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035 a| (Sirsi) o1257306768;

035 a| (OCoLC)1257306768;

049 a| EREE;

090 a| TE250;

100 1 a| Chawla, Vinay K.,e| author.?| UNAUTHORIZED;

245 10 a| Automated pavement condition assessment using Unmanned Aerial Vehicles (UAVs) and Convolutional Neural Network (CNN) /c| by Vinay K. Chawla.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2021.;

300 a| 80 pages :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 3.044 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2021.;

500 a| Presented to the faculty of the Department of Construction Management;

500 a| Advisor: Carol Massarra;

500 a| Title from PDF t.p. (viewed September 29, 2021).;

520 3 a| Assessing pavement condition is extremely essential in any effort to reduce future economic losses and improve the structural reliability and resilience. Data resulting from pavement condition assessment are used as a record of infrastructure performance and as a major component to assess their functionality and reliability. However, pavement condition assessment is challenging because of the cost associated with assessment, safety issues, and the accessibility restrictions, especially after natural hazards. This research aims to develop an automated classification model to rapidly classify pavement distresses. High-resolution aerial images representing alligator and longitudinal cracks are collected for flexible pavements using Unmanned Aerial Vehicle (UAV) around East Carolina University (ECU) campus. The image classification model is developed using Convolutional Neural Network (CNN), a deep learning approach. The results of the developed model indicate an accuracy of 96.7% in classifying the two categories of pavement distress. The developed model was further tested on a set of test images yielding a prediction accuracy of 90%. The methodology behind the developed model will help to reduce the need for on-site presence, increase safety, and assist emergency response managers in deciding the safest route to take after hurricane events. Additionally, application of the model will enable transportation engineers in rapidly assessing the pavement damage, aid in making quick decisions for road rehabilitation and recovery and devise a restoration or repair plan.;

504 a| Includes bibliographical references.;

650 0 a| Pavementsx| Quality control.=| ^A102073;

650 0 a| Pavementsx| Data processingx| Management.=| ^A102073;

650 0 a| Drone aircraft.=| ^A241920;

653 a| Pavement Distress Classification;

653 a| Convolutional Neural Network;

700 1 a| Massarra, Carol,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Construction Management.=| ^A1228087;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/9121;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5636920;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5636920-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 6/22/2021x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5636920-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 6/22/2021x| ETDz| HERESOURCE;

Automated pavement condition assessment using Unmanned Aerial Vehicles (UAVs) and Convolutional Neural Network (CNN) / by Vinay K. Chawla.

Author/creator	Chawla, Vinay K. author.
Other author	Massarra, Carol, degree supervisor.
Other author	East Carolina University. Department of Construction Management.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2021.
Description	80 pages : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Pavements--Quality control. Pavements--Data processing. --Management. Drone aircraft.

Summary	Assessing pavement condition is extremely essential in any effort to reduce future economic losses and improve the structural reliability and resilience. Data resulting from pavement condition assessment are used as a record of infrastructure performance and as a major component to assess their functionality and reliability. However, pavement condition assessment is challenging because of the cost associated with assessment, safety issues, and the accessibility restrictions, especially after natural hazards. This research aims to develop an automated classification model to rapidly classify pavement distresses. High-resolution aerial images representing alligator and longitudinal cracks are collected for flexible pavements using Unmanned Aerial Vehicle (UAV) around East Carolina University (ECU) campus. The image classification model is developed using Convolutional Neural Network (CNN), a deep learning approach. The results of the developed model indicate an accuracy of 96.7% in classifying the two categories of pavement distress. The developed model was further tested on a set of test images yielding a prediction accuracy of 90%. The methodology behind the developed model will help to reduce the need for on-site presence, increase safety, and assist emergency response managers in deciding the safest route to take after hurricane events. Additionally, application of the model will enable transportation engineers in rapidly assessing the pavement damage, aid in making quick decisions for road rehabilitation and recovery and devise a restoration or repair plan.
General note	Presented to the faculty of the Department of Construction Management
General note	Advisor: Carol Massarra
General note	Title from PDF t.p. (viewed September 29, 2021).
Dissertation note	M.S. East Carolina University 2021.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Automated pavement condition assessment using Unmanned Aerial Vehicles (UAVs) and Convolutional Neural Network (CNN) / by Vinay K. Chawla.

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