本文關(guān)鍵詞： 瀝青路面 結(jié)構(gòu)信息監(jiān)測 數(shù)據(jù)挖掘 性能預(yù)估　出處：《哈爾濱工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：對瀝青路面結(jié)構(gòu)的動力響應(yīng)進行實測研究是澄清復(fù)雜環(huán)境下瀝青路面損壞機理的重要手段。傳感器監(jiān)測技術(shù)和計算機技術(shù)快速發(fā)展,對瀝青路面結(jié)構(gòu)進行長期監(jiān)測并進行大量數(shù)據(jù)積累成為可能。許多監(jiān)測項目積累了大量的路面結(jié)構(gòu)動力響應(yīng)數(shù)據(jù)和環(huán)境信息數(shù)據(jù)。然而,現(xiàn)有的研究側(cè)重于對監(jiān)測數(shù)據(jù)進行規(guī)律性描述,未能有效分析利用海量數(shù)據(jù)。為此,本文把數(shù)據(jù)挖掘理論引入瀝青路面結(jié)構(gòu)信息監(jiān)測領(lǐng)域,實現(xiàn)對路面結(jié)構(gòu)信息數(shù)據(jù)的有效分析利用。研究海量數(shù)據(jù)處理方法,在數(shù)據(jù)挖掘的基礎(chǔ)上分析荷載和復(fù)雜環(huán)境下瀝青路面結(jié)構(gòu)動力響應(yīng),進行瀝青路面性能預(yù)估。主要研究內(nèi)容和成果如下:首先,分析常用的瀝青路面結(jié)構(gòu)監(jiān)測數(shù)據(jù)采集存儲方法,研究有效數(shù)據(jù)判定、數(shù)據(jù)濾波、關(guān)鍵信息提取等海量數(shù)據(jù)處理方法。分析數(shù)據(jù)挖掘在道路工程中的運用及橋梁監(jiān)測、隧道監(jiān)測、邊坡監(jiān)測等相關(guān)領(lǐng)域的數(shù)據(jù)挖掘技術(shù),引入瀝青路面結(jié)構(gòu)信息監(jiān)測數(shù)據(jù)挖掘概念。研究適用于瀝青路面監(jiān)測數(shù)據(jù)的挖掘方法,對海量路面結(jié)構(gòu)信息數(shù)據(jù)進行數(shù)據(jù)挖掘。其次,結(jié)合數(shù)值模擬方法挖掘溫度數(shù)據(jù),研究實際溫度場下的動力響應(yīng)。建立瀝青路面結(jié)構(gòu)溫度場分析有限元模型,研究基于實測瀝青路面關(guān)鍵位置溫度數(shù)據(jù)的瀝青路面結(jié)構(gòu)溫度場數(shù)值模擬方法。與實測溫度場對比分析,驗證基于實測溫度信息模擬路面結(jié)構(gòu)溫度場的有效性。使用ABAQUS軟件建立瀝青路面結(jié)構(gòu)動力分析三維有限元模型,賦予實際溫度場計算其動力響應(yīng),研究實際溫度場下瀝青路面結(jié)構(gòu)的動力響應(yīng)分布規(guī)律,并與使用等溫模型計算的動力響應(yīng)對比,分析等溫模型在瀝青路面力學(xué)計算中的不足�；陧椖壳捌谘芯拷�(jīng)驗、現(xiàn)場輪跡線位置調(diào)查及數(shù)值模擬方法,結(jié)合項目已埋傳感器位置信息及實測響應(yīng)大小,分析瀝青路面結(jié)構(gòu)信息監(jiān)測關(guān)鍵位置。再次,采用時間序列法研究應(yīng)變影響因素,根據(jù)動態(tài)稱重系統(tǒng)實測車輛信息數(shù)據(jù)進行軸載譜分析。根據(jù)關(guān)聯(lián)方法建立最大豎向應(yīng)變與軸載的關(guān)系,把設(shè)計年限內(nèi)的交通量按軸載分級�？紤]溫度、交通量、軸重等參數(shù),研究基于實測殘余豎向應(yīng)變的車轍預(yù)估方法。設(shè)計室內(nèi)車轍試驗,制作三層復(fù)合車轍板并埋入光纖光柵傳感器,實測荷載作用下的豎向殘余應(yīng)變和一定荷載作用次數(shù)下的車轍變形,驗證基于殘余豎向應(yīng)變的車轍預(yù)估模型。最后,分析實測縱向應(yīng)變、橫向應(yīng)變的統(tǒng)計分布規(guī)律,研究應(yīng)變衰變規(guī)律。基于時間序列自回歸模型進行響應(yīng)衰變預(yù)測,研究基于響應(yīng)衰變進行瀝青路面性能衰變預(yù)估的方法。本文重點對海量瀝青路面結(jié)構(gòu)信息數(shù)據(jù)進行挖掘研究,采用數(shù)據(jù)挖掘方法研究實際溫度場下的動力響應(yīng),分析響應(yīng)影響因素,進行使用性能預(yù)估。提高了對監(jiān)測信息的利用度。
[Abstract]:The research on dynamic response of asphalt pavement structure is an important means to clarify the damage mechanism of asphalt pavement in complex environment. The sensor monitoring technology and computer technology are developing rapidly. It is possible to carry out long-term monitoring and data accumulation of asphalt pavement structure. Many monitoring projects have accumulated a large amount of pavement structure dynamic response data and environmental information data. However, The existing research focuses on the regular description of the monitoring data and fails to analyze and utilize the massive data effectively. Therefore, this paper introduces the theory of data mining into the field of asphalt pavement structure information monitoring. Based on the data mining, the dynamic response of asphalt pavement structure under load and complex environment is analyzed. The main research contents and results are as follows: firstly, the common methods of collecting and storing asphalt pavement structure monitoring data are analyzed, and the effective data judgment and data filtering are studied. Analysis of the application of data mining