[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124964-en":3,"doc-seo-124964-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124964,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Classifying Flexible Pavement Defects Using Hybrid Machine Learning Approach","Transportation infrastructure critically affects GDP, making effective road maintenance a priority for developing nations. Pavement maintenance and management systems rely on pavement monitoring to evaluate defect severity and maintenance prioritization, yet traditional surveys require extensive human supervision. This study proposes a semi-automated hybrid machine learning method combining support vector machine and convolutional neural network to classify flexible pavement distress. Using Ahmedabad city data selected per IRC guidelines, the model trains on 1,000 images per crack type and tests on 100, achieving 87% training accuracy and 91% testing accuracy for defect classification.","Classifying flexible pavement defects using hybrid machine  \nlearning approach  \nJaykumar Soni, Rajesh Gujar  \nDepartment of Civil Engineering, Pandit Deendayal Energy University, Gandhinagar, India  \n\n| Article history:\u003Cbr>Received Dec 29, 2023 Revised Feb 21, 2024 Accepted Feb 22, 2024 | The transportation infrastructure sector significantly impacts a country ’s gross domestic product (GDP), particularly in developing nations striving to manage and maintain road networks as valuable assets. While asset generation is integral, the more intricate challenge lies in effective maintenance. Pavement monitoring, a crucial component of pavement maintenance and management systems (PMMS), evaluates defect severity, road maintenance prioritization, and maintenance types. To enhance road health monitoring, the present study introduces a hybrid machine learning (ML) method, integrating support vector machine (SVM) and convolutional neural network (CNN) . The proposed semi-automated detection system aims to reduce human supervision in traditional surveys, thereby cutting down the cost of pavement distress maintenance The research utilizes data collected by the authors from Ahmedabad city, Gujarat, following Indian road congress (IRC) guidelines for defect selection. Training involves 1,000 images for each crack type, with testing on 100 images. Results indicate that the SVM-CNN model achieves 87% accuracy in training and 91% accuracy in testing for road defect classification, showcasing its efficiency in pavement maintenance and management. The system presents the potential to significantly enhance the efficiency of road maintenance processes, making it a valuable asset for developing nations striving for a more streamlined approach to road network preservation.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Convolutional neural network Machine learning\u003Cbr>Pavement defects\u003Cbr>Pavement management system Support vector machine |  |\n\nCorresponding Author:  \nRajesh Gujar  \nDepartment of Civil Engineering, Pandit Deendayal Energy University Raisan 382 426, Gandhinagar, India  \nEmail: [rajesh.gujat@sot.pdpu.ac.in](rajesh.gujat@sot.pdpu.ac.in)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe development of infrastructure facilities and utilities has witnessed a significant surge in developing nations over the past few decades [1] . With over a century of growth, highways have evolved into major global transportation routes [2] . The transportation sector, a pivotal component of the infrastructure chain, profoundly influences the socioeconomic growth of regions and countries [3] . Roadway transportation plays a central role in daily life and the economy, providing mobility and contributing to production [4] . A well-maintained road network is crucial for socioeconomic progress, serving as a fundamental accelerator for civilization [5] . Within the domain of essential infrastructure asset management, the availability of a wellkept road network is essential to meet user needs and minimize issues arising from poorly maintained roads [6] . Maintenance considerations are particularly crucial, given the escalating population growth and economic development, leading to an increase in vehicles and traffic accidents [7] . The resulting increase in traffic load inevitably leads to pavement damage [8], imposing increased costs on the public and subsequently higher rehabilitation expenses [9] . Deteriorated pavement conditions adversely affect ride  \nquality, create hazardous driving conditions, and contribute to vehicle damage, costing drivers approximately £2.8 billion annually [9] .  \nDespite substantial expenditures on public infrastructure construction, road maintenance remains neglected in numerous developing countries, where emphasis on maintaining existing roads is deemed more desirable than constructing new ones [10], [11] . Neglect of short-term routine maintenance can result in general degradation a","cbCaiaGZWt4EZzzm","https://ap.wps.com/l/cbCaiaGZWt4EZzzm","pdf",361087,1,9,"English","en",105,"# Introduction\n## Pavement maintenance and management systems (PMMS)\n## Challenges of manual and conventional monitoring\n# Proposed hybrid machine learning approach\n## SVM-CNN semi-automated detection system\n# Data and experimental setup\n## Dataset collection and crack selection\n## Training and testing procedure\n# Results and discussion\n## Classification accuracy for defect types","[{\"question\":\"What problem does the study address in road maintenance?\",\"answer\":\"The study targets the challenge of effective pavement monitoring, where traditional surveys are costly and require heavy human supervision to detect and classify pavement defects.\"},{\"question\":\"How does the proposed method classify flexible pavement defects?\",\"answer\":\"It uses a hybrid machine learning model that combines a support vector machine (SVM) with a convolutional neural network (CNN) in a semi-automated detection system.\"},{\"question\":\"What accuracy does the SVM-CNN model achieve?\",\"answer\":\"The model reports 87% accuracy in training and 91% accuracy in testing for road defect classification.\"}]","Classifying Flexible Pavement Defects Using Hybrid Machine Learning Approach | 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problem does the study address in road maintenance?","Question",{"text":75,"@type":76},"The study targets the challenge of effective pavement monitoring, where traditional surveys are costly and require heavy human supervision to detect and classify pavement defects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method classify flexible pavement defects?",{"text":80,"@type":76},"It uses a hybrid machine learning model that combines a support vector machine (SVM) with a convolutional neural network (CNN) in a semi-automated detection system.",{"name":82,"@type":73,"acceptedAnswer":83},"What accuracy does the SVM-CNN model achieve?",{"text":84,"@type":76},"The model reports 87% accuracy in training and 91% accuracy in testing for road defect 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