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VOL. 10, ISSUE 3 (2025)
AI-driven school and college infrastructure maintenance for predictive repairs and safety
Authors
Prakash Balasaheb Mande, Ashwini Haushiram Raut
Abstract
AI-driven infrastructure maintenance in
schools and colleges is revolutionizing facility management by enabling
predictive repairs and enhancing safety. Traditional maintenance approaches
often rely on reactive or scheduled interventions, which can lead to unexpected
failures, costly repairs, and safety hazards. Leveraging artificial
intelligence (AI), predictive maintenance utilizes real-time data from IoT
sensors, historical maintenance records, and machine learning algorithms to
detect early signs of equipment deterioration, structural weaknesses, and
potential safety risks. AI-powered systems analyze environmental conditions,
wear patterns, and energy consumption to predict failures before they occur,
allowing for timely interventions that reduce downtime, optimize resource
allocation, and enhance operational efficiency. Additionally, automated anomaly
detection improves the safety of students and staff by identifying hazardous
conditions, such as gas leaks, electrical faults, or unstable structures,
before they pose serious threats. This proactive approach minimizes repair
costs, extends the lifespan of infrastructure, and ensures a secure learning
environment. By integrating AI-driven maintenance strategies, educational
institutions can transition from reactive to predictive models, fostering
sustainability, cost-effectiveness, and enhanced safety in school and college
campuses.
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Pages:135-138
How to cite this article:
Prakash Balasaheb Mande, Ashwini Haushiram Raut "AI-driven school and college infrastructure maintenance for predictive repairs and safety". International Journal of Advanced Scientific Research, Vol 10, Issue 3, 2025, Pages 135-138
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