Deep learning-based recognition of construction activities in real construction site environment

Purpose Today’s technological advancements have had a significant impact on the construction industry. Managing and controlling complex construction projects has been made significantly easier using technological tools. One such advancement is the automatic identification of workers’ activities. This study aims to classify construction worker activities by analyzing real-time motion data collected from sensors. Design/methodology/approach In accordance with our specific goals, we utilized advanced deep-learning methodologies such as deep neural networks, convolutional neural network, long short-term memory and convolutional long short-term memory to analyze the data thoroughly. This involved experimenting with various window sizes and overlap ratios to determine the optimal combination that would result in the most accurate predictions. Findings Based on the analysis results, the convolutional long short-term memory (ConvLSTM) deep learning model with a window size of 4.8 s and an overlap rate of 75% was found to be the most accurate prediction model. This model correctly predicted 98.64% of the basic construction worker activities in a real construction site environment. Originality/value Previous studies have mainly been conducted in laboratory environments and have focused on basic construction activities such as lifting, moving, sawing and hammering. However, this study collected data from real workers in a real construction site environment. Various deep learning models were employed to determine the most accurate one. Additionally, several options were tested to determine the optimal window size and overlap ratio during the data segmentation phase, aiming to select the most suitable ones for preparing the data for the model.