Automatic detection and location of pavement internal distresses from ground penetrating radar images based on deep learning

Xiong, Xuetang; Meng, Anxin; Lu, Jie; Tan, Yiqiu; Chen, Bo; Tang, Jiaming; Zhang, Chao; Xiao, Shenqing; Hu, Jinyuan

doi:10.1016/j.conbuildmat.2023.134483

Cited by 13 publications

(2 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These methods are low-cost but have many common limitations in terms of operational efficiency and traffic interference. However, with the advancement of testing methods, non-contact rapid detection methods have also explosively emerged in the current era [8]. The non-contact detection was proposed to measure the mean profile depth (MPD) by some specific sensors or devices, such as CCD cameras [9,10], point or line laser sensors [11][12][13][14][15], and internal structure detection (e.g., x-ray CT [16]).…”

Section: Introductionmentioning

confidence: 99%

Exploring the relationship between pavement mean texture depth and mean profile depth: from theoretical derivation to field results

Ran,

Xiao,

et al. 2024

Surf. Topogr.: Metrol. Prop.

Self Cite

View full text Add to dashboard Cite

Pavement mean texture depth (MTD) and mean profile depth (MPD) are common evaluation indicators for characterizing the pavement surface. Clarifying the relationship between the two contributes to coordinate various measurement methods and evaluation criterions. The calculation methods of MTD and MPD were first introduced to trace the similarity between the indicators. Subsequently, the tortuosity area of surface texture was observed using X-ray Computer Tomography (CT). To further determine the model parameter, over 3000 sets of field data in Research Institute of Highway Ministry of Transport (RIOH) track were measured. Meanwhile, the field results of another track and existing recommended model were also used to validate the model. The results show that the shift relationship between MTD and MPD is theoretically unified into a linear model with a slope of 1 and an intercept related to the surface tortuosity. These tortuous areas, the main difference source between the two indicators, would lead to a greater equivalent texture depth under the coarser gradation or lower the compactness of the asphalt mixture. Even for the different track roads, the determined model (MTD=MPD+0.03) has a lower mean relative estimation error, indicating good applicability. This study provides theoretical explanation and empirical references for the development of texture depth indicator MPD specifications.

show abstract

Section: Introductionmentioning

confidence: 99%

Exploring the relationship between pavement mean texture depth and mean profile depth: from theoretical derivation to field results

Ran,

Xiao,

et al. 2024

Surf. Topogr.: Metrol. Prop.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The study not only aims to enhance the predictive capabilities of PMS through machine learning-based models but also seeks to integrate advanced image detection technologies to support real-time monitoring and assessment of pavement conditions [37][38][39]. By providing accurate and timely detection of reflective cracking, these methodologies contribute to the optimization of pavement maintenance strategies and the overall improvement of pavement infrastructure management practices [40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Advanced Reflective Cracking Prediction and Detection for Pavement Management Systems Using Machine Learning and Image Detection

Shin,

Kim,

2024

Buildings

View full text Add to dashboard Cite

This research manuscript presents a comprehensive investigation into the prediction and detection of reflective cracking in pavement infrastructure through a combination of machine learning approaches and advanced image detection techniques. Leveraging machine learning algorithms, reflective cracking prediction models were developed and optimized for accuracy and efficiency. Additionally, the efficacy of image detection methods, particularly utilizing Mask R-CNN, was explored for robust and precise identification of reflective cracking on pavement surfaces. The study not only aims to enhance the predictive capabilities of pavement management systems (PMSs) through machine learning-based models but also seeks to integrate advanced image detection technologies to support real-time monitoring and assessment of pavement conditions. By providing accurate and timely detection of reflective cracking, these methodologies contribute to the optimization of pavement maintenance strategies and the overall improvement of pavement infrastructure management practices. Results indicate that the developed machine learning models achieve an average predictive accuracy of over 85%, with some models achieving accuracies exceeding 90%. Moreover, the utilization of a mask region-based convolutional neural network (Mask R-CNN) for image detection demonstrates exceptional precision, with a detection accuracy of over 95% on average across different pavement types and weather conditions. The results demonstrate the promising performance of the developed machine learning models in predicting reflective cracking, while the utilization of Mask R-CNN showcases exceptional accuracy in the detection of reflective cracking from images. This research underscores the importance of leveraging cutting-edge technologies to address challenges in pavement infrastructure management, ultimately supporting the sustainability and longevity of transportation networks.

show abstract

Choice of Sampling Strategy for Flexible Pavement Distress Survey

Ramamurthy,

Nivitha

2024

J. Inst. Eng. India Ser. A

View full text Add to dashboard Cite

Automatic detection and location of pavement internal distresses from ground penetrating radar images based on deep learning

Cited by 13 publications

References 58 publications

Exploring the relationship between pavement mean texture depth and mean profile depth: from theoretical derivation to field results

Exploring the relationship between pavement mean texture depth and mean profile depth: from theoretical derivation to field results

Feasibility of Advanced Reflective Cracking Prediction and Detection for Pavement Management Systems Using Machine Learning and Image Detection

Choice of Sampling Strategy for Flexible Pavement Distress Survey

Contact Info

Product

Resources

About