TY - JOUR
T1 - An Automated 3D Crack Severity Assessment Using Surface Data for Improving Flexible Pavement Maintenance Strategies
AU - Li, Zhe
AU - Eskandari Torbaghan, Mehran
AU - Zhang, Tuo
AU - Qin, Xia
AU - Li, Wenda
AU - Li, Yongjian
AU - Zhang, Jiupeng
N1 - Funding:
This work was supported in part by the National Natural Science Foundation of China under Grant 51978068 and in part by the Natural Science Foundation of Shaanxi Province under Grant 2020JM-217. The work of Zhe Li was supported by China Scholarship Council through the University of Birmingham, U.K., for two years.
PY - 2024/9
Y1 - 2024/9
N2 - Evaluation of crack severity in flexible pavements predominantly centers around the analysis of cracks’ surface characteristics. However, this study highlights the critical importance of 3D crack parameters, including volume and depth, for comprehensive assessment. The objective here is to develop an autonomous crack severity assessment, by predicting the vertical parameters of cracks exclusively from their surface properties. To achieve this, a dataset of 3D parameters comprising 200 cracks from eight flexible pavements was acquired, and both linear and nonlinear correlations were conducted among these 3D parameters. Subsequently, five single-output and one multi-output machine learning models were developed to explore the potential of utilizing surface parameters to predict the vertical parameters of cracks. The outcomes validated the effectiveness of two specific methods, namely, Artificial Neural Network and Extreme Gradient Boosting models, in predicting crack volume based on surface parameters, with R2 scores of 0.832 and 0.748, respectively. Additionally, the multi-output machine learning model we developed achieved classification prediction of the crack damage penetration depth using surface parameters, yielding optimal precision, recall, and F1 scores of 0.790, 0.779, and 0.761, respectively. This study has introduced a crack damage evaluation index, based on a 3D assessment, that relates crack depth classification to severity. We provide suggestions that could pave the way for informed decision-making on maintenance strategies that could be adopted to extend asset life cycle.
AB - Evaluation of crack severity in flexible pavements predominantly centers around the analysis of cracks’ surface characteristics. However, this study highlights the critical importance of 3D crack parameters, including volume and depth, for comprehensive assessment. The objective here is to develop an autonomous crack severity assessment, by predicting the vertical parameters of cracks exclusively from their surface properties. To achieve this, a dataset of 3D parameters comprising 200 cracks from eight flexible pavements was acquired, and both linear and nonlinear correlations were conducted among these 3D parameters. Subsequently, five single-output and one multi-output machine learning models were developed to explore the potential of utilizing surface parameters to predict the vertical parameters of cracks. The outcomes validated the effectiveness of two specific methods, namely, Artificial Neural Network and Extreme Gradient Boosting models, in predicting crack volume based on surface parameters, with R2 scores of 0.832 and 0.748, respectively. Additionally, the multi-output machine learning model we developed achieved classification prediction of the crack damage penetration depth using surface parameters, yielding optimal precision, recall, and F1 scores of 0.790, 0.779, and 0.761, respectively. This study has introduced a crack damage evaluation index, based on a 3D assessment, that relates crack depth classification to severity. We provide suggestions that could pave the way for informed decision-making on maintenance strategies that could be adopted to extend asset life cycle.
KW - Three-dimensional displays
KW - Surface cracks
KW - Volume measurement
KW - Surface morphology
KW - Predictive models
KW - Optical variables measurement
KW - Maintenance
U2 - 10.1109/TITS.2024.3379997
DO - 10.1109/TITS.2024.3379997
M3 - Article
SN - 1524-9050
VL - 25
SP - 12490
EP - 12503
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 9
ER -