Deep-Learning-Based Sound Classification Model for Concrete Pouring Work Monitoring at a Construction Site

Abstract

In the present study, the utilization of sound data in research and technology is examined, data classification techniques are analyzed, and the applicability and necessity of these techniques are explored in order to propose an acoustic classification model that differentiates between normal and abnormal sounds during concrete pouring. The paper presents an experiment in which normal sound data occurring during concrete pouring, main noise data from construction, and symptom data that could affect structural quality or even cause a collapse incident were collected. By analyzing sound data from actual construction sites and experiments, a deep-learning-based classification model was developed with the aim of preventing events that could compromise the quality and safety of structures in advance. In the classification model, both CNN (convolutional neural network) and RNN (recurrent neural network) exhibited high accuracies of 94.38% and 93.26%, respectively, demonstrating remarkable performance in identifying the status of concrete placement. Unlike previous research that only collected and sorted normal construction-related sound data, the current study developed a sorting model that addresses quality- and safety-related matters by including sound data that may influence material separation, concrete leakage, and formwork collapse during concrete placement, and differentiating these sounds from normal concrete pouring sounds. The research findings are expected to contribute to the improvement of safety management and work efficiency at construction sites.