發(fā)布時間：2018-03-05 20:34

  本文選題：滑坡預(yù)測　切入點：神經(jīng)網(wǎng)絡(luò)預(yù)測　出處：《江西理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：滑坡災(zāi)害是全球三大自然災(zāi)害之一,邊坡失穩(wěn)所造成的滑坡災(zāi)害已f嚴(yán)重影響了人類日常的生活與生產(chǎn),滑坡預(yù)報預(yù)測是有效降低滑坡帶來損失的途徑,目前,邊坡穩(wěn)定性預(yù)測方法的研究還處于不斷的發(fā)展和完善中。本文針對現(xiàn)階段滑坡預(yù)報預(yù)測方法中對邊坡模糊性處理的不足,引入模糊數(shù)學(xué)的相關(guān)理論,提出了一種基于模糊聚類迭代理論的滑坡預(yù)測預(yù)報方法,主要研究內(nèi)容及成果包括以下幾個方面:(1)為了提高預(yù)測精度:首先設(shè)計了邊坡數(shù)據(jù)收集及預(yù)處理方案,在進行滑坡預(yù)報預(yù)測前,設(shè)計了三維邊坡數(shù)據(jù)收集方案,并引入小波降噪理論,對監(jiān)測數(shù)據(jù)進行降噪處理。其次,基于原始的模糊聚類迭代模型,引入灰色關(guān)聯(lián)分析理論,構(gòu)建了改進的模糊聚類迭代模型,避免了確定各影響因素權(quán)重時的主觀性。(2)基于改進的模糊聚類迭代模型,構(gòu)建了由各影響邊坡穩(wěn)定因素評價邊坡穩(wěn)定性狀態(tài)的方法,針對模糊聚類迭代模型無法預(yù)測邊坡未來狀態(tài)的特點,引入BP神經(jīng)網(wǎng)絡(luò)的相關(guān)理論,構(gòu)建了預(yù)測邊坡穩(wěn)定性的改進模糊聚類迭代和神經(jīng)網(wǎng)絡(luò)復(fù)合模型。(3)基于改進模糊聚類迭代和神經(jīng)網(wǎng)絡(luò)復(fù)合模型,實現(xiàn)了邊坡失穩(wěn)的預(yù)測,為了便于計算與分析,運用MATLAB對預(yù)測的實現(xiàn)過程進行了程序化設(shè)計。(4)將本文建立的邊坡失穩(wěn)預(yù)測方法和以位移量作為預(yù)測對象的預(yù)測方法運用于同一工程實例中,通過對比兩種方法的預(yù)測結(jié)果,證實了該方法的工程實用性。
[Abstract]:Landslide disaster is one of the three natural disasters in the world. Landslide disaster caused by slope instability has seriously affected human daily life and production. Landslide prediction and prediction is an effective way to reduce landslide losses. The research of slope stability prediction method is still in the process of continuous development and perfection. In this paper, the fuzzy mathematics theory is introduced to solve the problem of slope fuzziness treatment in the present landslide prediction method. A landslide prediction method based on fuzzy clustering iteration theory is proposed. The main research contents and results include the following aspects: in order to improve the prediction accuracy: firstly, a slope data collection and pretreatment scheme is designed. Before the landslide prediction and prediction, the 3D slope data collection scheme is designed, and the wavelet denoising theory is introduced to deal with the monitoring data. Secondly, based on the original fuzzy clustering iteration model, the grey correlation analysis theory is introduced. An improved fuzzy clustering iteration model is constructed to avoid subjectivity in determining the weight of each factor. Based on the improved fuzzy clustering iteration model, a method for evaluating slope stability state by influencing slope stability factors is constructed. In view of the fact that fuzzy clustering iteration model can not predict the future state of slope, the related theory of BP neural network is introduced. The improved fuzzy clustering iteration and neural network compound model for predicting slope stability are constructed. Based on the improved fuzzy clustering iteration and neural network composite model, the slope instability prediction is realized. In this paper, the program design of the process of prediction is carried out by using MATLAB. The prediction method of slope instability established in this paper and the prediction method with displacement as the prediction object are applied to the same engineering example. The prediction results of the two methods are compared by comparing the results of the two methods. The engineering practicability of this method is proved.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TU43

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本文編號：1571775