沥青混合料水-温-光损伤超声波评价及预测方法  

Ultrasonic evaluation and prediction of asphalt mixture damage under the coupling effects of water-temperature-radiation

作　　者：程永春[1] 张鹏[1] 王叶丹 陶敬林 焦峪波[1] 

机构地区：[1]吉林大学交通学院,吉林长春130025

出　　处：《长安大学学报（自然科学版）》2014年第6期50-56,共7页Journal of Chang’an University(Natural Science Edition)

基　　金：国家自然科学基金项目(51278222);吉林大学研究生创新基金资助项目(20121079)

摘　　要：通过室内水-温-光循环试验,将降水、高温和日照3种环境因素对沥青混合料的损伤进行模拟。利用超声波检测技术,测定不同温度、不同含水量的沥青混合料试件在水-温-光循环过程中超声波参数性质的变化,从而对其损伤进行初步判断。通过试件劈裂强度与冻融劈裂强度的衰减来定义其损伤,并应用支持向量机理论,建立波速与沥青混合料损伤程度的回归预测模型,形成了沥青混合料水-温-光损伤无损检测方法。研究结果表明:利用超声波形及频谱的特性变化,可以对水-温-光循环过程中不同形式的沥青混合料的损伤程度进行初步判断;借助支持向量机理论对沥青混合料水-温-光损伤进行超声波评价,相关系数最大值为0.97,最小值为0.90,预测平均误差为4.53%,表现出较高的精度及稳定性。The laboratory water-temperature-radiation cycle test was designed and carried out to simulate the damage of asphalt mixture caused by the environmental factors of rain, high temperatures and sun- shine. The ultrasonic changes were determined while passing through the asphalt mixture specimen un- der different temperatures and water contents in different process of water-temperature-radiation cycles. Then, the damage of specimen was assessed preliminarily based on the ultrasonic changes. The splitting strength and freezing-splitting strength attenuation were defined as the damage parameters. Besides that, the regression prediction model of the ultrasonic velocity and damage coefficient of asphalt mixture was constructed by using the Support Vector Machine （SVM） to predict the asphalt mixture damage in dif- ferent conditions. This ultrasonic evaluation and prediction method can assess the damage of asphalt mix- ture specimen without destroying the specimen itself. The results show that the preliminary judgment of the asphalt mixture damage degree under the water-temperature-ultraviolet cycle can be determined by the changing of the ultrasonic waveform and spectral characteristics. The ultrasonic damage assessment based on support vector machine theory indicates that the maximum and minimum value of the correlation coefficient are 0. 97 and 0. 90 respectively, and the average prediction error is 4.53 %, which reflected the high accuracy and stability of this method. 5 tabs, 6 figs, 17 refs.

关 键 词：道路工程 沥青混合料 水-温-光循环 超声波 支持向量机 

分 类 号：U416.217[交通运输工程—道路与铁道工程]