發(fā)布時(shí)間：2018-07-11 11:14

  本文選題：變形監(jiān)測(cè) + 沉降監(jiān)測(cè)��； 參考：《寧夏大學(xué)》2015年碩士論文

【摘要】：建筑變形監(jiān)測(cè)及其數(shù)據(jù)分析處理與預(yù)報(bào)對(duì)于工程建設(shè)的安全非常重要。通過(guò)查閱、學(xué)習(xí)建筑變形監(jiān)測(cè)技術(shù)及其數(shù)據(jù)分析處理與預(yù)報(bào)相關(guān)的文獻(xiàn)資料,首先對(duì)深基坑和建筑物變形監(jiān)測(cè)的意義和目的、內(nèi)容及方法,以及監(jiān)測(cè)方案技術(shù)設(shè)計(jì)的主要內(nèi)容進(jìn)行歸納。然后對(duì)幾類常用的變形預(yù)報(bào)模型進(jìn)行理論描述。最后結(jié)合4個(gè)參與完成的建筑工程沉降監(jiān)測(cè)案例數(shù)據(jù),分別運(yùn)用MATLAB曲線擬合模型、神經(jīng)網(wǎng)絡(luò)模型、時(shí)間序列模型和灰色預(yù)報(bào)模型進(jìn)行預(yù)報(bào),并與實(shí)際監(jiān)測(cè)數(shù)據(jù)對(duì)比分析,得出以下結(jié)論：(1)曲線擬合模型中,三次樣條插值模型(cubic spline)具有一定的預(yù)報(bào)效果,當(dāng)沉降量較大或沉降速度較快時(shí),可以與其它模型聯(lián)合預(yù)報(bào)。三次埃爾米特插值模型(shape-preserving)和多項(xiàng)式模型(Polynomial),不適于沉降監(jiān)測(cè)預(yù)報(bào)。(2)神經(jīng)網(wǎng)絡(luò)模型中,GR神經(jīng)網(wǎng)絡(luò)模型預(yù)報(bào)效果好,BP神經(jīng)網(wǎng)絡(luò)模型和RBF神經(jīng)網(wǎng)絡(luò)模型預(yù)報(bào)效果較好。BP神經(jīng)網(wǎng)絡(luò)模型預(yù)報(bào)精度比RBF神經(jīng)網(wǎng)絡(luò)模型稍高,兩者可與GR神經(jīng)網(wǎng)絡(luò)模型聯(lián)合預(yù)報(bào)。(3)基于具有隨機(jī)性的實(shí)測(cè)數(shù)據(jù),運(yùn)用時(shí)間序列模型進(jìn)行預(yù)報(bào),其精度比神經(jīng)網(wǎng)絡(luò)模型高。缺點(diǎn)是模型建立過(guò)程和程序編制較為復(fù)雜。(4)當(dāng)有少量實(shí)測(cè)數(shù)據(jù)時(shí),灰色預(yù)報(bào)模型也可實(shí)現(xiàn)較高精度的預(yù)報(bào)。但是如果數(shù)據(jù)存在噪音,則預(yù)報(bào)精度會(huì)受到影響,甚至達(dá)不到合格要求。
[Abstract]:Deformation monitoring, data analysis and prediction are very important for the safety of engineering construction. Through consulting and studying the building deformation monitoring technology and its data analysis and processing related to the literature data forecast, first of all, the significance and purpose, contents and methods of deep foundation pit and building deformation monitoring are studied. And the main contents of the technical design of the monitoring scheme are summarized. Then several kinds of commonly used deformation prediction models are described theoretically. Finally, combining with the data of four cases of building engineering settlement monitoring, MATLAB curve fitting model, neural network model, time series model and grey forecast model are used to forecast, and the results are compared with the actual monitoring data. The conclusions are as follows: (1) in the curve fitting model, the cubic spline interpolation model (cubic spline) has a certain prediction effect. When the settlement is large or the settlement velocity is fast, it can be combined with other models to forecast. The cubic Hermitian interpolation model (shape-preserving) and the polynomial model (Polynomial) are not suitable for subsidence monitoring and forecasting. (2) in the neural network model, the prediction effect of the GR neural network model is better than that of the BP neural network model and the RBF neural network model, and the prediction effect of the BP neural network model is better than that of the BP neural network model. The prediction accuracy of the network model is slightly higher than that of the RBF neural network model. The two models can be combined with the gr neural network model. (3) based on the measured data with randomness, the time series model is used to forecast, and the accuracy is higher than that of the neural network model. The disadvantage is that the process of model establishment and programming are more complicated. (4) when there are a small number of measured data, the grey prediction model can also achieve a higher precision prediction. However, if the data is noisy, the prediction accuracy will be affected, or even meet the requirements.
【學(xué)位授予單位】：寧夏大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TU433

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本文編號(hào)：2114988