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Monitoring Climate Forcers from Heavy Construction Equipment Emissions in a Digital Twin Framework
Construction equipment operations are major sources of climate forcers, aka. Greenhouse Gas (GHG) emissions and air pollutants such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matters (PM). For the past years, technologies including cloud computing, the Internet of Things (IoT) and Artificial Intelligence (AI) have been paving the way toward sustainability and net zero emissions worldwide. Amongst them, Digital Twins (DT) have increasingly gained attention in several industries for its abilities of autonomous monitoring, evaluation, prediction and optimization of processes and products. The construction sector, however, is lagging behind in the adoption of DT and innovative technologies in general. DT for construction applications are still at early development stages and fully developed examples are rare. Research yet lacks realistic demonstrations and practical implementations, in particular as it relates to emissions and pollutants that can impact the construction workforce or inhabitants of the often nearby public communities. This paper aims at an early prototype of DT for monitoring and forecasting emissions data from fossil-fuel powered heavy construction equipment. A framework is proposed for a web-based DT platform for construction sites that is based on Building Information Modelling (BIM), data obtained from equipment telematics and Portable Emission Measuring Systems (PEMS), for providing services such as data visualization, performance control, emissions monitoring, and emissions prediction. Two implementation tests were conducted – one with artificial data for validation of the data processing methods with ground truth information – and one with data collected with PEMS for evaluating the results with real emissions data. The initial results show that the applied methods perform successfully as applied to concrete pile driving activities. Although more tests are needed from real site operations, including the numerous and various other types of equipment, the work identified ...
Monitoring Climate Forcers from Heavy Construction Equipment Emissions in a Digital Twin Framework
Construction equipment operations are major sources of climate forcers, aka. Greenhouse Gas (GHG) emissions and air pollutants such as carbon dioxide (CO2), nitrogen oxides (NOx), and particulate matters (PM). For the past years, technologies including cloud computing, the Internet of Things (IoT) and Artificial Intelligence (AI) have been paving the way toward sustainability and net zero emissions worldwide. Amongst them, Digital Twins (DT) have increasingly gained attention in several industries for its abilities of autonomous monitoring, evaluation, prediction and optimization of processes and products. The construction sector, however, is lagging behind in the adoption of DT and innovative technologies in general. DT for construction applications are still at early development stages and fully developed examples are rare. Research yet lacks realistic demonstrations and practical implementations, in particular as it relates to emissions and pollutants that can impact the construction workforce or inhabitants of the often nearby public communities. This paper aims at an early prototype of DT for monitoring and forecasting emissions data from fossil-fuel powered heavy construction equipment. A framework is proposed for a web-based DT platform for construction sites that is based on Building Information Modelling (BIM), data obtained from equipment telematics and Portable Emission Measuring Systems (PEMS), for providing services such as data visualization, performance control, emissions monitoring, and emissions prediction. Two implementation tests were conducted – one with artificial data for validation of the data processing methods with ground truth information – and one with data collected with PEMS for evaluating the results with real emissions data. The initial results show that the applied methods perform successfully as applied to concrete pile driving activities. Although more tests are needed from real site operations, including the numerous and various other types of equipment, the work identified ...
Monitoring Climate Forcers from Heavy Construction Equipment Emissions in a Digital Twin Framework
Andrade, Lylian M. (author) / Teizer, Jochen (author) / Fidelis, Emuze / Sherratt, Fred / Soeiro, Alfredo
2023-01-01
Andrade , L M & Teizer , J 2023 , Monitoring Climate Forcers from Heavy Construction Equipment Emissions in a Digital Twin Framework . in E Fidelis , F Sherratt & A Soeiro (eds) , Proceedings of the CIBW099W123 : Digital Transformation of Health and Safety in Construction . pp. 409-419 , CIB W099 & W123 Annual International Conference , Porto , Portugal , 21/06/2023 . < https://doi.org/10.24840/978-972-752-309-2 >
Article (Journal)
Electronic Resource
English
DDC:
690
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