AI-Driven Multi-Criteria Decision-Making Framework for Acoustic Performance Prediction and Optimization in Music Venue Design

Music-venue acoustic design requires the simultaneous consideration of reverberation, clarity, definition, and sound distribution. This study developed an artificial intelligence (AI)-based prediction-to-decision framework integrating the Dynamic Beluga Whale Optimizer (DBWO), Light Gradient Boosting Machine (LightGBM), and Analytic Hierarchy Process (AHP). Wavelet-based impulse-response denoising and leakage-controlled predictor standardisation preceded four separate regression models for reverberation time estimated from a 30 dB decay (T30), early decay time (EDT), clarity index (C80), and definition index (D50). DBWO tuned the LightGBM hyperparameters, after which predicted physical values were converted into acoustically contextualised utility scores and combined using AHP weights. On the independent test venues, DBWO–LightGBM achieved lower prediction errors than the benchmark models across most acoustic indicators and frequency bands. The balanced reflection–absorption alternative ranked first, and the ranking remained stable under AHP weight-sensitivity analysis. The framework therefore provides transparent decision support for predefined acoustic alternatives without claiming to generate venue geometry or replace detailed acoustic simulation.