本文選題：BP神經(jīng)網(wǎng)絡(luò) + 巖質(zhì)邊坡　； 參考：《南昌航空大學(xué)》2017年碩士論文

【摘要】：本文針對公路巖質(zhì)邊坡穩(wěn)定性問題,利用BP人工神經(jīng)網(wǎng)絡(luò)建立巖質(zhì)邊坡穩(wěn)定坡角預(yù)測模型,對穩(wěn)定邊坡角進(jìn)行預(yù)測。同時結(jié)合RMR巖體質(zhì)量分級系統(tǒng),對巖體穩(wěn)定性進(jìn)行分級評價。通過調(diào)研,收集大量已建公路邊坡工程的工程數(shù)據(jù),以其為訓(xùn)練樣本建立BP人工神經(jīng)網(wǎng)絡(luò)模型。該模型的輸入層影響因子采用RMR巖體質(zhì)量分級系統(tǒng)的參數(shù),并根據(jù)參數(shù)的評分標(biāo)準(zhǔn)來量化邊坡工程勘測數(shù)據(jù)。對于存在多組節(jié)理的巖質(zhì)邊坡,將初始參數(shù)值分別進(jìn)行算術(shù)平均調(diào)整,加權(quán)平均調(diào)整以及考慮楔形破壞加權(quán)平均調(diào)整,將調(diào)整后的參數(shù)值分別輸入到BP神經(jīng)網(wǎng)絡(luò)模型中預(yù)測穩(wěn)定邊坡角,并將預(yù)測值與已建公路的邊坡角實際值進(jìn)行對比分析。如果出現(xiàn)邊坡角預(yù)測值大于實際值的情況,那么無法直接根據(jù)對比結(jié)果判斷邊坡是否穩(wěn)定,此時利用有限元軟件對預(yù)測值進(jìn)行建模分析,驗證預(yù)測值的可靠性。本研究取得的主要研究成果如下:(1)對RMR巖體質(zhì)量分級系統(tǒng)參數(shù)值進(jìn)行考慮楔形破壞加權(quán)平均調(diào)整,得到的巖體質(zhì)量分級評價結(jié)果與已建工程實際情況最接近。(2)將BP神經(jīng)網(wǎng)絡(luò)預(yù)測結(jié)果與已建工程實際數(shù)據(jù)對比,結(jié)果表明利用BP人工神經(jīng)網(wǎng)絡(luò)方法預(yù)測公路巖質(zhì)邊坡穩(wěn)定坡角是可行的。(3)有限元軟件分析表明,利用BP神經(jīng)網(wǎng)絡(luò)預(yù)測得到的結(jié)果均是正確的,驗證了BP神經(jīng)網(wǎng)絡(luò)預(yù)測穩(wěn)定邊坡角的可靠性。(4)對RMR巖體質(zhì)量分級系統(tǒng)參數(shù)值進(jìn)行考慮楔形破壞加權(quán)平均調(diào)整,經(jīng)過BP神經(jīng)網(wǎng)絡(luò)預(yù)測得到的穩(wěn)定邊坡角預(yù)測值與已建邊坡工程邊坡角實際值最接近。
[Abstract]:Aiming at the stability of rock slope of highway, a prediction model of stable slope angle of rock slope is established by using BP artificial neural network, and the angle of stable slope is predicted. At the same time, the stability of rock mass is evaluated by RMR classification system. Through investigation and investigation, a large number of engineering data of highway slope engineering have been collected, and BP artificial neural network model has been established with it as the training sample. The parameters of RMR rock mass quality classification system are used for the influence factors of the input layer of the model, and the slope engineering survey data are quantified according to the scoring criteria of the parameters. For rock slopes with multiple joints, the initial parameter values are adjusted respectively by arithmetic average, weighted average adjustment and weighted average adjustment considering wedge failure. The adjusted parameters are input into the BP neural network model to predict the stable slope angle, and the predicted value is compared with the actual value of the slope angle of the built highway. If the prediction value of slope angle is larger than the actual value, it is impossible to judge whether the slope is stable or not directly based on the comparison results. At this time, the finite element software is used to model and analyze the predicted value to verify the reliability of the prediction value. The main research results obtained in this study are as follows: (1) adjusting the RMR rock mass quality classification system parameters considering the wedge failure weighted average. The obtained rock mass quality grading evaluation results are closest to the actual situation of the existing projects.) the BP neural network prediction results are compared with the actual data of the existing projects. The results show that it is feasible to predict the stable slope angle of highway rock slope by BP artificial neural network method. The finite element software analysis shows that the results obtained by BP neural network prediction are all correct. The reliability of BP neural network for predicting stable slope angle is verified. The predicted value of the stable slope angle obtained by BP neural network is the closest to the actual value of the slope angle of the existing slope engineering.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U416.14

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