AI-Driven Eco-Responsive Building Systems: Machine Learning-Based Adaptive Building Environment and Ecological Community Co-evolution Research

Abstract

Integrating artificial intelligence with ecological architecture represents a paradigm shift toward sustainable built environments that dynamically respond to ecological conditions. This meta-analysis examines the emerging field of AI-driven eco-responsive building systems, focusing on machine learning algorithms that enable real-time adaptation of building morphology, material performance, and spatial configurations based on ecological data analysis. Through a systematic review of 127 peer-reviewed studies published between 2019 and 2024, this research identifies key technological frameworks, performance metrics, and implementation challenges in developing "intelligent ecological building brains." The analysis reveals that deep learning-based predictive models can achieve up to 35% improvement in energy efficiency and 42% reduction in environmental impact compared to conventional building systems. The study establishes a comprehensive taxonomy of AI-driven ecological responsiveness, categorizing systems into four primary types: morphological adaptation, material phase-change, spatial reconfiguration, and ecosystem integration. Key findings indicate that convolutional neural networks (CNNs) and long short-term memory (LSTM) networks better predict ecological patterns and building responses. However, data integration, computational complexity, and long-term system reliability remain significant challenges. This research contributes to the emerging "predictive ecological architecture" discipline by providing a theoretical framework for AI-ecosystem-building co-evolution and identifying critical research directions for future smart city development.