本文選題：沉降監(jiān)測 + BP神經(jīng)網(wǎng)絡(luò)　； 參考：《江西理工大學(xué)》2015年碩士論文

【摘要】：隨著經(jīng)濟(jì)的發(fā)展與人口的增長,我國的各項(xiàng)基礎(chǔ)設(shè)施建設(shè)正在漸漸地完善和優(yōu)化,尤其是在現(xiàn)代化的交通建設(shè)方面,國家每年都投入巨大的人力、財力和物力,以保障人們安全、便捷、舒適的出行。然而,在北京、上海、廣州、天津、深圳等各大城市,地面上的公共交通依然較難滿足市民們的通勤要求,為緩解交通壓力,我國地鐵的修建正緊鑼密鼓地進(jìn)行。首先,地鐵的建設(shè)與運(yùn)行都在地下空間,將大量的地面交通量分散到地下,極大地緩解了地面的公共交通壓力;其次,在人口流動密集的城市,地鐵在速度、穩(wěn)定性、便捷性和運(yùn)輸力等方面都要優(yōu)于地面的公共交通;最后,重要的是,地鐵依靠電力來驅(qū)動,這不但可以節(jié)約煤炭和石油等不可再生能源、減少環(huán)境污染,而且還符合國家倡導(dǎo)的“低碳生活,綠色出行”的理念,這也正好符合我國建設(shè)現(xiàn)代化交通的目標(biāo)。但是,近年來由于地質(zhì)環(huán)境的破壞和其他原因引起的地表沉降都對地鐵的安全運(yùn)行產(chǎn)生了巨大的危害,因此,地鐵地表沉降問題的研究尤為必要和重要。地鐵周邊的地表沉降是一個涉及到測繪、巖土、水文、地質(zhì)和力學(xué)等各種學(xué)科交錯的綜合性問題,而變形監(jiān)測的數(shù)據(jù)極易受到地質(zhì)條件,氣候變化等因素的影響,還存在著參考資料不足、作用機(jī)理不明等問題,采用傳統(tǒng)常規(guī)的建模方法沒有辦法高效準(zhǔn)確地進(jìn)行預(yù)測和分析。本文采用遺傳算法中的選擇算子、交叉算子和變異算子來對BP神經(jīng)網(wǎng)絡(luò)進(jìn)行權(quán)值和閾值的改進(jìn),并將BP網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)進(jìn)行調(diào)整優(yōu)化,充分利用了BP神經(jīng)網(wǎng)絡(luò)模型具有較高容錯性、自適應(yīng)性和處理具有非結(jié)構(gòu)性、非精確性規(guī)律的數(shù)據(jù)時表現(xiàn)出的超強(qiáng)的非線性映射能力等優(yōu)點(diǎn),同時針對標(biāo)準(zhǔn)BP神經(jīng)網(wǎng)絡(luò)初始化具有隨機(jī)性、訓(xùn)練過程收斂速度慢、結(jié)果易陷入局部最優(yōu)等缺點(diǎn),應(yīng)用自適應(yīng)的遺傳算法來對BP神經(jīng)網(wǎng)絡(luò)模型的閥值參數(shù)進(jìn)行全局優(yōu)化,并結(jié)合蘇州地鐵一號線的濱河路站4號出口的地表沉降工程,將改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)模型與傳統(tǒng)常規(guī)的灰色Verhulst模型以及BP神經(jīng)網(wǎng)絡(luò)模型進(jìn)行了對比,定量地分析了三種模型的預(yù)測精度。結(jié)果證明,采用遺傳算法改進(jìn)的BP神經(jīng)網(wǎng)絡(luò)模型不僅能夠較好地利用原始監(jiān)測數(shù)據(jù)進(jìn)行復(fù)雜的學(xué)習(xí)和信息處理,并且具有較高的容錯性和魯棒性,同時也表明了該方法能夠綜合考慮多種因素的影響,可以將其應(yīng)用到實(shí)際的變形監(jiān)測中,是值得采用的一種模型。
[Abstract]:With the development of the economy and the growth of the population, the infrastructure construction of our country is gradually improving and optimizing, especially in the modern transportation construction, the country invests huge human, financial and material resources every year.In order to ensure the safety of people, convenient, comfortable travel.However, in Beijing, Shanghai, Guangzhou, Tianjin, Shenzhen and other major cities, the public transport on the ground is still difficult to meet the commuting requirements of citizens.First, the construction and operation of the subway are in underground space, dispersing a large amount of surface traffic to the ground, greatly relieving the pressure of public transport on the ground; secondly, in cities with dense population mobility, the subway is at speed and stability.In terms of convenience and transport power, it is better than public transport on the ground. Finally, it is important that the subway is driven by electricity, which not only saves non-renewable energy sources such as coal and oil, but also reduces environmental pollution.It is also in line with the concept of "low carbon life, green travel" advocated by the state, which coincides with the goal of building modern transportation in our country.However, in recent years, the ground subsidence caused by the destruction of geological environment and other reasons has caused great harm to the safe operation of subway. Therefore, the study of subway surface subsidence is particularly necessary and important.The ground subsidence around the subway is a comprehensive problem involving surveying, mapping, geotechnical, hydrology, geology and mechanics, and the deformation monitoring data are easily affected by geological conditions, climate change and other factors.There are still some problems such as lack of reference data and unclear mechanism of action. There is no way to predict and analyze efficiently and accurately by using conventional modeling methods.In this paper, the selection operator, crossover operator and mutation operator in genetic algorithm are used to improve the weight and threshold of BP neural network, and the topological structure of BP neural network is adjusted and optimized.The BP neural network model has the advantages of high fault-tolerance, self-adaptability and the ability of super-strong nonlinear mapping when dealing with data with imprecise laws and so on.Aiming at the randomness of the initialization of the standard BP neural network, the slow convergence speed of the training process and the easy to fall into the local optimum, the adaptive genetic algorithm is used to optimize the threshold parameters of the BP neural network model globally.The improved BP neural network model is compared with the traditional grey Verhulst model and the BP neural network model, combined with the surface settlement project of Binhe Road Station No. 4 of Suzhou Metro Line 1, the improved BP neural network model is compared with the traditional grey Verhulst model and the BP neural network model.The prediction accuracy of the three models is analyzed quantitatively.The results show that the BP neural network model improved by genetic algorithm can not only make use of the original monitoring data for complex learning and information processing, but also have high fault tolerance and robustness.It is also shown that the method can comprehensively consider the influence of many factors and can be applied to the actual deformation monitoring. It is a model worthy to be adopted.
【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】：P642.26;TP18

【共引文獻(xiàn)】

相關(guān)期刊論文 前10條

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本文編號：1748045