本文選題：地質(zhì)災害　切入點：高地應力　出處：《浙江大學》2014年博士論文　論文類型：學位論文

【摘要】：巖爆是高地應力條件下的一種典型工程地質(zhì)災害,給地下工程施工人員和設備安全造成嚴重威脅。隨著我國水利、交通和采礦事業(yè)的快速發(fā)展,高地應力環(huán)境中的深部巖體開挖越來越多,巖爆的預防與控制問題將越來越突出,成為深部地下工程地質(zhì)災害防治領(lǐng)域的重要課題。 巖爆預測是巖爆防控的重要內(nèi)容。準確的巖爆預測有助于在設計和施工中采取相應的工程對策,減少或避免巖爆災害帶來的損失。但由于巖爆機理復雜,使得巖爆預測十分困難。目前,工程實際中一般采用簡單分級的方法對巖爆進行預測,由于不能考慮各種因素的綜合影響,其結(jié)果往往與實際情況出入較大。 針對目前巖爆預測存在的問題,本文主要在以下幾方面開展了系統(tǒng)的研究： (1)以蒼嶺隧道為例,采用傳統(tǒng)的強度理論方法對其巖爆進行了系統(tǒng)的預測分析,對已有方法存在的問題進行了探討。 (2)針對傳統(tǒng)強度理論的缺陷,考慮巖爆的特點,采用粒子群算法對廣義回歸神經(jīng)網(wǎng)絡進行了優(yōu)化,構(gòu)建了客觀的巖爆預測模型,采用該模型對蒼嶺隧道、錦屏二級水電站兩個深埋地下工程進行了巖爆預測,闡述該方法的特點和局限性。 (3)考慮到巖爆分析數(shù)據(jù)是連續(xù)數(shù)據(jù),而巖爆等級是離散數(shù)據(jù)的特點,結(jié)合現(xiàn)場調(diào)查結(jié)果和國內(nèi)外工程實例,采用粗糙集理論對巖爆影響因素進行了重要性區(qū)分和客觀定量評價。 (4)從多目標規(guī)劃原理出發(fā),結(jié)合粗糙集理論分析成果和理想點方法,構(gòu)建了粗糙集-理想點巖爆預測模型,通過對蒼嶺隧道和錦屏二級水電站的巖爆預測驗證了其正確性和適用性。 (5)從信息融合角度出發(fā),結(jié)合粗糙集理論分析成果和理想點方法,構(gòu)建了粗糙集-理想點巖爆預測模型,同樣通過上述兩個工程實例對模型進行了驗證。 (6)開展粗糙集-理想點法模型、粗糙集-證據(jù)理論模型和模糊數(shù)學方法模型的對比分析,評價各種方法的優(yōu)缺點和預測效果。 通過上述這些內(nèi)容的研究,獲得了以下一些創(chuàng)新成果： (1)蒼嶺隧道巖爆預測結(jié)果顯示,與普通BP神經(jīng)網(wǎng)絡和普通廣義回歸神經(jīng)網(wǎng)絡相比,粒子群算法-廣義回歸神經(jīng)網(wǎng)絡模型輸出結(jié)果穩(wěn)定,預測結(jié)果準確,但該模型在預測錦屏二級水電站探洞巖爆時出現(xiàn)錯誤,說明其適用性存在一定的局限性。 (2)粗糙集理論分析結(jié)果顯示應力集中程度對巖爆影響最大,巖體的儲能情況影響居中,巖體的脆性條件影響相對較小。 (3)蒼嶺隧道、錦屏二級水電站探硐的巖爆預測結(jié)果顯示粗糙集-理想點法模型預測結(jié)果正確,并且其預測精度高于層次分析-理想點法模型和等權(quán)重-理想點法模型。 (4)蒼嶺隧道、錦屏二級水電站探硐的巖爆預測結(jié)果顯示粗糙集-證據(jù)理論模型預測結(jié)果正確,并且其預測精度高于通過人為指定建立的另外兩組證據(jù)理論巖爆預測模型。 (5)粗糙集-理想點巖爆預測模型、粗糙集-證據(jù)理論巖爆預測模型和模糊數(shù)學巖爆預測模型三者總體預測水平相當,但粗糙集-理想點巖爆預測模型和粗糙集-證據(jù)理論模型更能反映巖爆發(fā)展的趨勢,認為兩者略優(yōu)于模糊數(shù)學模型。
[Abstract]:Rock burst is a kind of typical ground engineering geological disasters force conditions, causing a serious threat to the safety of construction personnel and equipment in underground engineering. With the rapid development of China's water conservancy, transportation and mining industry, high deep rock excavation force environment more and more, the prevention and control of rock burst will be more and more outstanding, become an important research topic in the field of deep underground engineering geological disaster prevention and control.
Rockburst prediction is an important part of prevention and control of rock burst. Accurate prediction of rockburst can help take relevant engineering measures in design and construction, reduce or avoid the occurrence of rock burst disaster losses. But because of the rock burst mechanism is complex, the rock burst prediction is very difficult. At present, in actual engineering, a method using simple the classification of rock burst prediction, because not considering the influence of various factors, which often results with the actual situation is quite different.
In view of the existing problems of rock burst prediction, this paper has carried out systematic research in the following aspects:
(1) taking the Cang Ling tunnel as an example, using the traditional strength theory method, the rock burst is systematically predicted and analyzed, and the problems existing in the existing methods are discussed.
(2) aiming at the defects of the traditional strength theory, considering the characteristics of rock burst, the particle swarm algorithm to optimize the generalized regression neural network, construct the prediction model of rock tunnel by blasting the objective, the model of Cangling hydropower station, Jinping two two deep underground engineering for rock burst prediction. The paper describes the characteristics and limitations of the method.