in road engineering and the data mining technology of bridge monitoring, tunnel monitoring, slope monitoring and other related fields, This paper introduces the concept of asphalt pavement structure information monitoring data mining, studies the mining method suitable for asphalt pavement monitoring data, carries on the data mining to the massive pavement structure information data. Secondly, combines the numerical simulation method to mine the temperature data, The dynamic response of asphalt pavement structure under the actual temperature field is studied. The finite element model of temperature field analysis of asphalt pavement structure is established. The numerical simulation method of the temperature field of asphalt pavement structure based on the measured temperature data of the key position of asphalt pavement is studied. To verify the validity of simulating the pavement structure temperature field based on the measured temperature information, the three-dimensional finite element model of asphalt pavement structure dynamic analysis is established by using ABAQUS software, and the actual temperature field is given to calculate its dynamic response. The dynamic response distribution law of asphalt pavement structure under actual temperature field is studied, and compared with the dynamic response calculated by using isothermal model, the deficiency of isothermal model in asphalt pavement mechanics calculation is analyzed. Field wheel track location investigation and numerical simulation method, combined with the location information of buried sensors and the measured response size, analyzed the key position of asphalt pavement structure information monitoring. Thirdly, the influence factors of strain were studied by time series method. According to the measured vehicle information data of dynamic weighing system, the axial load spectrum is analyzed. The relationship between maximum vertical strain and axial load is established by the correlation method, and the traffic volume in the design life is classified according to axial load. The parameters such as temperature, traffic volume and axle load are considered. The rut prediction method based on the measured residual vertical strain is studied. In the design room rutting test, the three-layer composite rutting plate is made and the fiber Bragg grating sensor is embedded. The vertical residual strain under measured load and the rut deformation under a certain number of loads verify the rut prediction model based on residual vertical strain. Finally, the statistical distribution law of measured longitudinal strain and transverse strain is analyzed. The response decay prediction based on time series autoregressive model and the method of predicting asphalt pavement performance decay based on response decay are studied in this paper. The dynamic response under the actual temperature field is studied by using data mining method. The factors affecting the response are analyzed and the performance prediction is carried out. The utilization of monitoring information is improved.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：U416.217

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