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  本文關(guān)鍵詞：基于BP神經(jīng)網(wǎng)絡(luò)的瀝青老化預(yù)測系統(tǒng)研究　出處：《重慶交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 道路工程 瀝青老化 老化預(yù)測 BP神經(jīng)網(wǎng)絡(luò)

【摘要】：瀝青材料在行車荷載和自然因素（氧氣、水、陽光等）的長時間作用下非常容易發(fā)生老化，導(dǎo)致瀝青路面性能下降。影響瀝青老化的因素主要包括行車荷載作用、瀝青氧化、雨水作用、陽光中紫外線輻射、時間的長短等，其過程具有非線性、混沌性、長期記憶性等特點。本文基于歷史數(shù)據(jù)，運用BP神經(jīng)網(wǎng)絡(luò)模型，對瀝青老化指標的變化進行研究，建立瀝青老化BP網(wǎng)絡(luò)模型，對各影響因素下瀝青的老化性能進行預(yù)測。 首先運用了試驗分析的手段分別分析了3個主要影響瀝青老化的因素-溫度、水、紫外線對瀝青的老化作用，，然后經(jīng)過綜合分析3因素對老化的影響程度得出紫外線溫度水，其次收集了國內(nèi)幾條主要瀝青路面瀝青的實際老化指標，通過對這些老化指標的處理作者能夠得到不同地區(qū)不同使用年限瀝青25℃針入度、15℃延度、軟化點等數(shù)據(jù)。第三，研究BP神經(jīng)網(wǎng)絡(luò)的原理，并推導(dǎo)具體理論算法，了解BP神經(jīng)網(wǎng)絡(luò)的訓(xùn)練過程，為BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)設(shè)計奠定理論基礎(chǔ)。第四，采用多因素(最高氣溫、最低氣溫、年平均降雨量、年平均日照時間、使用年限等因素) BP神經(jīng)網(wǎng)絡(luò)對瀝青三大指標進行預(yù)測，得出在各種因素作用下下瀝青三大指標的變化情況，得到通過MATLAB工具箱實現(xiàn)的適用于指標預(yù)測的具體網(wǎng)絡(luò)。最后，通過對重慶地區(qū)的高速路上的回收瀝青的性能與網(wǎng)絡(luò)預(yù)測的實際性能進行對比，發(fā)現(xiàn)具有良好的規(guī)律性，驗證了BP神經(jīng)網(wǎng)絡(luò)預(yù)測的準確性。 大量可重復(fù)的BP網(wǎng)絡(luò)實驗結(jié)果顯示，運用BP神經(jīng)網(wǎng)絡(luò)模型可以對瀝青老化情況進行較高精度的預(yù)測，這證明了本文所采用的方法和所建立的模型是可行的和有效的，所以，在實驗室很快得到瀝青路面不同的老化數(shù)據(jù)具有了實現(xiàn)的可能，為相關(guān)研究提供參考。
[Abstract]:Asphalt materials in traffic load and natural factors (oxygen, water, sun, etc.) the long time effect is very prone to aging, resulting in a decline in the performance of asphalt pavement. The influence factors including the effect of asphalt aging, the traffic load of asphalt oxidation, rain, sunlight in the ultraviolet radiation, such as the length of time, the process is nonlinear the characteristics, chaos, long memory. Based on the historical data, using the BP neural network model, to study the changes of asphalt aging index, establish the BP network model of asphalt aging, aging properties and influencing factors of asphalt prediction.
First of all by means of experimental analysis respectively to analyze the 3 main factors influencing asphalt aging temperature, water, the aging effect of ultraviolet on the asphalt, and then after a comprehensive analysis of 3 factors on the impact of aging that ultraviolet temperature of water, the actual second collects the domestic several major asphalt pavement asphalt aging index, by the author the aging index can be different in different regions of the asphalt use age 25 C 15 C penetration, ductility, softening point data. Third, the principle of BP neural network, and derive the specific theoretical algorithm, understand the training process of BP neural network, which lays the theoretical foundation for the structure design of BP neural network. Fourth. The multi factors (maximum temperature, minimum temperature, average annual rainfall, the average annual sunshine time, age and other factors) of BP neural network on the three index of asphalt for prediction, obtained in the Changes of factors under the three indices of asphalt, concrete network application is realized through MATLAB toolbox to index prediction. Finally, by comparing the actual performance and the prediction of highway asphalt recycling in Chongqing area. The discovery has good regularity, to verify the accuracy of BP neural network forecast.
The experimental results of a large number of repetitive BP network show that the application of BP neural network model can predict with high accuracy of asphalt aging, which proves that the method used in this paper and the model is feasible and effective, therefore, in the laboratory soon got different asphalt pavement aging data has the potential to achieve and provide a reference for related research.

【學(xué)位授予單位】：重慶交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：U414

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本文編號：1406250