A multidimensional assessment of construction machinery noises based on perceptual attributes and psychoacoustic parameters

Abstract

Traditional decibel-based measures in predicting annoyance from construction activities are limited to reflect high acoustic variability of construction machinery noises. Hence, a multidimensional approach based on perceptual attributes and psychoacoustic parameters is proposed. In-situ audio-visual recordings of 16 construction machinery in operation were evaluated subjectively on both perceived annoyance and a 12-item semantic differential perceptual attribute scale. The 16 machinery noises formed three clusters based on four perceptual components (Incisiveness, Strength, Intermittency, and Periodicity) derived via principal component analysis of the perceptual attributes. Notably, individual perceptual components strongly correlate with mean values of psychoacoustic parameters (loudness, sharpness, roughness, and fluctuation strength) over time, which we use to develop an annoyance model for construction noise. Both loudness and fluctuation strength were critical parameters to discriminate between clusters. The model can be used to automatically categorize construction noises by cluster and manage it based on known cluster characteristics.