Comparative Evaluation of Distress Prediction Modeling of Village Roads in India Using Regression and ANN Techniques

Chandrakasu, Makendran; Murugasan, R.; Velmurugan, S.; Sekhar, Chalumuri Ravi

doi:10.1061/jpeodx.0000287

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…This inadequacy not only leads to inflated maintenance costs but also compromises road safety, necessitating a re-evaluation of existing strategies and the adoption of more technologically advanced and efficient approaches (Miller, 2003;Newman, 2012) [1] [2]. Makendran et al, (2021Makendran et al, ( , 2022Makendran et al, ( , 2015Makendran et al, ( , 2020Makendran et al, ( , 2018Makendran et al, ( , 2023Makendran et al, ( , 2022 developed pavement distress models for road management systems [21]…”

Section: Evolving Challenges In Pavement Maintenancementioning

confidence: 99%

Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence

Makendran,

Karthik,

Hasan

et al. 2024

MATEC Web Conf.

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This paper presents the development of an innovative pavement maintenance and management system leveraging advanced drone imagery and Convolutional Neural Network (CNN) image classification. Our system is designed to perform 2D modelling of road surfaces using high-resolution images captured by drones. These images are then analysed by a CNN model specifically trained to detect and classify pavement damages in accordance with the IRC:82 'Code of Practice for Maintenance of Bituminous Surfaces of Highways'. The classification process identifies various types of road distresses such as cracks, potholes, and surface wear. Each identified distress is documented in a comprehensive report detailing the nature of the damage and recommending specific remedies as per IRC guidelines. Furthermore, the system categorizes the severity of the damages, facilitating the dispatch of these results to maintenance authorities for immediate action. This ensures that repair efforts are prioritized effectively, contributing to the maintenance of safer and higher quality roadways. By automating the detection and classification of road damages, this system not only accelerates the repair process but also plays a crucial role in reducing road accidents by maintaining better road conditions. This approach showcases the potential of integrating artificial intelligence and drone technology in the field of road maintenance, marking a significant step towards smarter and safer road infrastructure.

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Section: Evolving Challenges In Pavement Maintenancementioning

confidence: 99%

Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence

Makendran,

Karthik,

Hasan

et al. 2024

MATEC Web Conf.

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“…Common issues encountered in flexible pavements include fatigue cracking, low-temperature cracking, and rutting [1][2][3]. The phenomenon of fatigue cracking, caused by repeated loading, has a detrimental impact on the performance and functionality of flexible pavements.…”

Section: Introductionmentioning

confidence: 99%

Bituminous mixes using Sisal fibre for controlling the fatigue cracks in rural roads in India

Makendran

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Pavements fail due to many factors, including traffic loading, environmental factors, and material degradation. Among these factors, fatigue cracking and rutting play a predominant role in the failure of roads. Specifically, pavements consider only two parameters: fatigue cracking and rutting, which are 20% and 20 mm thick, respectively. Fatigue cracks are formed at the bottom of the bituminous pavement layers due to tensile stresses. To improve the fatigue resistance of the pavements, this study introduced sisal fibre in bituminous mixes, where different fibre lengths 7.5, 10, 12.5, 15, and 17.5 mm were used, which were added to a calculated weight with varying percentages of 0.06, 0.12, 0.18, 0.24, and 0.30 of the total weight of aggregate. Finally, comparing the results significantly improved fatigue resistance and Marshall parameters. The study concluded that the stability of fibres, which were 10 mm in length and made up 0.6% of the sample, reached its lowest point at 12.80 KN when these fibres were mixed with 5.74% Optimum Binder Content (OBC) and 0.30% fibres measuring 17.5 mm in length.

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“…Life-cycle maintenance and operation costs sometimes exceed the original construction costs of pavement structures [6]. Makendran et al (2021) [7] indicated that the most common types of distress parameters observed on low-volume VRs include cracking, potholes, ravelling, and rutting. Rutting is one of the important parameters for low-volume roads as it directly affects the pavement life, travel time, safety, driving comfort, and vehicle operating cost.…”

Section: Introductionmentioning

confidence: 99%

Statistical modelling of rutting for low-volume roads in India

2023

JCE

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This study considers a road rutting model better-suited for the flexible pavements in the low-volume roads of India. The rutting on a road directly affects various aspects such as the pavement performance, safety, driving comfort, and travel time. Accordingly, this study is conducted on 173 test sections of low-volume roads, and a mathematical rutting model is developed using a multiple regression analysis. In the model, the rutting is the response variable, whereas the commercial vehicles per day, rainfall, subgrade strength (as a modified structural number), and pavement age are the explanatory variables. The model is statistically validated and used to forecast road rutting and the effectiveness of the rutting prediction is confirmed. The error between the field data and estimated values is within 5 %.

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Comparative Evaluation of Distress Prediction Modeling of Village Roads in India Using Regression and ANN Techniques

Cited by 8 publications

References 8 publications

Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence

Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence

Bituminous mixes using Sisal fibre for controlling the fatigue cracks in rural roads in India

Statistical modelling of rutting for low-volume roads in India

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