Sound-Based Construction Activity Monitoring with Deep Learning

Abstract

Automated construction monitoring assists site managers in managing safety, schedule, and productivity effectively. Existing research focuses on identifying construction sounds to determine the type of construction activity. However, there are two major limitations: the inability to handle a mixed sound environment in which multiple construction activity sounds occur simultaneously, and the inability to precisely locate the start and end times of each individual construction activity. This research aims to fill this gap through developing an innovative deep learning-based method. The proposed model combines the benefits of Convolutional Neural Network (CNN) for extracting features and Recurrent Neural Network (RNN) for leveraging contextual information to handle construction environments with polyphony and noise. In addition, the dual threshold output permits exact identification of the start and finish timings of individual construction activities. Before training and testing with construction sounds collected from a modular construction factory, the model has been pre-trained with publicly available general sound event data. All of the innovative designs have been confirmed by an ablation study, and two extended experiments were also performed to verify the versatility of the present model in additional construction environments or activities. This model has great potential to be used for autonomous monitoring of construction activities.