(3) considering that the data of rockburst analysis are continuous data, and the classification of rock burst is the characteristics of discrete data. Combined with field survey results and domestic and foreign engineering examples, the importance and objective quantitative evaluation of rock burst factors are made by rough set theory.
(4) based on the principle of multi-objective programming, combined with the results of rough set theory and the ideal point method, a prediction model of rock burst based on rough set and ideal point is constructed. The correctness and applicability of rockburst prediction of Cang Ling tunnel and Jinping two hydropower station is verified by its prediction.
(5) from the perspective of information fusion, combined with the analysis results and ideal points method of rough set theory, a prediction model of rock burst based on rough set and ideal point is constructed, and the above two engineering examples are used to validate the model.
(6) rough set ideal point model, rough set evidence theory model and fuzzy mathematics model are compared and analyzed to evaluate the advantages and disadvantages of various methods and prediction results.
Through the study of these contents, some of the following innovative achievements have been obtained.
(1) the prediction results show that the explosion in Cangling Tunnel Rock, compared with the common BP neural network and generalized regression neural network, generalized regression neural network model output particle swarm algorithm is stable, accurate prediction results, but the model error in the prediction of Jinping two hydropower station tunnel rock burst, that there are some limitations its applicability.
(2) the results of rough set theory show that the degree of stress concentration has the greatest impact on rock burst, and the influence of energy storage on rock mass is in the middle, and the brittle condition of rock mass is relatively small.
(3) the prediction results of rock burst in Cang Ling tunnel and Jinping two hydropower station show that the rough set ideal point method is correct, and the prediction accuracy is higher than the analytic hierarchy process ideal point method and the equal weight ideal point method.
(4) the prediction results of rock burst in Cang Ling tunnel and Jinping two level Hydropower Station show that the rough set evidence theory model is correct, and the prediction accuracy is higher than the other two sets of evidence theory based on artificial designation.
(5) rough set ideal prediction model of explosion point rock, rough set and evidence theory and fuzzy mathematics model of rockburst prediction of rock burst prediction model of the three overall prediction level, but the rough set ideal prediction model and rough set and evidence theory model can reflect the development tendency of rock burst rock burst, think two slightly better than the fuzzy mathematical model.

【學位授予單位】：浙江大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TU45;TU91

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