小波人工神經網絡在建筑沉降預測中的應用研究
發(fā)布時間：2018-09-07 19:34
【摘要】：隨著我國經濟的快速發(fā)展及城市化水平的不斷提高,城市可利用的土地資源正不斷減少,各類高層建筑正迅速崛起。由于樓層的增加,荷載的增加,其施工將給建筑物本身及周邊建筑群體帶來復雜的形變影響。其中,最常見的是導致其發(fā)生不均勻沉降,若沉降嚴重則將危及建筑物的安全。 變形監(jiān)測作為信息化施工的關鍵環(huán)節(jié),貫穿于建筑物設計期、施工期和運營期的整個過程,工程參建各方都對監(jiān)測工作和數據分析給予了極大的重視。近年來,為探索出一種快速有效的沉降預測的方法,許多學者從理論與實踐等多方面進行了大量的探索與研究,并取得了一定的成效,但也存在著許多的問題與不足。本文根據建筑地基沉降的特點,以及目前在該領域所廣泛研討的熱點方法,將具有自學習、自組織且非線性逼近能力較好的人工神經網絡模型納入建筑沉降的預測中來,以BP神經網絡為基礎,并利用小波分析等方法對傳統(tǒng)的網絡模型進行了優(yōu)化改進。通過實例工程的變形預測對傳統(tǒng)網絡模型與改進模型進行了分析與研究,并對其預測效果進行了評價,結果比較理想。從而表明小波分析與神經網絡模型結合在建筑沉降預測中是可行的,且具有廣闊的工程應用價值。本文主要從以下幾個方面作了研究： (1)研究了BP神經網絡算法。對單一的BP神經網絡模型算法的局限性進行分析,針對傳統(tǒng)網絡模型存在的問題,對其進行了優(yōu)化改進,較好克服了易形成局部極小而得不到全局最優(yōu)、訓學習效率低、收斂速度慢等問題,并將改進模型應用于變形預測。 (2)對小波分析進行研究。結合MATLAB軟件探討了小波分析在信號去噪領域中的應用,研究了利用小波分析實現信號去噪的方法,以及小波函數選取、閾值選取和小波分解、重構等問題,合理地運用小波分析對變形監(jiān)測數據進行去噪預處理,以求預測結果更加準確。 (3)探討了小波分析和神經網絡模型的結合方式。二者的結合通常有兩種式：一種是輔助式結合,也稱為松散型結合方式；另一種是嵌入式結合,也即緊致型結合方式。 (4)以BP神經網絡模型為基礎,借助MATLAB,將改進的BP神經網絡、輔助式小波神經網絡和嵌入式小波神經網絡模型應用于實際工程的沉降預測當中,通過和實測值的對比,分析比較三種模型的整體性能。結果表明,后兩種小波神經網絡的組合模型精度大體相當,預測效果明顯優(yōu)于單一的BP神經網絡模型。最后對本文的不足之處作了簡要的說明。
[Abstract]:With the rapid development of economy and the improvement of urbanization level, the available land resources in cities are decreasing, and various kinds of high-rise buildings are rising rapidly. Because of the increase of floor and the increase of load, the construction will bring complex deformation effect to the building itself and the surrounding buildings. Among them, the most common is to cause uneven settlement, if the settlement will endanger the safety of the building. Deformation monitoring, as a key link of information construction, runs through the whole process of building design period, construction period and operation period. All parties involved in the project pay great attention to monitoring work and data analysis. In recent years, in order to explore a rapid and effective method of settlement prediction, many scholars have made a great deal of exploration and research in theory and practice, and achieved certain results, but there are also many problems and shortcomings. In this paper, according to the characteristics of building foundation settlement and the hot methods which are widely studied in this field, the artificial neural network model with self-learning, self-organization and better nonlinear approximation ability is applied to the prediction of building settlement. Based on BP neural network and wavelet analysis, the traditional network model is optimized and improved. The traditional network model and the improved model are analyzed and studied through the deformation prediction of practical engineering, and the prediction effect is evaluated. The results are satisfactory. It shows that the combination of wavelet analysis and neural network model is feasible in building settlement prediction and has broad engineering application value. This paper mainly studies the following aspects: (1) the BP neural network algorithm is studied. The limitation of the single BP neural network model algorithm is analyzed. Aiming at the problems existing in the traditional network model, the optimization and improvement are carried out to overcome the local minima easily formed but not the global optimum, and the training and learning efficiency is low. The improved model is applied to the deformation prediction. (2) the wavelet analysis is studied. This paper discusses the application of wavelet analysis in signal denoising with MATLAB software, studies the method of signal denoising using wavelet analysis, and the selection of wavelet function, threshold selection, wavelet decomposition, reconstruction and so on. In order to obtain more accurate prediction results, wavelet analysis is used to preprocess the deformation monitoring data reasonably. (3) the combination of wavelet analysis and neural network model is discussed. There are usually two types of combination: one is auxiliary combination, also known as loose combination; the other is embedded combination, that is, compact combination. (4) based on BP neural network model, The improved BP neural network, the auxiliary wavelet neural network and the embedded wavelet neural network model are applied to the settlement prediction of practical engineering with the help of MATLAB,. The overall performance of the three models is analyzed and compared with the measured values. The results show that the combined models of the latter two kinds of wavelet neural networks have similar accuracy and the prediction effect is obviously better than that of the single BP neural network model. At last, the deficiency of this paper is briefly explained.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP183;TU196.2
