A model to reduce earthmoving impacts
Abstract
Meeting increasingly ambitious carbon regulations in the construction industry is particularly challenging for earthmoving operations due to the extensive use of heavy-duty diesel equipment. Better planning of operations and balancing of competing demands linked to environmental concerns, costs, and duration is needed. However, existing approaches (theoretical and practical) rarely address all of these demands simultaneously, and are often limited to parts of the process, such as earth allocation methods or equipment allocation methods based on practitioners’ past experience or goals. Thus, this study proposes a method that can integrate multiple planning techniques to maximize mitigation of project impacts cost-effectively, including the noted approaches together with others developed to facilitate effective decision-making. The model is adapted for planners and contractors to optimize mass flows and allocate earthmoving equipment configurations with respect to tradeoffs between duration, cost, CO2 emissions, and energy use. Three equipment allocation approaches are proposed and demonstrated in a case study. A rule-based approach that allocates equipment configurations according to hauling distances provided the best-performing approach in terms of costs, CO2 emissions, energy use and simplicity (which facilitates practical application at construction sites). The study also indicates that trucks are major contributors to earthmoving operations’ costs and environmental impacts.
Keyword : earthmoving operations, optimization framework, optimum configuration, tradeoff duration, cost, emissions
How to Cite
Jassim, H. S. H., Krantz, J., Lu, W., & Olofsson, T. (2020). A model to reduce earthmoving impacts. Journal of Civil Engineering and Management, 26(6), 490-512. https://doi.org/10.3846/jcem.2020.12641
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Goh, C. K., Tan, K. C., Liu, D., & Chiam, S. C. (2010). A competitive and cooperative co-evolutionary approach to multiobjective particle swarm optimization algorithm design. European Journal of Operational Research, 202(1), 42–54. https://doi.org/10.1016/j.ejor.2009.05.005
González, V., & Echaveguren, T. (2012). Exploring the environmental modeling of road construction operations using discrete-event simulation. Automation in Construction, 24, 100–110. https://doi.org/10.1016/j.autcon.2012.02.011
Grann, H. (1997). The industrial symbiosis at Kalundborg, Denmark. In The industrial green game. Implications for environmental design and management (pp. 117–123). National Academy Press.
Gwak, H., Seo, J., & Lee, D. (2018). Optimal cut-fill pairing and sequencing method in earthwork operation. Automation in Construction, 87, 60–73. https://doi.org/10.1016/j.autcon.2017.12.010
Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence. Orlando, FL, USA. https://doi.org/10.1109/ICEC.1994.350037
Hunkeler, D., & Rebitzer, G. (2005). The future of life cycle assessment. The International Journal of Life Cycle Assessment, 10(5), 305–308. https://doi.org/10.1065/lca2005.09.001
Jacobsson, M., & Linderoth, H. C. (2010). The influence of contextual elements, actors’ frames of reference, and technology on the adoption and use of ICT in construction projects: a Swedish case study. Construction Management and Economics, 28(1), 13–23. https://doi.org/10.1080/01446190903406154
Jassim, H., Lu, W., & Olofsson, T. (2017). Predicting energy consumption and CO2 emissions of excavators in earthwork operations: an artificial neural network model. Sustainability, 9(7), 1257. https://doi.org/10.3390/su9071257
Jassim, H. S., Lu, W., & Olofsson, T. (2018a). Assessing energy consumption and carbon dioxide emissions of off-highway trucks in earthwork operations: An artificial neural network model. Journal of Cleaner Production, 198, 364–380. https://doi.org/10.1016/j.jclepro.2018.07.002
Jassim, H. S., Lu, W., & Olofsson, T. (2018b). Quantification of energy consumption and carbon dioxide emissions during excavator operations. In I. Smith, & B. Domer (Eds.), Lecture Notes in Computer Science: Vol. 10863. Advanced Computing Strategies for Engineering. EG-ICE 2018. Springer. https://doi.org/10.1007/978-3-319-91635-4_22
Jassim, H. S., Lu, W., & Olofsson, T. (2019). Determining the environmental impact of material hauling with wheel loaders during earthmoving operations. Journal of the Air & Waste Management Association, 69(10), 1195–1214. https://doi.org/10.1080/10962247.2019.1640805
Jukic, D., & Carmichael, D. G. (2016). Emission and cost effects of training for construction equipment operators: a field study. Smart and Sustainable Built Environment, 5(2), 96–110. https://doi.org/10.1108/SASBE-11-2015-0040
Kaboli, A. S., & Carmichael, D. G. (2014). Truck dispatching and minimum emissions earthmoving. Smart and Sustainable Built Environment, 3(2), 170–186. https://doi.org/10.1108/SASBE-10-2013-0050
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