AI-Driven Optimization of Sanitary Facilities in Office Buildings: A Machine Learning Approach Using LSTM Neural Networks

This study introduces an AI-based approach to planning sanitary facilities in buildings, with a focus on Long Short-Term Memory (LSTM) neural networks. By examining factors like occupancy patterns, daily fluctuations, and building-specific characteristics, the model delivers datadriven guidance for fixture allocation. Validation was carried out using empirical data from a Singaporean office building, resulting in strong predictive performance and improved operational efficiency. The LSTM model surpassed traditional methods, with a 39.26% boost in Mean Absolute Percentage Error (MAPE) over queueing theory. In a real-world scenario, its predictions were within 2.63%–4.76% of actual needs, whereas prescriptive codes deviated by 12.07%–19.05%. Sensitivity analysis showed that total occupancy, time of day, and gender ratio exerted the greatest influence, enabling gender-specific recommendations. The findings also indicate a potential 15% cut in overall fixtures with no compromise in service quality, providing considerable cost and space savings. By uniting advanced computational modeling with real data, this research demonstrates how AI can elevate sanitary facility planning toward evidence-based decision-making, efficient resource use, and higher user satisfaction. The results may influence policy, regulatory standards, and performance oriented design in contemporary buildings.