本文關(guān)鍵詞： 公路隧道 新奧法 風(fēng)險(xiǎn)管理 模糊理論 BP神經(jīng)網(wǎng)絡(luò)　出處：《青島理工大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：公路隧道是公路網(wǎng)中不可或缺的重要組成部分,已成為公路工程建設(shè)中重點(diǎn)管控對(duì)象之一。其技術(shù)含量高、成本集中密、安全隱患多等特性,決定了在隧道項(xiàng)目建設(shè)中,必須詳細(xì)的分析其各階段、各過(guò)程的致險(xiǎn)因子,通過(guò)及時(shí)有效的經(jīng)濟(jì)技術(shù)等手段,才能保證它的有效投資及質(zhì)量安全。新奧法在我國(guó)公路隧道建設(shè)過(guò)程中,經(jīng)過(guò)不斷的發(fā)展和完善,目前已成為我國(guó)公路、鐵路山嶺隧道施工建設(shè)的主要方法之一。雖然現(xiàn)階段國(guó)內(nèi)外有許多專(zhuān)業(yè)人士、學(xué)者對(duì)新奧法施工中的風(fēng)險(xiǎn)管理進(jìn)行了不同側(cè)重的研究,但大都集中在較籠統(tǒng)的對(duì)隧道全長(zhǎng)范圍內(nèi)的風(fēng)險(xiǎn)進(jìn)行評(píng)價(jià),忽視了復(fù)雜地質(zhì)段中施工致險(xiǎn)因素非線性變化的事實(shí)。本文立足福建某高速公路中的ZY隧道左洞為例,依據(jù)AHP-Fuzzy-BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)模型對(duì)隧道全長(zhǎng)、全過(guò)程范圍內(nèi)的風(fēng)險(xiǎn)進(jìn)行“分-總”式的評(píng)價(jià)。主要研究?jī)?nèi)容如下:(1)結(jié)合國(guó)內(nèi)外對(duì)于新奧法隧道施工技術(shù)的實(shí)踐經(jīng)驗(yàn)以及相關(guān)學(xué)者的研究文獻(xiàn),本文采用因果分析圖法(魚(yú)刺圖)對(duì)隧道施工全過(guò)程的危險(xiǎn)因素進(jìn)行識(shí)別。(2)對(duì)識(shí)別出的致險(xiǎn)因素歸納總結(jié)、分類(lèi),建立風(fēng)險(xiǎn)評(píng)價(jià)指標(biāo)體系,利用層次分析法(AHP)確定系統(tǒng)指標(biāo)權(quán)重。(3)運(yùn)用模糊理論(Fuzzy)中的主觀理想點(diǎn)法,將隧道施工全過(guò)程中的定性、定量指標(biāo)參考化,得出此風(fēng)險(xiǎn)評(píng)價(jià)指標(biāo)體系的相應(yīng)標(biāo)準(zhǔn)參考值表。并通過(guò)線性?xún)?nèi)插足夠的數(shù)據(jù)作為BP神經(jīng)網(wǎng)絡(luò)的輸入訓(xùn)練數(shù)據(jù)向量,將對(duì)應(yīng)的AHP-Fuzzy系統(tǒng)得出的模糊綜合評(píng)價(jià)值作為輸出向量訓(xùn)練數(shù)據(jù),通過(guò)訓(xùn)練神經(jīng)網(wǎng)絡(luò)并檢驗(yàn)就建立起了AHP-Fuzzy-BP神經(jīng)網(wǎng)絡(luò)評(píng)價(jià)模型。(4)以ZY隧道左洞為例,將隧道按照不同地質(zhì)構(gòu)造或主、客觀施工條件分為若干“單元段”,運(yùn)用建立的AHP-Fuzzy-BP評(píng)價(jià)模型,將各單元段對(duì)應(yīng)風(fēng)險(xiǎn)指標(biāo)值作為BP神經(jīng)網(wǎng)絡(luò)測(cè)試輸入向量,可得出各“單元段”的風(fēng)險(xiǎn)評(píng)價(jià)結(jié)果。通過(guò)與隧道風(fēng)險(xiǎn)分析報(bào)告對(duì)比證明該模型在隧道施工風(fēng)險(xiǎn)評(píng)價(jià)中的正確性與適用性。(5)對(duì)上述評(píng)價(jià)結(jié)果進(jìn)行分析,列出在公路隧道新奧法施工全過(guò)程中主要致險(xiǎn)因子的應(yīng)對(duì)措施及控制方法,為今后的新奧法隧道施工風(fēng)險(xiǎn)管理提供一定的指導(dǎo)與借鑒。
[Abstract]:Highway tunnel is an indispensable part of highway network, and has become one of the key control objects in highway engineering construction. In order to ensure its effective investment and quality safety, it is necessary to analyze the risk factors of each stage and process in detail, and to ensure its effective investment and quality safety by means of timely and effective economic and technological means. Through continuous development and improvement, it has now become one of the main methods for the construction of highway and railway mountain tunnels in China. Although there are many professionals at home and abroad at the present stage, Scholars have carried out different studies on risk management in the construction of the New Olympic method, but most of them are focused on the evaluation of the risk in the full-length scope of the tunnel. The nonlinear variation of construction risk factors in complex geological section is ignored. Based on the example of the left tunnel of ZY tunnel in a highway in Fujian Province, the length of the tunnel is analyzed according to the structural model of AHP-Fuzzy-BP neural network. The main research contents are as follows: (1) combining the domestic and foreign practical experience on the construction technology of the New Austrian tunneling and the research literature of relevant scholars, In this paper, the causality analysis diagram (fishthorn chart) is used to identify the risk factors in the whole process of tunnel construction. The risk factors identified are summarized, classified, and the risk evaluation index system is established. Using the analytic hierarchy process (AHP) to determine the index weight of the system, the subjective ideal point method in fuzzy theory is used to refer the qualitative and quantitative indexes in the whole process of tunnel construction. The corresponding standard reference value table of the risk evaluation index system is obtained, and the input training data vector of BP neural network is obtained by linear interpolation enough data. Taking the fuzzy comprehensive evaluation value obtained from the corresponding AHP-Fuzzy system as the training data of the output vector, the evaluation model of AHP-Fuzzy-BP neural network is established by training and testing the neural network. (4) taking the left hole of the ZY tunnel as an example, the tunnel is divided into different geological structures or main structures. Objective construction conditions are divided into several "unit segments". Using the established AHP-Fuzzy-BP evaluation model, the corresponding risk index values of each unit segment are used as the BP neural network test input vector. The risk evaluation results of each "unit section" can be obtained. The correctness and applicability of the model in tunnel construction risk evaluation are proved by comparing with the tunnel risk analysis report. This paper lists the countermeasures and control methods of the main risk factors in the whole course of the construction of the road tunnel, and provides some guidance and reference for the construction risk management of the tunnel in the future.
【學(xué)位授予單位】：青島理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：U455.48

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