Research on Dam Safety Monitoring System Based on Artificial Neural Networks
作者：盧瑞章 來源：西安理工大學 專業：農業水土工程 學位年度：2005 導師：李智錄
中文摘要：
大壩的安全狀態不僅直接影響著工程經濟效益的發揮,而且關系到下游居民的生命財產安全乃至整個社會的穩定。因此,開發和完善一套實用可靠的大壩安全監測系統對企業、社會以及廣大人民群眾都具有極其重大的意義。 本文結合黑河、石頭河、石砭峪、澗峪等水庫大壩的監測項目,在分析和總結各大壩共性和個性的基礎上,以統計模型作為對比,研究了人工神經網絡模型及其在大壩安全分析中的應用,并開發出大壩監測分析系統,大大增加了大壩狀況分析的速度和便利性。 主要研究內容和成果如下: (1) 在對目前常用數據預處理算法的研究分析基礎上,建立了一種能夠實時進行粗差(野值)識別、數據整編的預處理算法,實例測試表明其具有很好的適應性,處理效果較為理想。 (2) 簡單討論了大壩統計模型的因子選擇依據,分析了統計模型建模的局限性。 (3) 測試了RBF神經網絡特有的局部性能,并在理論上對這些特性做了分析說明;針對其特點設計出一種自動增加模式節點的在線和快速離線學習算法,實例測試表明該算法在速度和在線記憶等性能上較其他都有很大的提高。 (4) 在不同條件下對RBF網絡與統計回歸模型做了測試對比,結果表明RBF神經網絡建模的條件比較寬松,對輸入因子相關性的敏感度很小,建模簡單方便。 (5) 采用先進的開發平臺和工具,開發出了基于B/S結構的大壩安全監測數據西安理工大學碩士學位論文分析系統。該系統功能完備,界面友好,可操作性強,自動化程度高。另外,高度的模塊化結構使系統具有良好的重用和擴充特性,便于在不同_L_程中使用。關鍵詞:大壩安全監測,人工神經網絡,RBF,B/S結構
Abstract：
The security state of dam not only influences the exertion of economic benefits of projects directly, but concerns the safety of life and property of downstream residents and even the stability of the whole society. Therefore, it is very significant to exploit a practical and credible dam safety monitoring system for enterprise, society and the broad masses of the people.Combined with the monitoring projects of the Black River, the Stone River, the Shibianyu River and the Jianyu River, based on analyzing and summarizing the commonness and individuality of these dams, compared with statistic model, researches were carried out on Artificial Neural Networks model and its use in the dam safety analysis. Besides, a dam safety monitoring system was developed, which increases the speed and facility of dam safety analysis greatly.Major contents and findings are as follows:(1) Based on the analysis of monitored data of dam and common arithmetic, a data pretreatment arithmetic was established which could identify and reorganize the data real-timely. The testes indicate that it has great adaptability and perfect treatment effect.(2) The basis of the factor chooses for statistical model of dam monitoring was simply analyzed, as well as its limitation of building model.(3) The part performances of RBF Neural Networks were tested and analyzed theoretically. Based on the characteristics of RBFNN, a celerity off-line and on-line training arithmetic which could add nodes automatically was designed and the test indicates that this arithmetic enhances the velocity and study ability greatly compared with others.(4) The tests on
RBF and statistical model in different conditions indicate that the former is insensitive to the factor correlativity of input-data and used simply and conveniently.(5) By using the advanced developing platform and tools, the dam safety monitoring system was developed based on B/S frame. The system has the characters of perfect function, friendly interface, good effectiveness and high automatic degree. In addition, the powerful module structure makes the system have a good reuse and expansion characteristic.
目錄章節：
基于神經網絡的大壩安全監測分析系統研究
| 目錄 | 7-9 |
| 1． 緒論 | 9-20 |
| 1．1 大壩安全監測分析的作用及其重要性 | 9-12 |
| 1．1．1 大壩安全監測的作用 | 9-12 |
| 1．1．2 大壩安全監測的重要性 | 12 |
| 1．2 大壩監測概述 | 12-18 |
| 1．3 本文主要內容及其技術路線 | 18-20 |
| 2． 數據的在線處理 | 20-30 |
| 2．1 在線異常數據識別處理 | 20-29 |
| 2．1．1 異常數據識別的常用方法 | 20-24 |
| 2．1．2 在線異常數據處理方法 | 24-29 |
| 2．2 小結 | 29-30 |
| 3． 離線分析的統計模型方法 | 30-41 |
| 3．1 回歸統計方法 | 30 |
| 3．2 多元回歸的遞推算法 | 30-32 |
| 3．3 土石大壩監測統計模型物理量的分析 | 32-35 |
| 3．3．1 位移模型的因子選擇 | 32-34 |
| 3．3．2 滲流因子的選擇 | 34-35 |
| 3．4 土石壩分析的統計模型 | 35-39 |
| 3．4．1 水平位移統計模型 | 35-37 |
| 3．4．2 豎向位移(沉降)統計模型 | 37-38 |
| 3．4．3 滲流壓力統計模型 | 38-39 |
| 3．5 統計建模的局限性 | 39-40 |
| 3．6 小結 | 40-41 |
| 4． 離線分析的神經網絡模型方法 | 41-73 |
| 4．1 人工神經網絡介紹 | 41-50 |
| 4．1．1 人工神經網絡的涵義及結構 | 41-42 |
| 4．1．2 人工神經網絡的學習與記憶方式 | 42-44 |
| 4．1．3 誤差后向傳播(BP)網絡結構 | 44-46 |
| 4．1．4 徑向基函數(RBF)網絡結構 | 46-48 |
| 4．1．5 神經網絡的類型選擇 | 48-50 |
| 4．2 RBF神經網絡的在線學習方式研究 | 50-63 |
| 4．2．1 RBF神經網路局部特性的測試 | 50-56 |
| 4．2．2 RBF網絡的一種有效的在線學習方法 | 56-61 |
| 4．2．3 測試 | 61-63 |
| 4．3 大壩監測神經網絡模型研究 | 63-72 |
| 4．4 小結 | 72-73 |
| 5． 大壩監測分析系統的設計與開發 | 73-88 |
| 5．1 大壩監測分析系統的設計 | 73-79 |
| 5．1．1 系統需求分析及功能設計 | 73-74 |
| 5．1．2 系統體系結構設計 | 74-77 |
| 5．1．3 數據庫設計 | 77-79 |
| 5．2 大壩監測分析系統介紹 | 79-87 |
| 5．2．1 數據預處理子系統 | 79-80 |
| 5．2．2 數據分析子系統 | 80-87 |
| 5．3 小結 | 87-88 |
| 6． 總結 | 88-91 |
| 6．1 總結 | 88-89 |
| 6．2 建議 | 89-91 |
| 致謝 | 91-92 |
| 附錄 | 92-93 |
| 參考文獻 | 93-97 |
