[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123127-en":3,"doc-seo-123127-105":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":4,"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},123127,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Road Surface Analysis through Machine Learning Techniques","Road surface monitoring is essential for reliable transportation, especially in developing countries where roads deteriorate and regular maintenance is difficult. Degraded surfaces contribute to vehicle damage and road accidents, while delays arise when conditions are not detected in time. A smartphone-sensor-based approach is proposed using accelerometer, gyroscope, and GPS data to collect condition signals, filter and process them, and classify road states into potholes, deep traverse cracks, and smooth roads. Neural networks are used to improve accuracy over decision trees and SVM.","IEIE Transactions on Smart Processing and Computing, vol. 13, no. 4, August 2024  \n[https://doi.org/](https://doi.org/10.5573/IEIESPC.2024.13.4.344 344)[10.5573/IEIESPC.2024.13.4.344](https://doi.org/10.5573/IEIESPC.2024.13.4.344 344)[ 344](https://doi.org/10.5573/IEIESPC.2024.13.4.344 344)  \nIEIE Transactions on Smart Processing and Computing  \nRoad Surface Analysis through Machine Learning Techniques  \nPrabhat Singh1, Shilpi Sharma2, Ahmed E. Kamal3, and Sunil Kumar4  \n1 Research Scholar, Department of Computer Science and Engineering, Amity School of Engineering & Technology, Noida,  \nUttar Pradesh [prabhatsinghwal@gmail.com](prabhatsinghwal@gmail.com)  \n2 Associate Professor, Department of Computer Science and Engineering, Amity School of Engineering and Technology, 3 NoProidfaes,sUot,ar PraDepatesmhent soshaf Elrmaectr2i2ca@l amandityCo. uputer Engineering, Iowa State University, Ames [kamal@iastate.edu](kamal@iastate.edu)  \n4 Associate Professor, Computer Science and Engineering, Amity School of Engineering & Technology, Noida, Uttar Pradesh [skumar58@amity.edu](skumar58@amity.edu)  \n* Corresponding Author: Prabhat Singh  \nReceived October 6, 2022; Revised July 20, 2023; Accepted September 22, 2023; Published August 30, 2024  \n* Regular Paper  \nAbstract: Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors overall three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM  \nKeywords: GPS based tracking, Mobile based application, Accelerometer, Gyroscope  \n1. Introduction  \nIn the field of transportation infrastructure, monitoring road conditions is the most challenging task on a worldwide scale because, without proper maintenance, the maintenance and repair costs of the roads significantly increase vehicle damage and road accidents. Major road accidents occur because of the poor conditions of the road surface [1-3] .  \nThe major focus while maintaining good quality roads is to support an efficient road network and reduce traffic accidents. Nevertheless, road maintenance must face various daily challenges, such as weather conditions, heavy manpower and heavy traffic loads.  \nAn efficient and low-latency road surface monitoring system is needed to meet the demands of frequently repairing the deteriorating road surface. Thus far,  \ntraditional systems use equipment that is too expensive, such as LIDAR and GPR, which makes it less efficient for deploying at a very large scale [5, 6] .  \nThe major anomaly that comes as distress in the roads is as follows:  \n·Rutting  \n·Patching  \n·Cracking  \n·Alligator  \n·Block  \n·Traverse  \n·Longitudinal  \n·Raveling  \n·Potholes [4]  \nIEIE Transactions on Smart Processing and Computing, vol. 13, no. 4, August 2024 345  \nFig. 1. Cracking and its types.  \nTherefore, this methodology was used to focus on these anomalies and analyze them using machine-learning methodology.  \n2. Methods  \nThe goal of this study was to des","cbCaitw245sYmMrD","https://ap.wps.com/l/cbCaitw245sYmMrD","pdf",1228974,1,10,"English","en",105,"# Abstract\n# 1. Introduction\n# 2. Methods\n## A. Data Collection\n## B. Data Processing\n## C. Feature Extraction and Labeling\n## D. 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