Han Lim Ming, Haron Zaiton, Yahya Khairulzan, Bakar Suhaimi Abu, Dimon Mohamad Ngasri
Faculty of Civil Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia.
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia.
PLoS One. 2015 Apr 15;10(4):e0120667. doi: 10.1371/journal.pone.0120667. eCollection 2015.
Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces.
战略噪声映射为噪声影响评估和噪声消减提供了重要信息。然而，在动态、复杂的工作环境中生成可靠的战略噪声映射是困难的。本研究提出采用随机游走方法作为一种新的随机技术来模拟噪声映射并预测工作场所的噪声暴露水平。开发了一个随机模拟框架和软件，即RW-eNMS，以促进随机游走方法在噪声映射预测中的应用。该框架考虑了机器运行的随机性和复杂性以及噪声排放水平。此外，它还评估了噪声对工人和周围环境的影响。为了进行数据验证，开展了三个案例研究，以检验预测数据的准确性，并确定该方法的效率和有效性。结果表明预测结果具有很高的准确性，大多数绝对差值小于2分贝；此外，预测的噪声剂量大多在测量范围内。因此，随机游走方法在处理环境噪声方面是有效的。它可以预测战略噪声映射，以促进工作场所的噪声监测和噪声控制。
