Application of signal processing and support vector machine to transverse cracking detection in asphalt pavement
Application of signal processing and support vector machine to transverse cracking detection in asphalt pavement
复制标题
信号处理和支持向量机在沥青路面横向裂缝检测中的应用
DOI：
10.1007/s11771-021-4779-6
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发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Zhang Jun
中科院分区：
文献类型：
--
作者：
Yang Qun;Zhou Shi-shi;Wang Ping;Zhang Jun
基于振动的路面状况（粗糙度和明显异常）监测在道路工程中得到了广泛的应用。然而，不明显的横向裂纹几乎没有被考虑。因此，提出了一种基于车载的横向裂纹检测新方法，该方法通过信号处理技术和支持向量机（SVM）。对车辆在有横向裂纹和无横向裂纹路段上行驶时的振动信号进行时域、频域和小波域的信号处理，寻找区分裂纹和无裂纹路段振动信号的指标。这些指标被用来形成8个支持向量机模型。具有最高准确度和F1测量的模型是优选的，包括车辆速度、范围、相对标准差、最大傅立叶系数和小波系数的特征。因此，开发了一种裂纹和无裂纹分级机。并通过2292个路面断面对该方法的可行性进行了研究。检测准确率为97.25%，F1测度为85.25%。本文提出的裂缝检测方法和基于智能手机的IRI等病害检测方法可以形成一个全面的路面状况调查系统。
Vibration-based pavement condition (roughness and obvious anomalies) monitoring has been expanding in road engineering. However, the indistinctive transverse cracking has hardly been considered. Therefore, a vehicle-based novel method is proposed for detecting the transverse cracking through signal processing techniques and support vector machine (SVM). The vibration signals of the car traveling on the transverse-cracked and the crack-free sections were subjected to signal processing in time domain, frequency domain and wavelet domain, aiming to find indices that can discriminate vibration signal between the cracked and uncracked section. These indices were used to form 8 SVM models. The model with the highest accuracy andF1-measure was preferred, consisting of features including vehicle speed, range, relative standard deviation, maximum Fourier coefficient, and wavelet coefficient. Therefore, a crack and crack-free classifier was developed. Then its feasibility was investigated by 2292 pavement sections. The detection accuracy andF1-measure are 97.25% and 85.25%, respectively. The cracking detection approach proposed in this paper and the smartphone-based detection method for IRI and other distress may form a comprehensive pavement condition survey system.
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DOI：
10.1177/1045389x06066292
发表时间：
2007-04
影响因子：
2.7
作者：
P. Pawar;Kanchi Venkatesulu Reddy;R. Ganguli
通讯作者：
P. Pawar;Kanchi Venkatesulu Reddy;R. Ganguli
DOI：
10.1088/0305-4470/37/5/b01
发表时间：
2004-02
期刊：
Journal of Physics A: Mathematical and General
影响因子：
--
作者：
J. Kingsbury
通讯作者：
J. Kingsbury
DOI：
10.1061/(asce)cp.1943-5487.0000258
发表时间：
2013-11-01
影响因子：
6.9
作者：
Ying, Yujie;Garrett, James H., Jr.;Jin, Yuanwei
通讯作者：
Jin, Yuanwei
影响因子：
--
作者：
G. Madhavan
通讯作者：
G. Madhavan
基于统计模式识别的稳定基层沥青路面裂缝评价
影响因子：
3.8
作者：
Qun Yang;Yingjie Deng
通讯作者：
Yingjie Deng