MACHINE LEARNING APPROACHES FOR PREDICTIVE ANALYTICS IN ENGINEERING APPLICATIONS

Predictive analytics has become a cornerstone of modern engineering systems, enabling data-driven decision-making, improved reliability, and optimized performance. Machine learning (ML) techniques offer powerful tools to model complex, nonlinear, and high-dimensional engineering data
that traditional analytical methods often fail to capture. This chapter presents a comprehensive overview of machine learning approaches for predictive analytics across diverse engineering applications such as manufacturing, mechanical systems, civil infrastructure, energy systems, and transportation. Quantitative performance indicators, including prediction accuracy (85–98%), mean absolute error (MAE reduced by 30–55%), and system downtime reduction (up to 40%), are discussed to demonstrate the effectiveness of ML-based models. The chapter outlines data acquisition, preprocessing, model development, validation strategies,
and deployment considerations. Challenges such as data scarcity, model interpretability, and computational complexity are also addressed. The presented methodologies align closely with sustainable development goals by promoting resource efficiency, resilient infrastructure, and intelligent industrial systems.