文章摘要
任秋兵,沈扬,李明超,孔锐,李明昊.水工建筑物安全监控深度分析模型及其优化研究[J].水利学报,2021,52(1):71-80
水工建筑物安全监控深度分析模型及其优化研究
Safety monitoring model of hydraulic structures and its optimization based on deep learning analysis
投稿时间:2020-04-24  修订日期:2020-11-09
DOI:10.13243/j.cnki.slxb.20200270
中文关键词: 水工建筑物  安全监控  深度学习  长短期记忆网络  智能分析
英文关键词: hydraulic structure  safety monitoring  deep learning  long short-term memory networks  intelli- gent analysis
基金项目:国家重点研发计划项目(2018YFC0406905);国家优秀青年科学基金项目(51622904);国家自然科学基金面上项目(51879185)
作者单位E-mail
任秋兵 水利工程仿真与安全国家重点实验室, 天津大学, 天津 300354  
沈扬 中国长江三峡集团有限公司, 北京 100038 tjusy1984@163.com 
李明超 水利工程仿真与安全国家重点实验室, 天津大学, 天津 300354  
孔锐 中国电建集团 西北勘测设计研究院有限公司, 陕西 西安 710065  
李明昊 水利工程仿真与安全国家重点实验室, 天津大学, 天津 300354  
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中文摘要:
      随着水工建筑物安全管理自动化技术的发展,以丰富性、多样性、复杂性为特点的大数据逐渐成为水工建筑物安全监控体系的显著特征。常用安全监控数学模型(三大常规模型、浅层学习算法)难以从大量数据中自动提取深层次潜在信息,即浅层模型与大数据挖掘分析不相适应。深度学习算法由多重非线性映射层构成,能够逐层学习输入数据本质特征并完成高级抽象,但也存在工程适用性不佳等问题。为解决这方面的难题,本文总结安全监控大数据特性,引入长短期记忆深层网络(Long Short-Term Memory,LSTM),提出了适用于不同类型水工建筑物的安全监控深度分析模型,并对算法进行了优化。该模型以竞争学习机制为核心,采用数字滤波、限定区间、滚动迭代等策略,从前端处理、网络结构和外延预测三方面对LSTM算法进行了改进,并通过随机搜索和步进式验证实现了最优化建模。结合实际工程选取了多组不同效应量实测数据作为典型应用场景,通过仿真对比实验对所提方法的有效性进行了验证评估。结果表明,与常规、浅层模型相比,多数场景下深度模型更适合用于安全监控大数据处理,以期为水工建筑物安全运行提供决策支持。
英文摘要:
      With the development of automation technology for safety management of hydraulic structures, big data characterized by richness, diversity and complexity has gradually become a significant feature of safety monitoring system of hydraulic structures. The commonly used mathematical models of safety monitor- ing (three conventional models and shallow learning algorithms) are difficult to extract the deep underlying information automatically from large amounts of data, i.e. the shallow model is incompatible with big data mining and analysis. Deep learning algorithm is composed of multiple nonlinear mapping layers, which can learn the essential characteristics of input data layer by layer and complete the high-level abstraction, but it also has some problems such as poor engineering applicability. To address this issue, this paper summa- rizes the features of safety monitoring big data,introduces long-term short-term memory (LSTM),and pro- poses an optimized deep analysis model for safety monitoring of different types of hydraulic structures. The model takes competitive learning mechanism as the core, adopts digital filtering, limited interval and roll- ing iteration to improve LSTM from three aspects of front-end processing, network structure and epitaxial prediction. It also achieves optimization modeling through random search and step verification. Combining with engineering projects,several groups of measured data of different effect quantities were selected as typ- ical application scenarios, and the effectiveness of the proposed method has been verified and evaluated through simulation and comparison experiments. The results indicate that compared with the shallow model, the deep model is more suitable for safety monitoring big data processing in most scenarios, so as to pro- vide decision support for the safe operation of hydraulic structures.
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