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

Abstract

The integration of data analytics and machine learning (ML) techniques in civil and mechanical engineering has revolutionized complex problem-solving by enabling more accurate modeling, prediction, and optimization. This review explores the current landscape of data-driven approaches and their transformative role in addressing challenges such as structural health monitoring, predictive maintenance, traffic and load forecasting, material behavior modeling, and system optimization. In civil engineering, techniques like regression analysis, support vector machines, and deep learning are being applied for infrastructure assessment, construction risk analysis, and smart city development. In mechanical engineering, ML algorithms are used for fault detection, thermal system modeling, design optimization, and robotics. The paper highlights the importance of data quality, feature selection, and model interpretability, and discusses the integration of Internet of Things (IoT) and real-time data streams. Furthermore, it emphasizes hybrid modeling approaches that combine physics-based simulations with machine learning to enhance prediction accuracy and computational efficiency. Challenges such as data scarcity, model generalization, and the need for interdisciplinary collaboration are also discussed. This review concludes by identifying future trends and research opportunities that could advance the application of AI-driven technologies in engineering, fostering smarter, safer, and more efficient systems.