Energy-Based Analysis of LCA and LCC for Selection of Residential Building Exterior Walls in Different Climate Conditions in Egypt

Buildings account for nearly 40% of global energy consumption and one-third of greenhouse gas emissions. Exterior walls significantly influence building energy performance and, consequently, Life Cycle Cost (LCC) and Life Cycle Assessment (LCA). However, most previous studies focus on specific case studies and lack generalizability across varying building characteristics. This study proposes an integrated LCC–LCA framework for selecting optimal exterior wall systems for residential buildings in Egypt, incorporating parametric modeling and machine learning to predict energy consumption. The framework considers essential building characteristics, including location, orientation, dimensions, and window properties. Initially, commonly used exterior wall configuration options in Egypt are modeled within a representative residential building and parametrically simulated to generate a comprehensive database of energy consumption. This database is then used to train an artificial neural network (ANN) model to predict the energy performance of alternative wall systems. Based on the predicted energy demand, LCC and LCA indicators are calculated. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to identify the optimal wall option. The proposed framework is validated using case study buildings. The findings demonstrate that the proposed model provides a reliable and robust approach for exterior wall selection in residential buildings.