The Impact of Artificial Intelligence on Engineering Applications

Engineering systems globally face mounting demands for efficiency, sustainability, and resilience due to climate change, aging infrastructure, and limited resources. Locally, inefficiencies-such as reactive maintenance, inadequate design, and insufficient real-time monitoringdrive up costs and system failures. Traditional engineering methods, largely manual and rule-based, lack flexibility and predictive insight. Although artificial intelligence offers substantial promise, its integration is hindered by complexity, expense, and a scarcity of domain expertise, revealing a pressing research gap. To address this, a modular AI-based framework is proposed incorporating machine learning, generative design, predictive analytics, and sensor data. This framework aims to advance automation in design, enable predictive maintenance, optimize energy use, and enhance structural health monitoring. The anticipated result is greater efficiency, reduced downtime, and improved reliability, ultimately transforming conventional engineering into a smart, data-driven discipline suited to contemporary challenges.