本文編號：2229214
[Abstract]:With the rapid development of economy and the improvement of urbanization level, the available land resources in cities are decreasing, and various kinds of high-rise buildings are rising rapidly. Because of the increase of floor and the increase of load, the construction will bring complex deformation effect to the building itself and the surrounding buildings. Among them, the most common is to cause uneven settlement, if the settlement will endanger the safety of the building. Deformation monitoring, as a key link of information construction, runs through the whole process of building design period, construction period and operation period. All parties involved in the project pay great attention to monitoring work and data analysis. In recent years, in order to explore a rapid and effective method of settlement prediction, many scholars have made a great deal of exploration and research in theory and practice, and achieved certain results, but there are also many problems and shortcomings. In this paper, according to the characteristics of building foundation settlement and the hot methods which are widely studied in this field, the artificial neural network model with self-learning, self-organization and better nonlinear approximation ability is applied to the prediction of building settlement. Based on BP neural network and wavelet analysis, the traditional network model is optimized and improved. The traditional network model and the improved model are analyzed and studied through the deformation prediction of practical engineering, and the prediction effect is evaluated. The results are satisfactory. It shows that the combination of wavelet analysis and neural network model is feasible in building settlement prediction and has broad engineering application value. This paper mainly studies the following aspects: (1) the BP neural network algorithm is studied. The limitation of the single BP neural network model algorithm is analyzed. Aiming at the problems existing in the traditional network model, the optimization and improvement are carried out to overcome the local minima easily formed but not the global optimum, and the training and learning efficiency is low. The improved model is applied to the deformation prediction. (2) the wavelet analysis is studied. This paper discusses the application of wavelet analysis in signal denoising with MATLAB software, studies the method of signal denoising using wavelet analysis, and the selection of wavelet function, threshold selection, wavelet decomposition, reconstruction and so on. In order to obtain more accurate prediction results, wavelet analysis is used to preprocess the deformation monitoring data reasonably. (3) the combination of wavelet analysis and neural network model is discussed. There are usually two types of combination: one is auxiliary combination, also known as loose combination; the other is embedded combination, that is, compact combination. (4) based on BP neural network model, The improved BP neural network, the auxiliary wavelet neural network and the embedded wavelet neural network model are applied to the settlement prediction of practical engineering with the help of MATLAB,. The overall performance of the three models is analyzed and compared with the measured values. The results show that the combined models of the latter two kinds of wavelet neural networks have similar accuracy and the prediction effect is obviously better than that of the single BP neural network model. At last, the deficiency of this paper is briefly explained.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP183;TU196.2
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