Pavement Maintenance Threshold Detection and Network-Level Rutting Prediction Model Based on Finnish Road Data

Dettenborn, Taavi; Hartikainen, Ari; Korkiala-Tanttu, Leena

doi:10.1061/(asce)is.1943-555x.0000539

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…e network of asphalt pavement roads is a crucial element of infrastructure in modern societies [1][2][3][4]. As pointed out by [5], asphalt pavement roads significantly support social interaction as well as economic development.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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During the phase of periodic asphalt pavement survey, patched and unpatched potholes need to be accurately detected. This study proposes and verifies a computer vision-based approach for automatically distinguishing patched and unpatched potholes. Using two-dimensional images, patched and unpatched potholes may have similar shapes. Therefore, this study relies on image texture descriptors to delineate these two objects of interest. The texture descriptors of statistical measurement of color channels, the gray-level cooccurrence matrix, and the local ternary pattern are used to extract texture information from image samples of asphalt pavement roads. To construct a classification model based on the extracted texture-based dataset, this study proposes and validates an integration of the Support Vector Machine Classification (SVC) and the Forensic-Based Investigation (FBI) metaheuristic. The SVC is used to generalize a classification boundary that separates the input data into two class labels of patched and unpatched potholes. To optimize the SVC performance, the FBI algorithm is utilized to fine-tune the SVC hyperparameters. To establish the hybrid FBI-SVC framework, an image dataset consisting of 600 samples has been collected. The experiment supported by the Wilcoxon signed-rank test demonstrates that the proposed computer vision is highly suitable for the task of interest with a classification accuracy rate = 94.833%.

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Section: Introductionmentioning

confidence: 99%

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

2021

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“…In recent years, propelled by the rapid evolution of artificial intelligence technology, deep learning techniques have found extensive application in the intelligent identification of road defects [1][2][3][4] , emerging as a significant tool to complement urban road maintenance decision-making [5][6][7][8] . Xiao Liyang et al [9] enhanced the Mask R-CNN model, achieving precise localization and extraction of road surface cracks under high thresholds through a cascade of multiple detectors.…”

Section: Introductionmentioning

confidence: 99%

A method for intelligent generation of decision information for urban road maintenance

Jiang,

Zhang,

et al. 2024

Seventh International Conference on Traffic Engineering and Transportation System (ICTETS 2023)

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Amidst the vigorous endeavor towards the monumental development of our nation's infrastructural foundation, the expansion of road mileage on an annual trajectory has magnified, elevating the timely detection and upkeep of road afflictions into an exigent imperative. In the present exposition, a novel methodology is introduced, predicated upon the sagacity of ailment discernment in urban thoroughfare preservation decision-making. This approach's principal objective is to augur the efficiency and scientificity inherent in road preservation determinations. Anchored in the outputs of a road ailment recognition model, propelled by the profundities of machine learning, the raw findings of road affliction identification engender an initial corpus. This is further processed through automated milepost parsing and correlation, coupled with the shrewd evaluation of roadcraft and preservation status, culminating in the astute generation of informative summative documents germane to the requisites of road quality oversight. The fruition of this endeavor manifests in the precise synthesis and comprehensive synthesis of road ailment intelligence. The feasibility and applicability of this approach are substantiated through meticulous experimentation and the ensuing analytical exegesis, thereby corroborating its utility as a Provide important reference information for road maintenance decision-making. A vital bedrock is thereby furnished, from which the precincts of maintenance planning for roadways and the expeditious amelioration of road maladies derive a resounding impetus, engendering a realm of augmented road safety and resilience, impeccably supported.

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“…Rutting is one of the most concerning asphalt pavement distresses as it not only causes further damage to the performance of the pavement, but also directly affects driving safety and comfort [1][2][3][4]. Fast and accurate inspection of rutting is an important prerequisite for transportation agencies to make appropriate road maintenance decisions [5,6].…”

Section: Introductionmentioning

confidence: 99%

Measuring Surface Deformation of Asphalt Pavement via Airborne LiDAR: A Pilot Study

Zhu,

Gao,

Huang

et al. 2023

Drones

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Measuring the surface deformation of asphalt pavement and acquiring the rutting condition is of great importance to transportation agencies. This paper proposes a rutting measuring method based on an unmanned aerial vehicle (UAV) mounted with Light Detection and Ranging (LiDAR). Firstly, an airborne LiDAR system is assembled and the data acquisition method is presented. Then, the method for point cloud processing and rut depth computation is presented and the results of field testing are discussed. Thirdly, to investigate error factors, the laser footprint positioning model is established and sensitivity analysis is conducted. Factors including flight height, LiDAR instantaneous angel, and ground inclination angle are discussed. The model was then implemented to obtain the virtual rut depth and to verify the accuracy of the field test results. The main conclusions include that the measurement error increases with the flight height, instantaneous angle, and angular resolution of the LiDAR. The inclination angle of the pavement surface has adverse impact on the measuring accuracy. The field test results show that the assembled airborne LiDAR system is more accurate when the rut depth is significant. The findings of this study pave the way for future exploration of rutting measurement with airborne LiDAR.

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Pavement Maintenance Threshold Detection and Network-Level Rutting Prediction Model Based on Finnish Road Data

Cited by 7 publications

References 6 publications

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

Computer Vision‐Based Patched and Unpatched Pothole Classification Using Machine Learning Approach Optimized by Forensic‐Based Investigation Metaheuristic

A method for intelligent generation of decision information for urban road maintenance

Measuring Surface Deformation of Asphalt Pavement via Airborne LiDAR: A Pilot Study

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