Efficient deep learning-based prediction of acoustic parameters in atrium spaces using BIM files

Abstract

Acoustic simulation tools, although common in the acoustic evaluation of buildings with large atrium space, are costly both in terms of time and resources. This study aims to develop an efficient prediction tool for Reverberation Time (RT) and Sound Pressure Level (SPL) within the atriums using deep learning, with Building Information Modelling (BIM) files as the only input. Initially, a 3-D acoustic simulation model was benchmarked against experimental measurements of RT for an existing building’s atrium, demonstrating an agreement within 15%. Subsequently, BIM files of 60 buildings were used to simulate RT and SPL at various listener locations within their atrium spaces. The BIM files provided essential geometry information, including atrium shape, dimensions, door placements, floor plans, material properties, and sound sources. This dataset was then utilized to train deep learning algorithms, enabling rapid and convenient predictions of RT and SPL for any new building's atrium space. The proposed prediction model serves as a valuable tool for architectural planning and noise regulation during the early design stages. By relying solely on basic building information from the BIM file, this tool obviates the need for time-consuming and computationally expensive simulation software typically used for acoustic evaluations in large atrium spaces.