## 1 Introduction
In Singapore, a regulatory void for office-specific sanitary facility standards has led to reliance on either broad international codes or personal heuristics. Under the National Environment Agency's (NEA) Code of Practice for Environmental Health 2024 [7], no direct guidance exists for offices, prompting designers to borrow from external sources that may not reflect Singapore's local norms. The Singtel Serangoon TEPL office building project in Singapore addressed this gap by examining cultural practices, workplace habits, and actual occupancy trends to form more precise, context-driven guidelines. This research combined international codes, queueing theory, and region-specific insights. By probing cultural norms and dynamic occupancy, it became clear that existing rules and models sometimes fall short when applied to a local setting. These observationsmove the need for more sophisticated methods, specifically LSTM neural networks, to generate better-informed recommendations. Recent years have seen AI revolutionize problem-solving in facility management. Unlike rigid models, machine learning (ML) algorithms embrace nonlinear interactions and can adjust to new data streams. LSTM neural networks [8,9], designed for sequential inputs, show particular promise when forecasting restroom usage patterns, given their ability to handle time-dependent data and unpredictable behaviors. Building on these advances, this research develops and tests an AI-based framework to optimize sanitary fixture requirements in office buildings in Singapore. Drawing on data from the Singtel Serangoon TEPL building at 1 Serangoon North Avenue 5, Singapore, it aims to build a predictive model that accounts for time-based, demographic, and environmental factors; compare AI-based forecasts against traditional methods; pinpoint critical factors shaping restroom usage; and illustrate practical benefits of the model in a working environment. This research makes several significant contributions to the field of sanitary facility planning. We present a novel LSTM-based neural network architecture specifically designed for sanitary facility optimization. This framework integrates temporal dependencies, occupancy patterns, and environmental factors to provide more accurate predictions than traditional methods. Our approach achieves a 39.26% improvement in mean absolute percentage error compared to conventional queueing theory models, demonstrating significant advancement in prediction accuracy for facility planning. We validate our approach using comprehensive empirical data from the Singtel Serangoon TEPL office building, supplemented by testing on two additional office complexes. This validation demonstrates that our model can achieve prediction accuracies within 2.63-4.76% of actual usage patterns, compared to 12.07-19.05% deviations observed with prescriptive codes. The framework's effectiveness is verified across different building sizes and occupancy profiles, confirming its practical applicability in real-world scenarios. Through systematic sensitivity analysis, we identify and quantify the key factors influencing sanitary facility demand. Our research reveals that total building occupancy (sensitivity coefficient: 0.842), time of day (0.731), and gender ratio (0.695) are the most significant determinants of fixture requirements. These insights enable more precise resource allocation by allowing facility planners to prioritize the most impactful factors. They facilitate gender-specific optimization, address critical gaps in current planning approaches, and help predict and manage peak demand periods more effectively. This analysis provides a scientific basis for dynamic facility management strategies, potentially reducing wait times by up to 40%, while supporting cost-effective design decisions by identifying where investments will have the greatest impact. Our research demonstrates potential resource savings of up to 15% in fixture requirements while maintaining service quality, with a maximum 60-s wait time for 90% of users.