| dc.description.abstract | The sounds of work activities and equipment operations at a construction site provide critical information regarding construction progress, task performance, and safety issues. The construction industry, however, has not investigated the value of sound data and their applications, which would offer an advanced approach to unmanned management and remote monitoring of construction processes and activities. For analyzing sounds emanating from construction work activities and equipment operations, which generally have complex characteristics that entail overlapping construction and environmental noise, a highly accurate sound classifier is imperative for data analysis. To establish the robust foundation for sound recognition, analysis, and monitoring frameworks, this research study examines diverse classifiers and selects those that accurately identify construction sounds. Employing nine types of sounds from about 100 sound data originating from construction work activities, we assess the accuracy of 17 classifiers and find that sounds can be classified with 93.16% accuracy. A comparison with deep learning technology has been also provided, obtaining results similar to the best ones of the traditional machine learning methods. The outcomes of this study are expected to help enhance advanced processes for audio-based construction monitoring and safety surveillance by providing appropriate classifiers for construction sound data analyses. | |
