Asphalt Pavement Structural Information Monitoring And Fatigue Prediction

Asphalt pavement performance is influenced by vehicle load, temperature,humidity and many other combined factors, which gradually produce fatigue,rutting, cracks and other damage, seriously impact on pavement service ability. Tolearn about the actual working state and material properties of asphalt pavement isof great importance. However, the existing pavement research methods are mostlybased on damage observation, which is post-phenomenological and with lessaccuracy. On the other hand, numerical simulation is a good research method, butwithout plenty of experimental data support, the authenticity and validity of theresults could not be verified. In response to these problems, a long-term real-timeonline asphalt pavement monitoring system was developed, which is mainly basedon fiber grating sensing technology. Contents and results are as follows:Firstly, test section with four kinds of pavement structures was built, and thedetailed fiber Bragg grating (FBG) sensor layout program was designed.Considering hardware devices and monitoring requirements, a remote onlinemonitoring and data analysis software was developed, which makes the real-timemonitoring of asphalt pavement in a long term come true.Secondly, lab tests were carried out to test FBG sensor’s sensitivity coefficient.Results were analyzed in-depth, and wavelength-temperature conversion formulasand practical applications recommendations were given. Based on laboratory testresults, thermocouples were used to calibrate FBG temperature sensors which wereinstalled in the pavement. Through the analysis of long-term experimental results,pavement temperature field distributions of time and space were obtained.Combined with local weather information, pavement temperature prediction modelswere established.Thirdly, based on composite rutting plate test, FBG strain sensors packagedwith PP and FRP were tested. Temperature and strain measurement features ofdifferent FBG sensors were compared in laboratory. With the help of ABAQUSsoftware, finite element analysis was done to learn about dynamic response of thepavement, the time-history curves were obtained. After a long-term continuousmonitoring, the use of FBG sensors to monitor pavement dynamic and temperature information was verified to be applicable and effective.Finally, based on experimental results of traffic load information, pavementtemperature distribution law and pavement dynamic response, combined with theuniaxial compression dynamic modulus test, fatigue prediction parameters wereselected, and fatigue life prediction with the existing model was completed.In this thesis, FBG temperature, strain, pressure sensors were studied in-depthand applied to a practical project. A set of long-term real-time pavement monitoringsystem was built, including hardware and software. A long-period of monitoringdata was analyzed and some useful results were obtained. Fatigue prediction ofasphalt pavement was completed based on in-the-field data in the end.