Abstract

The rapid deterioration of roads creates major obstacles that affect public security, urban movement capacity, and economic durability. Standardized road maintenance operations depend heavily on human inspections that prove time-consuming while being labour-intensive and subject to human mistakes. Implementing Augmented Reality (AR) with Point Cloud Data represents a smart approach that improves delivery efficiency in road maintenance procedures for modern cities. AR enables real-time visual content and interactive displays through this research, yet Point Cloud Data delivers detailed, precise road inspections through high-definition three-dimensional mapping. These technologies are evaluated for their usability, effectiveness, and adoption barriers in research that advance digital transformation knowledge for infrastructure management. Qualitative and quantitative research methods collected data to provide complete feedback about the proposed technologies. A structured questionnaire (with close- and open-ended questions) was first administered to road maintenance professionals to gather information about their present procedures and difficulties and understand their perspectives regarding AR and Point Cloud Data systems. The study conducted pilot tests and field demonstrations, bringing Point Cloud Data and AR-based solutions together to validate the technology in real-world conditions. Quantitative data analysis involved applying descriptive statistics and correlation methods with open-ended answers evaluated through thematic pattern identification. Tests against traditional workflows were carried out to determine how AR technologies performed better than conventional methods in maintenance operations. The evaluation results show robust endorsement for AR and Point Cloud Data systems in road infrastructure maintenance. These technologies enhance how defects appear to operators, provide clear step-by-step instructions, and make maintenance procedures more efficient. Real-time system overlays in AR applications received high praise because they improved situational awareness while diminishing inspection outcomes and manual documentation requirements. Point Cloud Data delivered accurate road condition mapping as its main benefit, which helped predictive maintenance teams and extended further into asset management practices. Multiple barriers emerged during the assessment process because existing infrastructure management tools proved difficult to merge with this new technology while training requirements proved extensive and the total expenditure proved high. The research reveals that Point Cloud Data and AR show strong implementation potential but need systematic strategic deployment. This research validates that AR and Point Cloud Data systems possess transformative capabilities for road maintenance by improving the precision of work, operational speed, and managerial choices for maintenance staff. The barriers to large-scale deployment include technical connection problems, computing requirements, and budgetary restrictions. The implementation speed and safety from AR-based solutions require more research on machine learning algorithms, data processing optimization, and total cost assessment for extended maintenance periods. Additional research should work to expand the scale and standardize information systems for AR and Point Cloud Data implementation within various road infrastructure management ecosystems.

Library of Congress Subject Headings

Roads--Maintenance and repair--Data processing; Augmented reality; Computer vision; Optical pattern recognition

Publication Date

5-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Khalil Al Hussaeni

Campus

RIT Dubai

Plan Codes

PROFST-MS

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