Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6695513 | Automation in Construction | 2018 | 14 Pages |
Abstract
Excessive greenhouse gas (GHG) emissions from construction stage pose an obvious and emerging challenge. Most previous studies on emissions from the construction phase only focused on emission prediction in advance of the actual construction activities or on quantitative analysis after building construction. A system that enables builders to monitor emissions from construction activities in real time is still lacked, although GHG emission monitoring (GEM) and analysis are considered as the top activities that must be performed to minimize excessive environmental impacts. As an initial exploration, this paper proposes a GEM system based on Internet of things (IoT) technology to real-time monitor emissions when manufacturing prefabricated components. In this system, Radio Frequency Identification (RFID) sensors are adopted to identify the component ID, and the corresponding material usage data are extracted from a database that is preset in the GEM system. Laser sensors are installed in the components production line to measure the running time of equipment so that energy usage can be calculated in real time. In addition, a data service platform was developed to implement wireless data transmission from production line to computing platform, where the monitoring results are visually presented. A production line of real-life prefabricated components in China is adopted to demonstrate that the IoT-based monitoring system can acquire and analyze real-time carbon emission data from the manufacturing process. The results indicate that GEM system can facilitate project teams to timely control irregular emissions, identify potential emission risks and explore possible strategies for minimizing carbon emissions in the construction sector.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Civil and Structural Engineering
Authors
Xingyu Tao, Chao Mao, Fangyun Xie, Guiwen Liu, PengPeng Xu,