Machine learning-based equipment sound classification for advanced construction management and site supervision

This study focuses on machine learning classification of the sounds of equipment operating in the construction site environment to support improved construction management and site supervision processes. The research utilizes a large, openly available, open access audio dataset of seven different types of equipment, collected in the field under real conditions in urban regeneration projects initiated after a major earthquake in Elazığ. The dataset consists of 15,588 sounds recorded from vehicles such as bulldozers, excavators, dump trucks, graders, loaders, mixer trucks and rollers used on the construction site.

In the developed classification system, discriminative features were first extracted from equipment sounds using Local Binary Patterns (LBP) and statistical moments. In the feature selection stage, the Neighborhood Component Analysis (NCA) and Chi-Square (Chi2) method is applied to identify the most significant features and dimensionality reduction is achieved. In the final stage, Support Vector Machines (SVM) and k-Nearest Neighbor (kNN) classifiers are used to discriminate the equipment types with high accuracy. The findings show that the proposed method makes a significant contribution to construction management objectives such as effective monitoring of vehicles and equipment on the construction site, resource management and process tracking. In addition, the transparency and reproducibility provided by the open dataset provides a strong basis for further studies in the related field.