本文選題：地質(zhì)災(zāi)害　切入點(diǎn)：高地應(yīng)力　出處：《浙江大學(xué)》2014年博士論文　論文類型：學(xué)位論文

【摘要】：巖爆是高地應(yīng)力條件下的一種典型工程地質(zhì)災(zāi)害,給地下工程施工人員和設(shè)備安全造成嚴(yán)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(yīng)力環(huán)境中的深部巖體開挖越來越多,巖爆的預(yù)防與控制問題將越來越突出,成為深部地下工程地質(zhì)災(zāi)害防治領(lǐng)域的重要課題。 巖爆預(yù)測是巖爆防控的重要內(nèi)容。準(zhǔn)確的巖爆預(yù)測有助于在設(shè)計(jì)和施工中采取相應(yīng)的工程對(duì)策,減少或避免巖爆災(zāi)害帶來的損失。但由于巖爆機(jī)理復(fù)雜,使得巖爆預(yù)測十分困難。目前,工程實(shí)際中一般采用簡單分級(jí)的方法對(duì)巖爆進(jìn)行預(yù)測,由于不能考慮各種因素的綜合影響,其結(jié)果往往與實(shí)際情況出入較大。 針對(duì)目前巖爆預(yù)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(qiáng)度理論方法對(duì)其巖爆進(jìn)行了系統(tǒng)的預(yù)測分析,對(duì)已有方法存在的問題進(jìn)行了探討。 (2)針對(duì)傳統(tǒng)強(qiáng)度理論的缺陷,考慮巖爆的特點(diǎn),采用粒子群算法對(duì)廣義回歸神經(jīng)網(wǎng)絡(luò)進(jìn)行了優(yōu)化,構(gòu)建了客觀的巖爆預(yù)測模型,采用該模型對(duì)蒼嶺隧道、錦屏二級(jí)水電站兩個(gè)深埋地下工程進(jìn)行了巖爆預(yù)測,闡述該方法的特點(diǎn)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(jí)是離散數(shù)據(jù)的特點(diǎn),結(jié)合現(xiàn)場調(diào)查結(jié)果和國內(nèi)外工程實(shí)例,采用粗糙集理論對(duì)巖爆影響因素進(jìn)行了重要性區(qū)分和客觀定量評(píng)價(jià)。 (4)從多目標(biāo)規(guī)劃原理出發(fā),結(jié)合粗糙集理論分析成果和理想點(diǎn)方法,構(gòu)建了粗糙集-理想點(diǎn)巖爆預(yù)測模型,通過對(duì)蒼嶺隧道和錦屏二級(jí)水電站的巖爆預(yù)測驗(yàn)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(diǎn)方法,構(gòu)建了粗糙集-理想點(diǎn)巖爆預(yù)測模型,同樣通過上述兩個(gè)工程實(shí)例對(duì)模型進(jìn)行了驗(yàn)證。 (6)開展粗糙集-理想點(diǎn)法模型、粗糙集-證據(jù)理論模型和模糊數(shù)學(xué)方法模型的對(duì)比分析,評(píng)價(jià)各種方法的優(yōu)缺點(diǎn)和預(yù)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(yù)測結(jié)果顯示,與普通BP神經(jīng)網(wǎng)絡(luò)和普通廣義回歸神經(jīng)網(wǎng)絡(luò)相比,粒子群算法-廣義回歸神經(jīng)網(wǎng)絡(luò)模型輸出結(jié)果穩(wěn)定,預(yù)測結(jié)果準(zhǔn)確,但該模型在預(yù)測錦屏二級(jí)水電站探洞巖爆時(shí)出現(xiàn)錯(cuò)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(yīng)力集中程度對(duì)巖爆影響最大,巖體的儲(chǔ)能情況影響居中,巖體的脆性條件影響相對(duì)較小。 (3)蒼嶺隧道、錦屏二級(jí)水電站探硐的巖爆預(yù)測結(jié)果顯示粗糙集-理想點(diǎn)法模型預(yù)測結(jié)果正確,并且其預(yù)測精度高于層次分析-理想點(diǎn)法模型和等權(quán)重-理想點(diǎn)法模型。 (4)蒼嶺隧道、錦屏二級(jí)水電站探硐的巖爆預(yù)測結(jié)果顯示粗糙集-證據(jù)理論模型預(yù)測結(jié)果正確,并且其預(yù)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(yù)測模型。 (5)粗糙集-理想點(diǎn)巖爆預(yù)測模型、粗糙集-證據(jù)理論巖爆預(yù)測模型和模糊數(shù)學(xué)巖爆預(yù)測模型三者總體預(yù)測水平相當(dāng),但粗糙集-理想點(diǎn)巖爆預(yù)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(rèn)為兩者略優(yōu)于模糊數(shù)學(xué)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(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TU45;TU91

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 朱茵,孟志勇,闞叔愚;用層次分析法計(jì)算權(quán)重[J];北方交通大學(xué)學(xué)報(bào);1999年05期

2 劉朝安;高文龍;闕金聲;陳劍平;;基于熵值-理想點(diǎn)法的泥石流危險(xiǎn)度研究[J];吉林大學(xué)學(xué)報(bào)(地球科學(xué)版);2011年S1期

3 佘躍心;用神經(jīng)網(wǎng)絡(luò)殘余Kriging預(yù)測場地液化勢[J];成都理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2005年04期

4 馮夏庭,趙洪波;巖爆預(yù)測的支持向量機(jī)[J];東北大學(xué)學(xué)報(bào);2002年01期

5 丁學(xué)仁,吳長江;福建及其沿海地區(qū)中強(qiáng)以上地震的震源機(jī)制研究[J];地殼形變與地震;1999年01期

6 王連捷;崔軍文;張曉衛(wèi);唐哲民;李朋武;李雙林;;中國大陸科學(xué)鉆主孔現(xiàn)今地應(yīng)力狀態(tài)[J];地球科學(xué);2006年04期

7 吳其斌;微重力方法在巖爆預(yù)測中的應(yīng)用[J];地球物理學(xué)進(jìn)展;1993年03期

8 張倬元,宋建波,李攀峰;地下廠房洞室群巖爆趨勢綜合預(yù)測方法[J];地球科學(xué)進(jìn)展;2004年03期

9 郭延華;姜福興;張常光;;高地應(yīng)力下圓形巷道臨界沖擊地壓解析解[J];工程力學(xué);2011年02期

10 何雄輝;肖紅飛;;基于麥克斯韋方程的礦山巖爆現(xiàn)象研究[J];物理與工程;2007年03期

，

本文編號(hào)：1639979