深入挖掘项目属性与评价指标间的关联关系,探明指标改善所对应的投资概算.为了同时兼顾规划指标与投资成效,提出了计及指标约束的配网项目两阶段优选排序法,将优选过程分为针对关键规划指标的项目优选和针对普通指标的项目优选两个阶段.通过对某地市电力公司中压配电网项目进行优选评估,验证了所提方法的有效性与合理性.     

Performance Evaluation Indicator (PEI): A new paradigm to evaluate the competence of machine learning classifiers in predicting rockmass conditions

To illustrate an unprejudiced comparison among machine learning classifiers established on proprietary databases, and to guarantee the validity and robustness of these classifiers, a Performance Evaluation Indicator (PEI) and the corresponding failure criterion are proposed in this study. Three types of machine learning classifiers, including the strictly binary classifier, the normal multiclass classifier and the misclassification cost-sensitive classifier, are trained on four datasets recorded from a water drainage TBM project. The results indicate that: (1) the PEI successfully compares the competence of classifiers under different scenarios by isolating the effects of different overlapping-degree of rockmass classes, and (2) the cost-sensitive algorithm is warranted to classify rockmasses when the ratio of inter-class classes is more than 8:1. The contributions of this research are to fill the gap in performance evaluations of a classifier for imbalanced training data, and to identify the best situation to apply this classifier.     

A real-time vehicle detection and a novel vehicle tracking systems for estimating and monitoring traffic flow on highways

Real-time highway traffic monitoring systems play a vital role in road traffic management, planning, and preventing frequent traffic jams, traffic rule violations, and fatal road accidents. These systems rely entirely on online traffic flow info estimated from time-dependent vehicle trajectories. Vehicle trajectories are extracted from vehicle detection and tracking data obtained by processing road-side camera images. General-purpose object detectors including Yolo, SSD, EfficientNet have been utilized extensively for real-time object detection task, but, in principle, Yolo is preferred because it provides a high frame per second (FPS) performance and robust object localization functionality. However, this algorithm’s average vehicle classification accuracy is below 57%, which is insufficient for traffic flow monitoring. This study proposes improving the vehicle classification accuracy of Yolo, and developing a novel bounding box (Bbox)-based vehicle tracking algorithm. For this purpose, a new vehicle dataset is prepared by annotating 7216 images with 123831 object patterns collected from highway videos. Nine machine learning-based classifiers and a CNN-based classifier were selected. Next, the classifiers were trained via the dataset. One out of ten classifiers with the highest accuracy was selected to combine to Yolo. This way, the classification accuracy of the Yolo-based vehicle detector was increased from 57% to 95.45%. Vehicle detector 1 (Yolo) and vehicle detector 2 (Yolo + best classifier), and the Kalman filter-based tracking as vehicle tracker 1 and the Bbox-based tracking as vehicle tracker 2 were applied to the categorical/total vehicle counting tasks on 4 highway videos. The vehicle counting results show that the vehicle counting accuracy of the developed approach (vehicle detector 2 + vehicle tracker 2) was improved by 13.25% and this method performed better than the other 3 vehicle counting systems implemented in this study.     

ＴＢＭ隧道下穿市政桥梁施工变形规律研究

针对ＴＢＭ不同角度下穿既有结构,对既有结构产生的扰动问题,依托青岛地铁１号线海泊桥站到小村庄站区间ＴＢＭ隧道下穿市政桥梁工程,利用ＦＬＡＣ３Ｄ数值模拟分析软件与现场施工监测相结合的方法,建立相应的三维地质力学模型,研究双线ＴＢＭ隧道施工对既有桥梁结构的受力特征与变形规律,揭示ＴＢＭ隧道下穿市政桥梁施工变形规律。计算结果表明:ＴＢＭ下穿会导致既有桥面板向上隆起,左线隧道开挖引起的桥面板隆起值要远远大于右线开挖;当左线隧道开挖到桥梁跨中时,桥头与桥尾隆起量最大,最大值约４．６３ｍｍ;新建隧道下穿既有结构尽量采用小角度下穿,避免既有结构发生大变形。     

公共文化PPP项目中承包商机会主义行为奖惩机制演化博弈

在公共文化PPP项目管理模式中,承包商为了追求自身利益在一定情况下可能会选择利用不对称信息采取机会主义行为转移项目利益,这将会损害政府建设单位的利益,影响公众的享受公共文化服务的权利,扰乱政府建设市场,因此建设单位需采取一定的管理手段来抑制承包商的机会主义行为.运用演化博弈理论分析政府建设单位应对承包商机会主义行为的演化路径,分别建立惩罚机制和补偿机制2种状态下的演化博弈模型,分别探究甲乙双方在惩罚机制和补偿机制下的演化均衡策略.结果表明,惩罚机制并未对承包商机会主义行为产生实质性的抑制效果,而补偿机制在一定条件下可显著影响承包商的机会主义行为.基于此,政府建设单位可充分利用补偿机制手段应对承包商的机会主义行为.     

论煤矿井巷工程中工程变更投资的控制

工程变更是一项贯穿煤矿井巷工程建设全过程的经常性工作.文章首先从两个方面对井巷工程的工程变更进行了分类,然后对常见的影响工程变更情况进行了分析,最后从明确井巷工程变更的目的、严格控制井巷工程设计变更、严格计量管理把好签证关、严格计价管理做好决算管理等五个方面提出了控制井巷工程工程变更投资的方法.     

高速公路机电工程施工难度系数评估模型

依据高速公路机电工程施工特点,采用层次分析法和模糊数学相结合的方法对高速公路机电系统的施工难度进行综合评估。通过实例分析表明,基于AHP的高速公路机电工程施工难度评估模型计算客观、公正和合理,可为高速公路机电管理在合理配置人、财、物等资源,考核工程施工提供科学的依据。     

关于质量管理在矿井建设项目中的应用探讨

质量管理是矿井建设项目中的重要组成部分,将质量管理与矿井建设项目结合在一起,并作出相关的应用探讨,有利于矿井企业的健康、稳定发展。简要叙述了矿井建设项目中存在的问题和矿井质量管理的主要内容,进一步研究了质量管理在矿井建设项目中的应用,以期能为矿井建设项目提供一些借鉴。     

丹江口大坝加高改扩建工程档案管理浅析

丹江口大坝加高工程的档案管理不同于一般水利工程档案管理,它具有新旧档案形成时间间隔长、法律依据不统一和档案归档要求不同等特点。指出了改扩建工程档案管理存在的问题,介绍了南水北调中线水源有限公司在丹江口改扩建水利工程档案管理实践中所取得的宝贵经验,包括建立完善的档案保障体系和约束机制,科学分类以促进新老档案结合,加强档案收集、整理和归档,充分发挥业主核心作用,推进档案信息化建设等具体措施。可为其他改扩建水利工程档案管理工作提供参考。