| 基于结构固有频率的沥青混合料动态模量及预估模型研究 |
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| 引用本文： | 孟安鑫,徐慧宁,傅锡光,谭忆秋.基于结构固有频率的沥青混合料动态模量及预估模型研究[J].中国公路学报,2019,32(2):31-38. |
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| 作者姓名： | 孟安鑫 徐慧宁 傅锡光 谭忆秋 |
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| 作者单位： | 1. 哈尔滨工业大学交通科学与工程学院, 黑龙江哈尔滨 150090;
2. 上海市政工程设计 研究总院(集团)佛山斯美设计院有限公司, 广东佛山 528200;
3. 哈尔滨工业大学 城市水资源与水环境国家重点实验室, 黑龙江哈尔滨 150090 |
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| 基金项目： | 国家自然科学基金项目（U1633201） |
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| 摘 要： | 为实现沥青混合料动态模量的快速、无损检测，开展了基于无约束共振法的沥青混合料动态模量研究。首先，基于固有频率计算原理和时温等效原理，分别采用无约束共振法和重复加载法构建动态模量主曲线，阐明无约束共振法测试动态模量的可行性；然后，基于无约束共振法揭示温度、级配类型和空隙率对沥青混合料动态模量的影响规律。基于此，构建包含584组试验数据的动态模量预估模型数据库，采用BP神经网络方法建立动态模量预估模型，并与传统Witczak预估模型进行对比。结果表明：采用沥青混合料固有频率表征动态模量合理可行；无约束共振法可将重复加载法构建的动态模量主曲线最大换算频率107 Hz扩大至1014 Hz，构建更宽频域范围的动态模量主曲线，提高高频模量预估的准确性；BP神经网络预估模型的预测效果明显优于Witczak模型。研究为动态模量的快速、无损检测提供了技术基础。
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| 关 键 词： | 道路工程 沥青混合料 无约束共振法 动态模量 BP神经网络 |
| 收稿时间： | 2018-03-11 |
Dynamic Modulus and Prediction Model of Asphalt Mixture Based on Structural Natural Frequency |
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| MENG An-xin,XU Hui-ning,FU Xi-guang,TAN Yi-qiu.Dynamic Modulus and Prediction Model of Asphalt Mixture Based on Structural Natural Frequency[J].China Journal of Highway and Transport,2019,32(2):31-38. |
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| Authors: | MENG An-xin XU Hui-ning FU Xi-guang TAN Yi-qiu |
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| Affiliation: | 1. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China;
2. Foshan Simei Design Institute Co., Ltd., Shanghai Municipal Engineering Design Institute(Group), Foshan 528200, Guangdong, China;
3. State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China |
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| Abstract: | To realize rapid and nondestructive testing of the dynamic modulus of an asphalt mixture, Free-free Resonant Test (FFRT) was studied. Firstly, based on the principles of natural frequency calculations and equivalent temperatures, the main dynamic modulus curves were constructed by FFRT and the repetitive loading method. Then, the feasibility of testing dynamic modulus using FFRT was expounded, and the influence of temperature, gradation type, and porosity on the dynamic modulus of the asphalt mixture was studied by the FFRT method. Hence, a dynamic modulus prediction-model database was constructed based on the experimental data of 584 groups. Based on this investigation, the back-propagation (BP) neural network method was used to establish the dynamic modulus prediction model and compared with the traditional Witczak prediction model. The results demonstrate that it is both reasonable and feasible to characterize the dynamic modulus of an asphalt mixture by measuring the natural frequency. The FFRT can extend the constructed maximum frequency of the dynamic modulus main curve from 107 Hz to 1014 Hz using the repeated loading method (RLM). The main dynamic modulus in this wider frequency range was constructed, and the accuracy of high frequency prediction of the dynamic modulus was improved. The prediction results of the BP neural network model are more accurate than the Witczak model. In conclusion, the above study provides a technical basis for fast and nondestructive testing of the dynamic modulus. |
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| Keywords: | road engineering asphalt mixture free-free resonant test dynamic modulus BP neural network |
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