Automated Material Separation Activity Identification for Sustainable Demolition Operations

The reusability and recyclability of demolition waste are significantly affected by demolition operations, particularly material separation activities, which are largely driven by productivity considerations. As such, investigating the productivity of demolitions operations is key to understanding the decision-making processes affecting the reusability and recyclability of demolition waste. Traditional approaches for tracking the duration of demolition operations and thereby monitoring their productivity are costly, time consuming, and prone to human errors. To enable more effective and efficient demolition productivity monitoring, this study presents an automated approach for identifying demolition waste material separation activities using the motion data of demolition machinery. As proof of concept, small-scale heavy equipment is used to simulate demolition operations. Inertial measurement unit (IMU) sensors are attached to different moving members of the small-scale heavy equipment to collect angular and linear acceleration data. Collected time-stamped sensor data are preprocessed and subsequently used to train and test an activity recognition model using various supervised machine learning classification algorithms. The output of the developed model facilitates the delivery of actual productivity information, which can be used to optimize demolition planning and decision-making in a way that increases the recycling and reuse of demolition waste.