Civil Engineering ETDs
Publication Date
Fall 12-31-2025
Abstract
This dissertation focuses on improving the accuracy and consistency of structural crack inspections by integrating AI, Augmented Reality (AR), and human expertise into a unified inspection framework. Traditional visual inspections often suffer from variability due to inspector subjectivity, leading to inconsistent maintenance decisions and resource allocation. To address this challenge, the research introduces an AI–AR human-centered (AHI) inspection methodology that enables near–real-time, standardized, and more reliable data collection. The AHI system overlays AI-driven analyses directly into the inspector’s field of view, supporting accurate crack measurement, and minimizing human error. The framework also facilitates consistent information sharing among inspectors, managers, and stakeholders across time and space. Additionally, the dissertation demonstrates that AHI reduces inspector neck fatigue and improves ergonomics during fieldwork. By enabling in-field report generation and immediate access to analytical insights, the proposed approach significantly enhances inspection reliability, reduces variability, and supports more informed maintenance and funding decisions.
Keywords
Artificial Intelligence (AI), Augmented Reality (AR), Structural Inspection, Computer Vision, Human-in-the-Loop Framework
Document Type
Dissertation
Language
English
Degree Name
Civil Engineering
Level of Degree
Doctoral
Department Name
Civil Engineering
First Committee Member (Chair)
Dr. Fernando Moreu
Second Committee Member
Dr. Rafiqul Tarefder
Third Committee Member
Dr. Sreenivas Alampalli
Fourth Committee Member
Dr. Victor Law
Recommended Citation
Mohammadkhorasani, Ali. "A Novel Augmented Reality Human-in-the-Loop Methodology for Enhancing Structural Inspections." (2025). https://digitalrepository.unm.edu/ce_etds/368