A new perspective in life cycle assessment enhanced by principal component analysis: a comperative case study of facade materials for sports and event building envelope

Sustainable architectural design increasingly recognizes critical role of facade systems in environmental and energy performance. This study investigates environmental and energy performance of five facade systems; cement board, aluminum composite panel and wooden panel cladding; PVC tensile systems; inflated ETFE systems within LCA framework integrated with AI-assisted PCA. Research addresses limitation of conventional LCA studies, which are often constrained by high-dimensional datasets, interdependent variables, and limited interpretability of complex environmental indicators. The aim is to develop cradle-to-grave comparative framework that enhances interpretability of multi-criteria LCIA (based on LCI) data through AI-assisted dimensionality reduction, enables consistent comparison of facade systems. The proposed LCA-PCA integration with AI transforms multi-stage life cycle data into structured dataset, enabling pattern recognition and holistic interpretation of environmental impacts, representing a novel contribution to facade sustainability assessment. The inclusion of ETFE inflated systems is evaluated in comparison with other facade systems within a unified cradle-to-grave framework, providing more comprehensive sustainability assessment. Findings show that production stage generates the highest environmental impacts across all systems, followed by the use stage. Cement board and aluminum composite claddings exhibit the highest environmental burdens, while wooden panel claddings show low and balanced performance. ETFE inflated systems achieves the best overall sustainability performance despite energy demand during use, whereas PVC tensile shows higther environmental impacts in production, end-of-life stages. PCA results reveal clustering among materials and strong covariance relationships between LCA variables. In conclusion, the integration of AI-assisted LCA-PCA provides robust and scalable framework for comprehensive facade system evaluation across life cycle.