A visual monitoring framework for integrated productivity and carbon footprint control of construction operations

As buildings and infrastructure are becoming more energy efficient, reducing and mitigating construction-phase carbon footprint and embodied carbon is getting more attention. Government agencies are forming incentive-based regulations on controlling these impacts and expressing control of carbon footprints as principle dynamic goals in projects. These regulations are placing requirements upon construction firms to find control techniques to minimize carbon footprint without affecting productivity of operations. Nevertheless, there is limited research on integrated real-time techniques to monitor operations productivity and carbon footprint. This paper proposes a new framework and presents preliminary data in which (1) construction operations are visually sensed through construction site imagery and video-streams; subsequently (2) equipment's location and action are semantically analyzed through an integrated 3D image-based reconstruction and appearance-based recognition algorithm; (3) productivity and carbon footprint of construction operations are measured through a new machine learning approach; and finally (4) for each construction schedule activity, measured productivity and carbon footprint are visualized.