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Automated Estimation of Construction Equipment Emission Using Inertial Sensors and Machine Learning Models

Author

Listed:
  • Farid Shahnavaz

    (Computational Science Research Center, San Diego State University, San Diego, CA 92182, USA)

  • Reza Akhavian

    (Department of Civil, Construction, and Environmental Engineering, San Diego State University, San Diego, CA 92182, USA)

Abstract

The construction industry is one of the main producers of greenhouse gasses (GHG). With the looming consequences of climate change, sustainability measures including quantifying the amount of air pollution during a construction project have become an important project objective in the construction industry. A major contributor to air pollution during construction projects is the use of heavy equipment. Therefore, efficient operation and management can substantially reduce a project’s carbon footprint and other environmental harms. Using unintrusive and indirect methods to predict on-road vehicle emissions has been a widely researched topic. Nevertheless, the same is not true in the case of construction equipment. This paper describes the development and deployment of a framework that uses machine learning (ML) methods to predict the level of emissions from heavy construction equipment. Data is collected via an Internet of Things (IoT) approach with accelerometer and gyroscope sensors as data collection nodes. The developed framework was validated using an excavator performing real-world construction work. A portable emission measurement system (PEMS) was used along with the inertial sensors to record the amount of CO, NO X , CO 2 , SO 2 , and CH 4 pollution emitted by the equipment. Different ML algorithms were developed and compared to identify the best model to predict emission levels from inertial sensors data. The results show that Random Forest with the coefficient of determination (R 2 ) of 0.94, 0.91, and 0.94, and normalized root-mean-square error (NRMSE) of 4.25, 6.42, and 5.17 for CO, NO X , and CO 2 , respectively, was the best algorithm among different models evaluated in this study.

Suggested Citation

  • Farid Shahnavaz & Reza Akhavian, 2022. "Automated Estimation of Construction Equipment Emission Using Inertial Sensors and Machine Learning Models," Sustainability, MDPI, vol. 14(5), pages 1-22, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2750-:d:759262
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    References listed on IDEAS

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    1. Cline, William R, 1996. "The Impact of Global Warming on Agriculture: Comment," American Economic Review, American Economic Association, vol. 86(5), pages 1309-1311, December.
    2. Michael Ben-Chaim & Efraim Shmerling & Alon Kuperman, 2013. "Analytic Modeling of Vehicle Fuel Consumption," Energies, MDPI, vol. 6(1), pages 1-11, January.
    3. Huang, Lizhen & Krigsvoll, Guri & Johansen, Fred & Liu, Yongping & Zhang, Xiaoling, 2018. "Carbon emission of global construction sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1906-1916.
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    Cited by:

    1. Abdullah H. Al-Nefaie & Theyazn H. H. Aldhyani, 2023. "Predicting CO 2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model," Sustainability, MDPI, vol. 15(9), pages 1-21, May.

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