Open Access
This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Ravi Kumar,
Smita Tung,
- Assistant Professor, Department of civil engineering, GLA University, Mathura, India
- Assistant Professor, Department of civil engineering, GLA University, Mathura, India
Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_123663’);});Edit Abstract & Keyword
Bitumen, often referred to as asphalt in its solid form, is a complex mixture of organic compounds derived from the distillation of crude oil. Its chemical composition varies depending on its source, processing methods, and intended application. Pavement surface monitoring is an important part to increase the life of pavement and to minimize the cost incur in maintenance of pavement at an early stage. Traditionally pavement monitoring done using manually method. The aim of this study is to detect the distresses at an early stage so that maintenance can be cost effective, time consuming and economical. In this study National Highway -44 is selected which is stretch from New Delhi to Agra about 200 Km. Taj Mahal and Lord Krishna temple in Agra and Mathura respectively is the center point of attraction for tourism and also for Business, therefore traffic increases more in these cities and subsequently on NH-44 because of that pavement distresses are more frequent and need to be maintained effectively. Pavement distresses are captured using camera and at the same time location is also recorded, these images are then plotted on the map using the software, Geo Setter. Geo Setter helps to export the images to Google earth file which can be used to identify the pavement condition. Pavement distress are classified on the basis of their severity, low, medium and high severity pavement condition. Severity parameters from Federal Highway Administration taken as the bench mark to classify distresses on the basis of distress severity. This helps to save the manual labour and provide the efficient and accurate data and safety is also major concern which can be minimized using this method. This file can be shared with maintenance department to plan for future maintenance according to requirement and severity.
Keywords: Mathura, Geo Setter, Pavement Distress Images, Polymerization of Bitumen
Ravi Kumar, Smita Tung. A Method to Produce a GIS Database of Asphalt Polymer Pavement Distress of National Highway -44. Journal of Polymer and Composites. 2024; ():-.
Ravi Kumar, Smita Tung. A Method to Produce a GIS Database of Asphalt Polymer Pavement Distress of National Highway -44. Journal of Polymer and Composites. 2024; ():-. Available from: https://journals.stmjournals.com/jopc/article=2024/view=0
References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_123663’);});Edit
1. Fakhri M, Dezfoulian RS, Golroo A, Makkiabadi B. Developing an approach for measuring the intensity of cracking based on geospatial analysis using GIS and automated data collection system. Int J Pavement Eng [Internet]. 2021;22(5):582–96. Available from: https://doi.org/10.1080/10298436.2019.1628961 2. Weng X, Huang Y, Wang W. Segment-based pavement crack quantification. Autom Constr.2019;105(November 2018). 3. Leonardi G, Barrile V, Palamara R, Suraci F, Candela G. 3D mapping of pavement distresses using an Unmanned Aerial Vehicle (UAV) system. Smart Innov Syst Technol. 2019;101:164–71. 4. Gandhi S, Roji SSS, Motta M, Nalawade RRD, Allah Khan M, Palanisamy S. Analysis of potential incorporation of waste into asphalt pavements. Mater Today Proc [Internet]. 2024;(February).Available from: https://doi.org/10.1016/j.matpr.2024.05.097 5. Zhang A, Wang KCP, Li B, Yang E, Dai X, Peng Y, et al. Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network. Comput Civ Infrastruct Eng. 2017;32(10):805–19. 6. Abulizi N, Kawamura A, Tomiyama K, Fujita S. Measuring and evaluating of road roughness conditions with a compact road profiler and ArcGIS. J Traffic Transp Eng (English Ed [Internet].2016;3(5):398–411. Available from: http://dx.doi.org/10.1016/j.jtte.2016.09.004 7. Veeramanickam MR., Mohanapriya M. IOT enabled Futures Smart Campus with effective E- Learning : i-Campus. Int J Eng Technol. 2014;2(3):14–20. 8. Nautiyal A, Sharma S. Condition Based Maintenance Planning of low volume rural roads using GIS. J Clean Prod [Internet]. 2021;312(May):127649. Available from: https://doi.org/10.1016/j.jclepro.2021.127649 9. Kennedy J, Shalaby A, Van Cauwenberghe R. Dynamic segmentation of pavement surface conditiondata. 2000 Annu Conf Abstr – Can Soc Civ Eng. 2000;273.
Journal of Polymer and Composites
| Volume | |
| Received | 23/04/2024 |
| Accepted | 17/07/2024 |
| Published | 06/12/2024 |