Conceptual Framework for Smart Sensor–driven Predictive Maintenance in Infrastructure Management
Habib Shehu
*
Department of Information Technology and Systems, Kampala International University, Kampala, Uganda.
Omolayo Sunday
Department of Management with Data Analytics, BPP University, United Kingdom.
Damilola Ayodele Ojo
College of Business, Missouri State University, Springfield, Missouri, United States of America.
Oladele Nicholas Afolayan
Vascular and Diabetics Team, Salisbury NHS Trust Fund, Salisbury, United Kingdom.
Temitope Anthony Adebanjo
Department of Marketing Intelligence and Analytics, Regenesys Education, Johannesburg, South Africa.
Eric Iyere Eromosele
Department of Engineering Management, University of Kansas, Kansas, United States of America.
Amienye Babatunde Omo Enabulele
College of Business, Missouri State University, Springfield, Missouri, United States of America.
Oghenetega A. Okpoko
Engineering Standards and Applications, ComEd, IL, United States of America.
Francis Chukwudalu Okeke
Department of Computer Science, C. K. Tedam University of Technology and Applied Sciences (Formerly University for Development Studies), Navrongo, Ghana.
Benjamin Osaze Enobakhare
Peterbilt Motors, Denton, Texas, United States of America.
*Author to whom correspondence should be addressed.
Abstract
Smart sensors, predictive analytics, and machine learning (ML) are reshaping how infrastructure is managed by enabling real-time, data-driven decision-making. Rather than relying on reactive maintenance, these technologies support proactive strategies that improve efficiency, reduce costs, and enhance sustainability. Predictive models based on sensor data can anticipate potential failures, allowing timely interventions that extend the lifespan of infrastructure systems. Machine learning enhances these models by improving accuracy and adaptability in predicting maintenance needs, detecting anomalies, and optimizing system performance. This article provides a conceptual framework and narrative review of the integration of these technologies across key domains such as energy, transportation, and structural health monitoring. It highlights opportunities for improving safety, efficiency, and environmental outcomes while acknowledging persistent challenges related to data quality, scalability, and security. The discussion emphasizes the need for continued research on sensor networks, edge computing, and AI-driven decision support to advance smart infrastructure management and prepare for future applications in smart cities and large-scale industrial systems.
Keywords: Predictive analytics, machine, learning, smart sensor, infrastructure, management