本文關(guān)鍵詞：基于BP神經(jīng)網(wǎng)絡(luò)下的深長隧道突水預(yù)測分析與數(shù)值模擬研究　出處：《中南大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 深長隧道 BP神經(jīng)網(wǎng)絡(luò) 涌突水 數(shù)值模擬

【摘要】：隧道突水突泥一直以來都是隧道施工過程中的重大難題,突水突泥可能會導(dǎo)致隧道工期延誤,造成巨大的財產(chǎn)損失,甚至還可能成為隧道塌方的誘因,導(dǎo)致人員傷亡,施工機械損壞,對整個社會造成不利的影響。過往的研究表明,地質(zhì)構(gòu)造、地下水源和開挖擾動成為深長隧道突水突泥災(zāi)害發(fā)生的三大原因。為了保障隧道施工的安全性,減少不必要的財產(chǎn)損失,加強對隧道突水突泥前兆信息的研究是十分必要的。在隧道施工過程中,綜合分析隧道突水突泥災(zāi)害誘因,預(yù)測突水突泥發(fā)生的可能性,采取針對性的控制措施,從而保障隧道的順利施工和人員的安全。 本文以江西鐘家山隧道為依托工程,通過收集工程中突水突泥災(zāi)害發(fā)生時的現(xiàn)場數(shù)據(jù),運用BP神經(jīng)網(wǎng)絡(luò)對深長隧道的突水突泥前兆信息進行預(yù)測,并采用數(shù)值模擬的方法對隧道突水過程進行了分析,論文主要完成了以下幾方面的工作： (1)根據(jù)國內(nèi)外有關(guān)深長隧道突水突泥涌水量預(yù)測的研究,探討了深長隧道涌水預(yù)測過程中的影響因素,結(jié)合鐘家山隧道現(xiàn)場的施工情況,確定了涌水量預(yù)測的六個變量參數(shù)。 (2)依據(jù)鐘家山隧道施工過程中采集的數(shù)據(jù),利用BP神經(jīng)網(wǎng)絡(luò)建立突水突泥的災(zāi)害預(yù)測模型。通過對現(xiàn)場觀測點涌水量的預(yù)測,驗證了該預(yù)測模型的有效性和正確性。 (3)運用MIDAS和FLAC3D數(shù)值模擬軟件對深長隧道的突水情況進行數(shù)值模擬分析,研究了隧道突水前兆信息的演化規(guī)律,分析了隧道開挖過程中滲流速度、滲透水壓力、孔隙水壓力的變化情況。
[Abstract]:Water and mud inrush has always been a major problem in tunnel construction, which may lead to the delay of tunnel construction period, cause huge property losses, and even may become the inducement of tunnel collapse. Causes casualties, construction machinery damage, to the entire society has a negative impact. Previous studies show that the geological structure. The underground water source and excavation disturbance are the three main causes of water inrush and mud inrush in deep tunnel. In order to ensure the safety of tunnel construction and reduce unnecessary property losses. It is necessary to strengthen the study on the precursor information of water and mud inrush in tunnel. In the process of tunnel construction, the inducement of water and mud inrush in tunnel is analyzed synthetically, and the possibility of water inrush is predicted. Take targeted control measures to ensure the smooth construction of the tunnel and the safety of personnel. Based on the project of Zhongjiashan Tunnel in Jiangxi Province, this paper uses BP neural network to predict the precursor information of water inrush mud in deep long tunnel by collecting the field data of water inrush and mud inrush disaster. The water inrush process of tunnel is analyzed by numerical simulation. The main work of this paper is as follows: 1) based on the domestic and foreign research on the prediction of water inrush and mud inrush from deep long tunnel, the influencing factors in the process of water inrush prediction in deep long tunnel are discussed, combined with the construction situation of Zhongjiashan tunnel site. Six variable parameters of water inflow prediction are determined. 2) based on the data collected during the construction of Zhongjiashan Tunnel, a disaster prediction model of water inrush and mud inrush is established by using BP neural network. The validity and correctness of the prediction model are verified. 3) using MIDAS and FLAC3D software to simulate the water inrush of the deep tunnel, and study the evolution rule of the precursor information of water inrush in the tunnel. The variation of seepage velocity, seepage water pressure and pore water pressure during tunnel excavation are analyzed.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U456.3

【參考文獻】

相關(guān)期刊論文 前10條

1 仵彥卿;地下水與地質(zhì)災(zāi)害[J];地下空間;1999年04期

2 楊小禮;王金明;眭志榮;;基于Hoek-Brown屈服準則的隧道圍巖穩(wěn)定性分析[J];鐵道科學(xué)與工程學(xué)報;2008年05期

3 陳建宏;蔣權(quán);李賢云;陸玉根;;基于Visual Modflow和灰色理論的鋁土礦礦井涌水量預(yù)測[J];礦業(yè)工程研究;2011年03期

4 譚永紅;基于BP神經(jīng)網(wǎng)絡(luò)的自適應(yīng)控制[J];控制理論與應(yīng)用;1994年01期

5 宋桂軍;梁冰;王俊光;李剛;;長溝峪煤礦礦井涌水量預(yù)測研究[J];煤炭技術(shù);2008年11期

6 楊新安,黃宏偉,丁全錄;FLAC程序及其在隧道工程中的應(yīng)用[J];上海鐵道大學(xué)學(xué)報;1996年04期

7 戚德虎,康繼昌;BP神經(jīng)網(wǎng)絡(luò)的設(shè)計[J];計算機工程與設(shè)計;1998年02期

8 夏永旭;;現(xiàn)代公路隧道發(fā)展概述[J];交通建設(shè)與管理;2006年12期

9 王建秀,楊立中,何靜;大型地下工程巖溶涌(突)水模式的水文地質(zhì)分析及其工程應(yīng)用[J];水文地質(zhì)工程地質(zhì);2001年04期

10 姬永紅,項彥勇;水底隧道涌水量預(yù)測方法的應(yīng)用分析[J];水文地質(zhì)工程地質(zhì);2005年04期

相關(guān)博士學(xué)位論文 前1條

1 劉斌;基于電阻率法與激電法的隧道含水地質(zhì)構(gòu)造超前探測與突水災(zāi)害實時監(jiān)測研究[D];山東大學(xué);2010年

，

本文編號：1423816