Citation :

S. Mohamed Kasim, 2025. "Artificial Intelligence Predictive Models for Infrastructure Wear and Maintenance Need" International Journal of Computer Science & Information Technology  Volume 1, Issue 1: 22-28.

Abstract :

Growing demand for strong and efficient infrastructure has led artificial intelligence (AI) to be implemented into predictive maintenance models. Artificial intelligence-driven predictive analytics allow to identify wear and possible flaws in infrastructure systems, therefore improving maintenance schedule and reducing costs. Predictive maintenance using artificial intelligence approaches including sensor-based Internet of Things (IoT) integrations, deep learning (DL), and machine learning (ML) generates quite accurate prediction models. These models find trends, anomalies, and forecast breakdowns before they develop by means of real-time and historical data analysis, therefore supporting proactive maintenance programs.This paper investigates the function of artificial intelligence in predictive maintenance by means of several AI-driven approaches—including supervised and unsupervised learning, neural networks, and reinforcement learning—underlines the need of data collecting, processing, and integration as well as the challenges applying artificial intelligence-based predictive maintenance in actual infrastructure systems.Case studies from several disciplines, including transportation, energy, and smart city management, also show how effectively artificial intelligence based predictive maintenance performs. Among these are wind farms using AI-based analytics to optimise turbine performance, train systems using AI-powered sensors to monitor track conditions, and bridges employing deep learning algorithms for structural health monitoring. These pragmatic applications demonstrate how artificial intelligence may increase operational costs, enhance safety, and extend the lifetime of infrastructure components.Although artificial intelligence-based predictive maintenance has advantages, data security and privacy issues, computational resource constraints, and data quality issues challenge it. This paper also addresses future prospects including improvements in artificial intelligence explainability, federated learning, and quantum computing which are expected to increase predictive accuracy and strengthen infrastructure resilience.Ultimately, artificial intelligence-driven predictive maintenance signals a paradigm revolution in infrastructure management, therefore facilitating proactive, data-driven decision-making. As artificial intelligence technologies grow so as to ensure the lifetime and sustainability of vital infrastructure systems, their inclusion into infrastructure maintenance will become more vital.

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Keywords :

Artificial Intelligence; Predictive Maintenance; Machine Learning; Deep Learning; Infrastructure Wear; Smart Cities; Iot; Data Analytics; Structural Health Monitoring; Predictive Modelling.