【摘要】：本文通過對現(xiàn)有國內(nèi)外隧道工程監(jiān)測、圍巖參數(shù)反分析、隧道時間序列預測的研究和總結(jié)的基礎(chǔ)上,重點研究并完成了以下內(nèi)容：(1)提出高斯過程-差異進化協(xié)同優(yōu)化算法(GP-DE),開發(fā)基于matlab的GP-DE程序,發(fā)揮高斯過程對非線性映射關(guān)系優(yōu)良的處理能力,利用差異進化算法優(yōu)化GP-DE包含的超參數(shù),有效提高非線性映射關(guān)系模型的精度,為地下工程巖體參數(shù)智能優(yōu)化反分析及巖體變化時間序列預測提供一種新方法。(2)結(jié)合大連地鐵實際工程,在工程現(xiàn)場布置多元信息自動化監(jiān)測系統(tǒng),以獲得更加及時、豐富、準確的圍巖變化信息,并對所采集信息進行了綜合分析。另設(shè)計正交試驗方案進行數(shù)值計算,對圍巖參數(shù)進行敏感性分析,分析各圍巖參數(shù)對控制變量的影響。利用現(xiàn)場監(jiān)測所得圍巖位移應力變化信息作為控制值,進行圍巖參數(shù)GP-DE位移應力聯(lián)合反分析,并與圍巖參數(shù)DE位移反分析作對比,驗證方法的優(yōu)越性。(3)結(jié)合陳家店山嶺隧道實際工程,考慮加入滲流作用,通過數(shù)值計算、圍巖參數(shù)敏感性分析及圍巖參數(shù)GP-DE反分析,獲得現(xiàn)場圍巖參數(shù),并通過數(shù)值計算,將模擬結(jié)果與實際對比,對反分析結(jié)果進行驗證。另利用反分析參數(shù)進行模擬,對不同工法、有無滲流作用的施工結(jié)果作對比。(4)利用GP-DE算法對大連地鐵隧道拱頂沉降時間序列進行預測，，通過主成分分析法優(yōu)化了訓練樣本,實現(xiàn)了隧道拱頂沉降值和監(jiān)測斷面與掌子面距離的二變量時間序列預測,另外比較不同樣本構(gòu)成方法、單一變量時間序列與多變量時間序列的預測效果。
[Abstract]:Based on the research and summary of existing tunnel engineering monitoring, back analysis of surrounding rock parameters and prediction of tunnel time series at home and abroad, The main contents are as follows: (1) the Gao Si process-differential evolution collaborative optimization algorithm (GP-DE) is proposed, and the GP-DE program based on matlab is developed. The Gao Si process has good processing ability to the nonlinear mapping relationship. In order to improve the precision of nonlinear mapping relation model, differential evolution algorithm is used to optimize the superparameters contained in GP-DE. This paper provides a new method for intelligent optimization of rock mass parameters and prediction of time series of rock mass change in underground engineering. (2) combined with the actual project of Dalian Metro, multi-information automatic monitoring system is arranged in the project site to get more timely. Rich and accurate surrounding rock change information, and comprehensive analysis of the collected information. In addition, the orthogonal test scheme is designed for numerical calculation, the sensitivity analysis of surrounding rock parameters is carried out, and the influence of surrounding rock parameters on the control variables is analyzed. Using the displacement stress change information obtained from field monitoring as the control value, the joint back analysis of surrounding rock parameter GP-DE displacement stress is carried out and compared with the back analysis of surrounding rock parameter DE displacement. The advantages of the method are verified. (3) combined with the actual project of Chenjiadianshan Tunnel, the surrounding rock parameters are obtained by numerical calculation, sensitivity analysis of surrounding rock parameters and GP-DE back analysis of surrounding rock parameters. Through numerical calculation, the simulation results are compared with the actual ones, and the inverse analysis results are verified. In addition, the inverse analysis parameters are used to simulate and compare the construction results of different construction methods with or without seepage. (4) the GP-DE algorithm is used to predict the settlement time series of the arch roof of Dalian metro tunnel. The training samples are optimized by principal component analysis, and the two-variable time series prediction of the settlement value of tunnel arch roof and the distance between the monitoring section and the palm surface is realized. In addition, different sample composition methods are compared. Prediction effect of single variable time series and multivariable time series.
【學位授予單位】：大連海事大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U455

【引證文獻】

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1 韓敏;范明明;劉玉花;席劍輝;;改進的神經(jīng)網(wǎng)絡預測多變量非線性時間序列[A];第二十四屆中國控制會議論文集（下冊）[C];2005年

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