本文選題：大壩監(jiān)測(cè)位移 + 灰色因果模型��； 參考：《合肥工業(yè)大學(xué)》2014年碩士論文

【摘要】：位移是大壩安全狀態(tài)的重要指標(biāo)，大壩位移監(jiān)測(cè)資料的分析工作關(guān)系到大壩和壩區(qū)人們的生命財(cái)產(chǎn)安全，因此，加強(qiáng)對(duì)大壩的位移監(jiān)測(cè)是保障大壩安全的一項(xiàng)意義重大的工作。 大壩監(jiān)測(cè)位移通常受到水壓、溫度、時(shí)效等因素的影響，而且各因素之間相互制約、相互影響，使得大壩監(jiān)測(cè)位移系統(tǒng)成為非線性系統(tǒng)。為了準(zhǔn)確的分析大壩位移監(jiān)測(cè)資料，進(jìn)行實(shí)時(shí)預(yù)報(bào)，本文針對(duì)大壩位移監(jiān)測(cè)的特點(diǎn)并對(duì)位移監(jiān)測(cè)資料和監(jiān)測(cè)方法分析的基礎(chǔ)上，提出以改進(jìn)灰色因果時(shí)序組合模型對(duì)大壩位移監(jiān)測(cè)資料進(jìn)行分析和研究。 考慮到大壩受到多因素的影響，結(jié)合灰色因果模型的應(yīng)用特點(diǎn)，本文通過(guò)建立灰色因果模型對(duì)大壩位移監(jiān)測(cè)資料進(jìn)行建模分析。灰色因果模型建模的主要工作是對(duì)因子的選擇和模型程序的編譯，通過(guò)選取合理的因子，對(duì)大壩位移監(jiān)測(cè)資料建立灰色因果模型。模型精度是評(píng)判模型優(yōu)劣的一個(gè)標(biāo)準(zhǔn)，在運(yùn)用灰色因果模型時(shí)，，為了提高擬合和預(yù)測(cè)精度，本文將基于Simpson公式改進(jìn)GM(1，N)模型，并將改進(jìn)的灰色因果模型應(yīng)用在大壩監(jiān)測(cè)位移中，通過(guò)分析比較傳統(tǒng)灰色因果模型和改進(jìn)后的模型擬合及預(yù)測(cè)效果。 對(duì)于建立的改進(jìn)GM(1，N)模型所產(chǎn)生的擬合殘差，由于殘差序列具有隨機(jī)性和動(dòng)態(tài)性，而時(shí)間序列分析是處理這類(lèi)數(shù)據(jù)的有效方法，所以文中對(duì)殘差序列建立ARMA模型�；谏鲜龇治�，本文以改進(jìn)后的灰色因果模型和ARMA模型建立改進(jìn)灰色因果時(shí)序組合模型對(duì)大壩位移監(jiān)測(cè)資料進(jìn)行建模分析。 本文是以某拱壩位移監(jiān)測(cè)資料為基礎(chǔ)建立實(shí)例，并通過(guò)監(jiān)測(cè)資料的分析對(duì)模型的效果進(jìn)行比較分析。研究表明，本文所采用的Simpson改進(jìn)方法，具有一定的適用性，改進(jìn)后的方法在工程實(shí)例中取得了良好的效果。其次，建立的組合模型預(yù)測(cè)結(jié)果相對(duì)于改進(jìn)后的灰色因果模型，又有進(jìn)一步的提高，因此組合模型能更準(zhǔn)確的反映大壩的位移情況，為大壩位移監(jiān)測(cè)資料的應(yīng)用研究和保障大壩安全運(yùn)行提供了有效工具。
[Abstract]:Displacement is an important indicator of dam safety. The analysis of dam displacement monitoring data is related to the safety of people's lives and property in dam and dam area. Therefore, it is of great significance to strengthen the monitoring of dam displacement.Dam monitoring displacement is usually affected by water pressure, temperature, aging and other factors, and each factor restricts and affects each other, which makes the dam monitoring displacement system become a nonlinear system.In order to accurately analyze the dam displacement monitoring data and carry out real-time prediction, this paper analyzes the characteristics of dam displacement monitoring and the analysis of displacement monitoring data and monitoring methods.An improved grey causal time series combination model is proposed to analyze and study the dam displacement monitoring data.Considering that the dam is affected by many factors and combined with the application characteristics of the grey causality model, this paper establishes a grey causal model to model and analyze the dam displacement monitoring data.The main work of the grey causal model modeling is to select the factors and compile the model program. By selecting the reasonable factors, the grey causality model is established for the dam displacement monitoring data.The model precision is a criterion for evaluating the model. In order to improve the fitting and prediction accuracy of the grey causality model, this paper improves the GM1N) model based on the Simpson formula, and applies the improved grey causality model to the dam monitoring displacement.By analyzing and comparing the traditional grey causality model with the improved model fitting and forecasting effect.For the fitting residuals produced by the improved GM1N) model, because the residual sequence is random and dynamic, and time series analysis is an effective method to deal with this kind of data, the ARMA model of residual sequence is established in this paper.Based on the above analysis, the improved grey causality model and ARMA model are used to model and analyze the dam displacement monitoring data.Based on the monitoring data of the displacement of an arch dam, an example is established in this paper, and the effect of the model is compared and analyzed through the analysis of the monitoring data.The research shows that the improved Simpson method adopted in this paper has certain applicability, and the improved method has achieved good results in engineering examples.Secondly, compared with the improved grey causality model, the prediction results of the combined model are further improved, so the combined model can reflect the displacement of the dam more accurately.It provides an effective tool for the application research of dam displacement monitoring data and the guarantee of dam safe operation.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TV698.11

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