A Surrogate Machine Learning Method for Real-time Indoor Acoustic Analysis: A Case Study in an Educational Building
Author(s)
Hossein Tabatabaei Manesh, Mohammad
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Abstract
Achieving optimal speech intelligibility in educational settings is crucial for effective learning. Designers face challenges due to the diversity of building regulations, which define acoustic comfort in various ways. Objective acoustic parameters such as Definition (D50) and the Speech Transmission Index (STI) are pivotal in assessing acoustic quality tailored to a room’s function, with STI being especially indicative of speech intelligibility. To address the need for quick, accurate predictions of D50 and STI values across classroom areas, this research employs a surrogate machine learning (ML) approach. Our methodology involves simulating acoustic properties in a single educational room using the Pachyderm plugin within Grasshopper to analyze D50 at three key frequencies (125, 1000, and 4000 Hz), as well as the overall STI. We utilize the CatBoost algorithm as a surrogate model to predict the acoustic performance of individual sensors. The effectiveness of our model is assessed using the R2 score, Mean Absolute Error (MAE), and Mean Squared Error (MSE) for individual sensors, along with Pearson correlation for comprehensive sensor analysis. The results demonstrate the high performance and potential of this surrogate ML approach in generating detailed and accurate acoustic heatmaps, thus ensuring enhanced acoustic comfort in educational environments. This method provides a cost-effective and efficient solution for real-time acoustic assessment, paving the way for improved educational building design.
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Date
2025-03
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Text
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Proceedings
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