A Supervised Machine Learning-Based Sound Identification for Construction Activity Monitoring and Performance Evaluation

The sound recognition technology, which has been adopted in diverse disciplines, has not received much attention in the construction industry. Since each working and operation activity on a construction site generates its distinct sound, its identification provides imperative information regarding work processes, task performance, and safety relevant issues. Thus, the accurate analysis of construction sound data is vital for construction project participants to monitor project procedures, make data-driven decisions, and evaluate task productivities. To accomplish this objective, this paper investigates the sound recognition technology for construction activity identification and task performance analyses. For sound identification, Mel-frequency cepstral coefficients are extracted as the features of the six types of sound data. In addition, a supervised machine learning algorithm called Hidden Markov Model is used to perform sound classification. The research findings show that the maximum classification accuracy is 94.3% achieved by a 3-state HMM. This accuracy of the adopted technique is expected to reliably execute the construction sound recognition, which significantly leverage construction monitoring, performance evaluation, and safety surveillance approaches.