Yu Lei, Kang Jian
School of Architecture, University of Sheffield, Western Bank, Sheffield, United Kingdom.
J Acoust Soc Am. 2009 Sep;126(3):1163-74. doi: 10.1121/1.3183377.
This research aims to explore the feasibility of using computer-based models to predict the soundscape quality evaluation of potential users in urban open spaces at the design stage. With the data from large scale field surveys in 19 urban open spaces across Europe and China, the importance of various physical, behavioral, social, demographical, and psychological factors for the soundscape evaluation has been statistically analyzed. Artificial neural network (ANN) models have then been explored at three levels. It has been shown that for both subjective sound level and acoustic comfort evaluation, a general model for all the case study sites is less feasible due to the complex physical and social environments in urban open spaces; models based on individual case study sites perform well but the application range is limited; and specific models for certain types of location/function would be reliable and practical. The performance of acoustic comfort models is considerably better than that of sound level models. Based on the ANN models, soundscape quality maps can be produced and this has been demonstrated with an example.
本研究旨在探讨在设计阶段使用基于计算机的模型来预测城市开放空间中潜在用户的声景质量评价的可行性。利用来自欧洲和中国19个城市开放空间的大规模实地调查数据，对各种物理、行为、社会、人口统计学和心理因素在声景评价中的重要性进行了统计分析。然后在三个层面上探索了人工神经网络（ANN）模型。结果表明，对于主观声级和声学舒适度评价，由于城市开放空间中复杂的物理和社会环境，针对所有案例研究地点的通用模型不太可行；基于单个案例研究地点的模型表现良好，但应用范围有限；针对特定类型位置/功能的特定模型将是可靠且实用的。声学舒适度模型的性能明显优于声级模型。基于人工神经网络模型，可以生成声景质量地图，并通过一个例子进行了演示。
