APPLICATION OF MACHINE LEARNING IN STRUCTURAL ANALYSIS: ENHANCING ENERGY CONSERVATION AND SUSTAINABLE BUILDING PRACTICES IN CIVIL ENGINEERING

Abstract

The increasing global emphasis on sustainability and energy efficiency has profoundly influenced the evolution of civil engineering practices, particularly in the domain of structural analysis and design. This research investigates the transformative role of Machine Learning (ML) in optimizing structural performance while promoting energy conservation and sustainable building development. The study explores how advanced ML algorithms, such as artificial neural networks, support vector machines, and ensemble learning models, can enhance predictive accuracy, automate complex analyses, and enable adaptive decision-making in structural engineering systems. By integrating these computational tools with conventional analytical models, engineers can efficiently evaluate material behaviors, load responses, and lifecycle performance with significantly reduced computational time and improved precision. The research adopts a multi-dimensional approach, combining theoretical modeling with practical simulations to evaluate the efficiency of ML-driven frameworks in structural energy optimization. Key parameters such as thermal performance, embodied energy, material utilization, and structural resilience are analyzed using large datasets derived from building performance monitoring and environmental data acquisition systems. The application of supervised and unsupervised learning models enables the identification of optimal design configurations that balance energy efficiency with structural safety. Moreover, the use of data-driven approaches facilitates the detection of structural anomalies, supports predictive maintenance, and extends the operational lifespan of built environments, thereby contributing to sustainable construction practices. The findings reveal that ML integration in structural analysis not only enhances analytical capabilities but also fosters real-time adaptability in design processes. By learning from diverse datasets and environmental interactions, these models provide dynamic insights into material degradation, load redistribution, and energy performance under variable climatic conditions. This adaptive intelligence paves the way for intelligent infrastructures that are self-optimizing, resource-efficient, and aligned with sustainable development goals. Furthermore, the study emphasizes that the adoption of ML-based frameworks encourages cross-disciplinary collaboration between data science and structural engineering, establishing a new paradigm for smart and sustainable construction management. In conclusion, the incorporation of machine learning into structural analysis represents a crucial advancement toward a more energy-conscious and environmentally responsible civil engineering landscape. The research underscores the necessity of integrating data-driven intelligence into every stage of the design, assessment, and maintenance lifecycle to achieve long-term sustainability and resilience in the built environment.