Applied IT & Engineering

Information and engineering sciences | Online ISSN 3068-0115
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RESEARCH ARTICLE   (Open Access)

Adoption and Effectiveness of AI-Enabled Digital Inspection Systems for Infrastructure Safety and Construction Quality Assurance

Nipa Akter 1*

+ Author Affiliations

Applied IT & Engineering 1 (1) 1-8 https://doi.org/10.25163/engineering.1110699

Submitted: 26 December 2022 Revised: 06 March 2023  Published: 14 March 2023 


Abstract

Background: Infrastructure inspection has long depended on manual, experience-driven approaches, yet increasing structural complexity and safety demands are gradually exposing the limitations of such methods. In recent years, artificial intelligence (AI) and digital technologies—particularly drones, computer vision, and sensor-based systems—have been proposed as transformative tools. Still, their real-world adoption remains uneven, and professional perceptions of their effectiveness are not fully understood.Methods: This study employed a cross-sectional survey of 160 infrastructure professionals across the United States, including engineers, inspectors, managers, and safety officers. The survey explored technology adoption patterns, perceived effectiveness, operational benefits, and implementation barriers. Data were analyzed using descriptive statistics, Chi-square tests, and logistic regression to identify factors associated with AI system acceptance and perceived performance.Results: Drone-based inspection (25%), computer vision (21%), and BIM-integrated systems (19%) emerged as the most widely adopted technologies. A majority of respondents (62.5%) rated AI-based inspection as effective or very effective. Key perceived benefits included improved defect detection (26.3%), enhanced worker safety (21.2%), and faster inspection processes (18.8%). Statistical analysis indicated that technology adoption (β = 0.47, p = 0.041), professional experience (p = 0.049), digital training (p = 0.047), and organizational support (p = 0.043) significantly influenced perceived effectiveness.Conclusion: AI-enabled inspection systems show clear promise, yet their success appears to depend less on technological capability alone and more on organizational readiness, training, and practical integration into existing workflows.

Keywords: Artificial intelligence; infrastructure inspection; digital construction; quality assurance; technology adoption

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