本文選題：海堤滲壓 + 多測點監(jiān)測　； 參考：《合肥工業(yè)大學(xué)》2014年碩士論文

【摘要】：沿海地區(qū)經(jīng)濟發(fā)展迅猛，，為保障堤內(nèi)人們的生命財產(chǎn)安全，海堤的重要性越來越受到重視，相關(guān)部門也加強了對海堤的保護措施。海堤是沿海而建的堤防工程，堤身一般延續(xù)很長，涉及范圍廣，工作環(huán)境復(fù)雜，容易出現(xiàn)安全隱患，因此，加強海堤安全監(jiān)測是十分必要且意義重大的工作。隨著海堤建設(shè)管理工作的推進，海堤堤身的安全監(jiān)測已逐步成為保障海堤安全和運行的重要手段，得到了越來越多的關(guān)注。滲壓是影響海堤堤身安全的主要指標，所以滲壓監(jiān)測在海堤安全監(jiān)測分析中占有重要地位。 BP神經(jīng)網(wǎng)絡(luò)是一個相對比較成熟的網(wǎng)絡(luò)，具有很強的非線性映射能力�？紤]到海堤工作環(huán)境的復(fù)雜性，以及滲壓與其影響因素之間的不明確性，本文嘗試將BP神經(jīng)網(wǎng)絡(luò)應(yīng)用于海堤滲壓監(jiān)測預(yù)報中。基于梯度下降法的標準BP算法在應(yīng)用時存在不足之處，通過分析該算法的缺陷，選用優(yōu)化激活函數(shù)方法和附加動量算法分別對標準BP神經(jīng)網(wǎng)絡(luò)進行改進。優(yōu)化激活函數(shù)方法是在激活函數(shù)的公式中加入可調(diào)參數(shù)，對函數(shù)的陡度及映射范圍進行調(diào)節(jié)，從而達到改善網(wǎng)絡(luò)性能的目的；附加動量算法則是在每一個權(quán)值變化量的基礎(chǔ)上加上一項正比于前一次權(quán)值變化量的值，進而加快網(wǎng)絡(luò)權(quán)值更新，對標準BP算法進行改進。前兩種方法是從兩個不同的角度對標準BP神經(jīng)網(wǎng)絡(luò)進行改進，在此基礎(chǔ)上，本文提出一種組合改進算法，即將附加動量算法與優(yōu)化激活函數(shù)方法結(jié)合起來應(yīng)用。 以浦東某海堤實測數(shù)據(jù)為基礎(chǔ)，考慮到單測點建模時不僅工作量大，而且各滲壓測點之間信息關(guān)聯(lián)度不高，本文從多測點角度出發(fā)進行建模，整體分析潮位、降雨、時效等因素對海堤滲壓的影響。輸入層因子選擇時，以BP神經(jīng)網(wǎng)絡(luò)為分析手段，分別對簡化因子形式和組合因子形式進行計算，選擇預(yù)測效果比較好的一組因子作為網(wǎng)絡(luò)模型的輸入。 網(wǎng)絡(luò)結(jié)構(gòu)確定后，分別對三種改進BP算法編程建模，根據(jù)訓(xùn)練和預(yù)測結(jié)果分析它們在海堤滲壓多測點監(jiān)測方面的應(yīng)用情況，并比較改進模型對海堤滲壓的預(yù)測效果。結(jié)果表明，三種改進BP神經(jīng)網(wǎng)絡(luò)在速度和精度方面都有所提高，其中組合改進模型比單一的改進模型具有更好的預(yù)測精度，在海堤滲壓監(jiān)測模型的分析預(yù)報方面取得了很好的效果。
[Abstract]:In order to ensure the safety of people's life and property, the importance of seawall has been paid more and more attention to, and the relevant departments have also strengthened the measures to protect the seawall. The seawall is the levee project built along the coast. The levee body is very long, involving a wide range, the working environment is complex, and it is easy to appear the hidden trouble of safety. Therefore, it is very necessary and significant to strengthen the safety monitoring of the seawall. With the development of the construction and management of the seawall, the safety monitoring of the seawall body has gradually become an important means to ensure the safety and operation of the seawall, and has been paid more and more attention. Seepage pressure is the main index affecting the safety of seawall levees, so seepage pressure monitoring plays an important role in the safety monitoring and analysis of seawall. BP neural network is a relatively mature network with strong nonlinear mapping ability. Considering the complexity of seawall working environment and the uncertainty between seepage pressure and its influencing factors, this paper attempts to apply BP neural network to the monitoring and forecasting of seawall seepage pressure. The standard BP algorithm based on gradient descent method has some shortcomings in application. By analyzing the shortcomings of the algorithm, the optimization activation function method and the additional momentum algorithm are used to improve the standard BP neural network. The method of optimizing activation function is to add adjustable parameters to the formula of activation function to adjust the steepness and mapping range of function, so as to improve the network performance. The additional momentum algorithm is to add a value proportional to the change of the previous weight on the basis of each weight change, and then to speed up the network weight update and improve the standard BP algorithm. The first two methods are to improve the standard BP neural network from two different angles. On this basis, this paper proposes a combinatorial improved algorithm, which combines the additional momentum algorithm with the optimization activation function method. Based on the measured data of a certain seawall in Pudong, considering the heavy workload and the low correlation degree of information between each seepage pressure measuring point, this paper analyzes the tidal level and rainfall from the point of view of multiple measuring points. Effect of aging and other factors on seepage pressure of seawall. In the selection of input layer factors, the simplified factor form and the combination factor form are calculated by BP neural network, and a set of factors with good prediction effect is selected as the input of the network model. After the network structure is determined, three kinds of improved BP algorithm are programmed and modeled respectively. According to the results of training and prediction, their application in monitoring the seepage pressure of seawall is analyzed, and the prediction effect of the improved model on seepage pressure of seawall is compared. The results show that the speed and accuracy of the three improved BP neural networks are improved, and the combined improved model has better prediction accuracy than the single improved BP neural network. Good results have been obtained in the analysis and prediction of seawall seepage pressure monitoring model.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U657;U656.314

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