本文選題：高面板堆石壩 + 參數(shù)反演　； 參考：《武漢大學》2017年碩士論文

【摘要】：隨著國家經濟發(fā)展,國內水電開發(fā)力度進一步加強,其主戰(zhàn)場逐步向西和西南方向轉移,隨之帶來的是面板堆石壩的規(guī)模從200m級向300m級跨越,300m級面板堆石壩的堆石體顆粒所處的應力環(huán)境相對于200m級面板堆石壩發(fā)生了較大的變化,高圍壓下所表現(xiàn)出來的變形特性更加復雜。由于尺縮效應的存在,目前常用的堆石體本構模型以及室內試驗的參數(shù)測定方式無法對大壩應力變形進行有效模擬,對大壩修建過程中或后期運行階段的應力變形特點和趨勢無法有效預測,使得面板堆石壩建設的安全性、經濟性得不到有效保障�；趯崪y資料對堆石體本構模型參數(shù)進行修正是指導大壩安全施工和變形預測的有效手段。已有的堆石體參數(shù)反演方法主要為單目標方法,對整個壩體或某個斷面僅采用一個目標函數(shù),其未考慮堆石材料對變形的區(qū)域性影響,將所有材料整合到一起,映射關系復雜,反演效果不佳,結果不穩(wěn)定。本文引入多目標的思想,將壩體劃分多個子區(qū)間,在每個子區(qū)間內建立反演子目標函數(shù),從而充分考慮材料的區(qū)域性影響,并基于非支配排序的多目標遺傳算法(NSGA-Ⅱ),從而兼顧了材料的全局性影響,提出了基于NSGA-Ⅱ和RBF神經網絡的堆石壩多目標參數(shù)反演方法。本文的主要研究內容如下:1)介紹了非支配排序的多目標遺傳算法(NSGA-Ⅱ)和RBF神經網絡的基本原理,以及與堆石壩參數(shù)反演工程應用的搭接技術,搭建了基于NSGA-Ⅱ算法和RBF神經網絡的高堆石壩多目標參數(shù)反演平臺,并系統(tǒng)闡述了該反演平臺的各個環(huán)節(jié)。2)總結了堆石壩的分區(qū)原則和典型形式,并以此提出了子區(qū)間劃分的基本原理、方法和優(yōu)勢。以一種典型材料分區(qū)模式為例,建立標準壩模型,進行材料區(qū)域敏感性分析研究,總結規(guī)律,驗證了堆石材料對變形的區(qū)域性影響以及提出了針對該分區(qū)模式的反演子區(qū)間劃分方案。3)闡述了堆石體E-B本構模型和流變本構模型的基本原理,以及瞬變-流變的有限元聯(lián)合計算法方法。并通過對瞬變和流變本構模型的力學參數(shù)敏感性研究,選出了最合適的反演參數(shù)。通過整理和分析水布埡面板堆石壩的變形監(jiān)測資料,選出了最合適的反演斷面和測點。4)運用上述方法對水布埡主堆石和次堆石的瞬變-流變參數(shù)進行了反演,并與單目標反演方法進行了對比分析。計算結果表明,多目標參數(shù)反演方法不僅在變形量和變形趨勢上與實測值基本一致,且所有監(jiān)測點的反演計算值與實測值均吻合較好,計算結果的穩(wěn)定性較好,整體變形和應力分布規(guī)律均符合實際,而單目標參數(shù)反演的計算結果只在局部區(qū)域與實測值較為接近,計算結果不穩(wěn)定,多目標參數(shù)反演計算結果明顯優(yōu)于單目標參數(shù)反演,說明了本文提出的多目標參數(shù)反演方法具有合理性和優(yōu)越性。
[Abstract]:With the development of the national economy, domestic hydropower development has been further strengthened, and its main battlefield has gradually shifted to the west and southwest. As a result, the stress environment of the rockfill grain in the face rockfill dam from 200m to 300m is changed compared with that of 200m CFRD. The deformation characteristics under high confining pressure are more complicated. Due to the existence of scale shrinkage effect, the commonly used constitutive model of rockfill and the measurement of parameters in laboratory tests can not effectively simulate the stress and deformation of the dam. The characteristics and trends of stress and deformation in the process of dam construction or in the later stage of operation can not be effectively predicted, which makes the construction of face rockfill dam safe and economic can not be effectively guaranteed. It is an effective method to guide dam safety construction and deformation prediction by modifying the parameters of constitutive model of rockfill based on the measured data. The existing inversion methods of rockfill parameters are mainly single-objective method. Only one objective function is used for the whole dam body or a section. The method does not take into account the regional influence of rockfill material on deformation, so all materials are integrated together and the mapping relationship is complex. The inversion effect is not good and the result is unstable. In this paper, the idea of multi-objective is introduced, the dam body is divided into several sub-regions, and the inversion subobjective function is established in each sub-interval, so as to fully consider the regional influence of the material. A multi-objective genetic algorithm (NSGA- 鈪，

本文編號：2097959