IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i5p2750-d759262.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/5/2750/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/5/2750/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael Ben-Chaim & Efraim Shmerling & Alon Kuperman, 2013. "Analytic Modeling of Vehicle Fuel Consumption," Energies, MDPI, vol. 6(1), pages 1-11, January.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Albert, Osei-Owusu Kwame & Marianne, Thomsen & Jonathan, Lindahl & Nino, Javakhishvili Larsen & Dario, Caro, 2020. "Tracking the carbon emissions of Denmark's five regions from a producer and consumer perspective," Ecological Economics, Elsevier, vol. 177(C).
    2. Felipe Jiménez & Wilmar Cabrera-Montiel, 2014. "System for Road Vehicle Energy Optimization Using Real Time Road and Traffic Information," Energies, MDPI, vol. 7(6), pages 1-23, June.
    3. Anna Życzyńska & Dariusz Majerek & Zbigniew Suchorab & Agnieszka Żelazna & Václav Kočí & Robert Černý, 2021. "Improving the Energy Performance of Public Buildings Equipped with Individual Gas Boilers Due to Thermal Retrofitting," Energies, MDPI, vol. 14(6), pages 1-19, March.
    4. Khozema Ahmed Ali & Mardiana Idayu Ahmad & Yusri Yusup, 2020. "Issues, Impacts, and Mitigations of Carbon Dioxide Emissions in the Building Sector," Sustainability, MDPI, vol. 12(18), pages 1-11, September.
    5. Eric Njuki & Boris E Bravo-Ureta & Christopher J O’Donnell, 2018. "A new look at the decomposition of agricultural productivity growth incorporating weather effects," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-21, February.
    6. Habtemariam, Lemlem Teklegiorgis & Abate Kassa, Getachew & Gandorfer, Markus, 2017. "Impact of climate change on farms in smallholder farming systems: Yield impacts, economic implications and distributional effects," Agricultural Systems, Elsevier, vol. 152(C), pages 58-66.
    7. Meiling Zhang & Stephen Nazieh & Teddy Nkrumah & Xingyu Wang, 2021. "Simulating Grassland Carbon Dynamics in Gansu for the Past Fifty (50) Years (1968–2018) Using the Century Model," Sustainability, MDPI, vol. 13(16), pages 1-20, August.
    8. Ahmad, Munir & Nawaz, Muhammad & Iqbal, Muhammad & Javed, Sajid, 2014. "Analysing the Impact of Climate Change on Rice Productivity in Pakistan," MPRA Paper 72861, University Library of Munich, Germany.
    9. Zeynep K. Hansen & Gary D. Libecap & Scott E. Lowe, 2011. "Climate Variability and Water Infrastructure: Historical Experience in the Western United States," NBER Chapters, in: The Economics of Climate Change: Adaptations Past and Present, pages 253-280, National Bureau of Economic Research, Inc.
    10. B. Sonneveld & M. Keyzer & P. Adegbola & S. Pande, 2012. "The Impact of Climate Change on Crop Production in West Africa: An Assessment for the Oueme River Basin in Benin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 553-579, January.
    11. Fabrice Ochou & Philippe Quirion, 2022. "Impact du changement climatique sur l’agriculture : une quantification du biais de prix dans les approches économétriques," Revue économique, Presses de Sciences-Po, vol. 73(1), pages 43-67.
    12. Ay, Jean-Sauveur & Latruffe, Laure, 2013. "The Empirical Content of the Present Value Model: A survey of the instrumental uses of farmland prices," Working papers 157112, Factor Markets, Centre for European Policy Studies.
    13. Zhai, Fan & Lin, Tun & Byambadorj, Enerelt, 2009. "A General Equilibrium Analysis of the Impact of Climate Change on Agriculture in the People’s Republic of China," Asian Development Review, Asian Development Bank, vol. 26(1), pages 206-225.
    14. Zhang, Yanfang & Gao, Qi & Wei, Jinpeng & Shi, Xunpeng & Zhou, Dequn, 2023. "Can China's energy-consumption permit trading scheme achieve the “Porter” effect? Evidence from an estimated DSGE model," Energy Policy, Elsevier, vol. 180(C).
    15. Fisher, Anthony, 2009. "Climate Change and Agriculture Reconsidered," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt33v2d7vc, Department of Agricultural & Resource Economics, UC Berkeley.
    16. Chonabayashi, Shun, 2014. "Accounting for Land Use Adaptation to Climate Change Impacts on US Agriculture," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170710, Agricultural and Applied Economics Association.
    17. Liu, Lirong & Huang, Guohe & Baetz, Brian & Huang, Charley Z. & Zhang, Kaiqiang, 2019. "Integrated GHG emissions and emission relationships analysis through a disaggregated ecologically-extended input-output model; A case study for Saskatchewan, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 106(C), pages 97-109.
    18. Pérez-Sánchez, Laura À. & Velasco-Fernández, Raúl & Giampietro, Mario, 2022. "Factors and actions for the sustainability of the residential sector. The nexus of energy, materials, space, and time use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    19. S. Seo & Robert Mendelsohn & Ariel Dinar & Rashid Hassan & Pradeep Kurukulasuriya, 2009. "A Ricardian Analysis of the Distribution of Climate Change Impacts on Agriculture across Agro-Ecological Zones in Africa," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 43(3), pages 313-332, July.
    20. Patrick S. Ward & Raymond J. G. M. Florax & Alfonso Flores-Lagunes, 2014. "Climate change and agricultural productivity in Sub-Saharan Africa: a spatial sample selection model," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 41(2), pages 199-226.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2750-:d:759262. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.