Data Analytics and Machine Learning Techniques for Complex Problem Solving in Civil and Mechanical Engineering

Abstract

The increasing complexity of civil and mechanical engineering systems has necessitated the adoption of advanced data analytics and machine learning techniques for effective problem solving and decision-making. Traditional analytical and numerical methods, while foundational, often struggle to manage large-scale, nonlinear, and uncertain datasets generated by modern infrastructure and mechanical systems. This review paper presents a comprehensive analysis of data-driven methodologies employed across civil and mechanical engineering domains, with emphasis on machine learning models such as regression algorithms, support vector machines, artificial neural networks, ensemble learning, and deep learning architectures. Applications including structural performance prediction, traffic modeling, energy system optimization, fault diagnosis, and predictive maintenance are critically examined. The paper further discusses data acquisition strategies, feature engineering, model validation, and interpretability challenges associated with engineering datasets. Emerging trends such as physics-informed machine learning, hybrid modeling, and real-time analytics are explored as solutions to existing limitations. By synthesizing recent research and practical implementations, this review highlights the transformative potential of data analytics and machine learning in addressing complex engineering problems and shaping the future of intelligent infrastructure and mechanical systems.