Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA


A machine-learning framework for acoustic design assessment in early design stages

Reyhane Abarghooie 1, ZahraSadat Zomorodian 1, Mohammad Tahsildoost 1, Zohreh Shaghaghian 2
1 Shahid Beheshti University, Tehran, Iran
2 Texas A&M University, College Station, United States

Abstract: In time-cost scale model studies, predicting acoustic performance by using simulation methods is a commonly used method that is preferred. In this field, building acoustic simulation tools are complicated by several challenges, including the high cost of acoustic tools, the need for acoustic expertise, and the time-consuming process of acoustic simulation. The goal of this project is to introduce a simple model with a short calculation time to estimate the room acoustic condition in the early design stages of the building. This paper presents a working prototype for a new method of machine learning (ML) to approximate a series of typical room acoustic parameters using only geometric data as input characteristics. A novel dataset consisting of acoustical simulations of a single room with 2916 different configurations are used to train and test the proposed model. In the stimulation process, features that include room dimensions, window size, material absorption coefficient, furniture, and shading type have been analysed by using Pachyderm acoustic software. The mentioned dataset is used as the input of seven machine-learning models based on fully connected Deep Neural Networks (DNN). The average error of ML models is between 1% to 3%, and the average error of the new predicted samples after the validation process is between 2% to 12%.
Keywords: Room acoustic, Parametric acoustic simulation, Machine Learning, Deep Neural Network
Pages: 1548 - 1555