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 n...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 and F1-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 and F1-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.展开更多
With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection...With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection were analyzed considering cracks qualities.A computer program was developed by visual C++6.0 programming language to detect the cracks,which was tested by 15cases of bridge video images.The results indicate that the relative error is within 6%for cracks larger than 0.3 mm cracks and it is less than 10%for crack width between 0.2 mm and 0.3 mm.In addition,for the crack below 0.1 mm,the relative error is more than30%because the bridge is in safe stage and it is very difficult to detect the actual width of crack.展开更多
基金Project(51778482)supported by the National Natural Science Foundation of China。
文摘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 and F1-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 and F1-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.
基金Project(51178193)supported by the National Natural Science Foundation of ChinaProject(2009 353-344-570)supported by the Ministry of Transport of ChinaProject(2010-02-051)supported by the Transportation Department of Guangdong Province,China
文摘With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection were analyzed considering cracks qualities.A computer program was developed by visual C++6.0 programming language to detect the cracks,which was tested by 15cases of bridge video images.The results indicate that the relative error is within 6%for cracks larger than 0.3 mm cracks and it is less than 10%for crack width between 0.2 mm and 0.3 mm.In addition,for the crack below 0.1 mm,the relative error is more than30%because the bridge is in safe stage and it is very difficult to detect the actual width of crack.
