Examination on Crack Evaluation Index of Pavement

Nakamura, Hiroyasu; Kameyama, Shuuichi; Sakuraba, Akira; Yokota, Yoshiyasu

doi:10.2208/jscejpe.76.1_60

Cited by 3 publications

(4 citation statements)

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Section: Data 221 Pavement Survey Datamentioning

confidence: 99%

“…Then, the AI program (the U-net network architecture [52]) detects cracks in extracted images. Finally, the process calculates the crack ratio (CR) value for each segment, a conventional pavement damage index in Japan [53]. Figure 2 illustrates the process for determining the CR by following the Handbook of Pavement Survey and Test Methods published by the Japan Road Association [54].…”

Section: Data 221 Pavement Survey Datamentioning

confidence: 99%

See 1 more Smart Citation

A Novel Approach for Urban Road Network Maintenance Plans Using Spatial Autocorrelation Analysis and Roadside Conditions: A Case Study of Muroran City, Japan

Asada

Arimura

et al. 2022

Sustainability

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Urban and residential roads play an integral role in the infrastructure system of a city. Although they take up a large proportion of the national road network, maintenance plans for urban roads are beset by many problems. These include difficulty in collecting enormous volumes of data, implementing analyses, and interpreting results because of complicated frameworks. Thus, this study aims to introduce an effective and reliable method of formulating a maintenance plan using integrated criteria of spatial autocorrelation analysis and roadside conditions. The results demonstrate that defective pavements are clustered in certain areas, for example, mountainous and forested areas, which indicate environmental effects. Using a mixed index as a criterion for prioritization, approximately 55% of roadside residents (represented by the total residential housing floor area) and 90% of commercial and medical facilities surrounding critical sections gained benefit from maintenance activities in the second year. Importantly, the proposed method presents the advantages of simplifying implications and quantitative outcomes that could support local agents in not only implementing but also making decisions and interpreting such decisions for the community.

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Section: Data 221 Pavement Survey Datamentioning

confidence: 99%

Section: Data 221 Pavement Survey Datamentioning

confidence: 99%

A Novel Approach for Urban Road Network Maintenance Plans Using Spatial Autocorrelation Analysis and Roadside Conditions: A Case Study of Muroran City, Japan

Asada

Arimura

et al. 2022

Sustainability

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show abstract

“…Lengths should be quantitatively evaluated. In previous research, it is suggested the thicknesses of asphalt pavement cracks are related to the conditions of pavements (Nakamura et al [2020]). In terms of concrete structures, the thicknesses of vertical cracks are related to the extent of corrosion of reinforcement steel rebars (Vidal et al [2004]).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Yamaguchi¹,

Mizutani²

2022

Preprint

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<p> Road cracks are an important concern of administrators. Visual inspection is labor-intensive and subjective, while previous algorithms detecting cracks from optical camera images were not accurate. Furthermore, the actual length and thicknesses of a crack cannot be estimated only from images. Light Detection and Ranging (Lidar) is a standard feature introduced on the latest smartphones. In this research, for completely automatic, accurate and quantitative road crack evaluation using smartphones, an up-to-date segmentation technique, U-Net with morphology transform adopting data augmentation was proposed. Lidar 3D point cloud data of smartphones is linked to color data obtained from cameras. By registering images to Lidar data, geometrical relationships were estimated to calculate the length and thicknesses. The proposed algorithm was validated by a standard database of road cracks and dataset constructed by the authors, showing 95% length accuracy and 0.98 coefficient of determination for thickness estimation irrespective of various crack shapes and asphalt pavement color patterns. </p>

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“…Thus, the cracks must be properly segmented, and their lengths must be quantitatively evaluated. Previous research has suggested that the thickness of asphalt pavement cracks is related to pavement conditions (Nakamura et al., 2020). In concrete structures, the thickness of vertical cracks is related to the extent of corrosion of reinforcing steel rebars (Vidal et al., 2004).…”

Section: Introductionmentioning

confidence: 99%

Quantitative road crack evaluation by a U‐Net architecture using smartphone images and Lidar data

Yamaguchi

Mizutani

2023

Computer aided Civil Eng

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Road cracks are a major concern for administrators. Visual inspection is labor‐intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation of road cracks. The two contributions of this study are as follows. To achieve the highest segmentation accuracy, U‐Net is combined with data augmentation and morphology transform. To calculate the crack length and thickness, crack images are registered into Lidar color data. The proposed algorithm was validated using a public database of road cracks and those measured by the authors. The algorithm was 95% accurate in determining crack length. The coefficient of determination for thickness estimation accuracy was 0.98 addressing various crack shapes and asphalt pavement patterns.

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Examination on Crack Evaluation Index of Pavement

Cited by 3 publications

References 0 publications

A Novel Approach for Urban Road Network Maintenance Plans Using Spatial Autocorrelation Analysis and Roadside Conditions: A Case Study of Muroran City, Japan

A Novel Approach for Urban Road Network Maintenance Plans Using Spatial Autocorrelation Analysis and Roadside Conditions: A Case Study of Muroran City, Japan

Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Quantitative road crack evaluation by a U‐Net architecture using smartphone images and Lidar data

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