Earthworks management, monitoring and optimization system based on digital twin and simulation of a case-study
DOI:
https://doi.org/10.14195/2184-8394_159_3Keywords:
earthworks, resource allocation, multi-objective optimisation, sensorization, digital twinAbstract
The aim of planning earthworks is to provide a efficient and economical scheduling of the available resources. However, it is common for unforeseen situations to occur, as a result of breakdowns in mechanical resources, or adverse weather conditions, leading to the need to constantly reorganise the workflow on the construction site. This paper describes an innovative methodology to support the decision-making process throughout all construction phases, providing optimal solutions for resource allocation and corresponding costs and duration. During construction, support for decision-making is provided through sensor-based monitoring, which allows for a constant assessment of equipment performance. The system can provide suggestions for readjustments to resource allocation when necessary to ensure that work progresses optimally. The methodology is applied to a case study concerning the earthworks project for the construction of a motorway in Portugal.
Downloads
References
Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput, 6, pp. 182–197. https://doi.org/10.1109/4235.996017
Gomes Correia, A.; Cortez, P.; Tinoco, J.; Marques, R. (2013). Artificial Intelligence Applications in Transportation Geotechnics. Geotechnical and Geological Engineering, 31(3), pp. 861–879. https://doi.org/10.1007/s10706-012-9585-3
Gomes Correia, A.; Magnan, J.P. (2012). Trends and challenges in earthworks for transportation infrastructures. Adv Transp Geotechs, 2 (Eds: Miura S, Ishikawa T, Yoshida N, Hisari Y, Abe N), pp. 1–12. Londres, CRC Press.
Jassim, H.S.H.; Lu, W.; Olofsson, T. (2017). Predicting Energy Consumption and CO2 Emissions of Excavators in Earthwork Operations: An Artificial Neural Network Model. Sustainability, 9, pp. 1257. https://doi.org/10.3390/su9071257
Kassem, M.; Mahamedi, E.; Rogage, K.; Duffy, K.; Huntingdon, J. (2021). Measuring and benchmarking the productivity of excavators in infrastructure projects: A deep neural network approach. Automation in Construction, 124(January), pp. 103532. https://doi.org/10.1016/j.autcon.2020.103532
Lee, S.; Park, S.; Seo, J. (2018). Utilization analysis methodology for fleet telematics of heavy earthwork equipment. Automation in Construction, 92, pp. 59–67. https://doi.org/10.1016/j.autcon.2018.02.035
Liu, Y.; You, K.; Jiang, Y.; Wu, Z.; Liu, Z.; Peng, G.; Zhou, C. (2022). Multi-objective optimal scheduling of automated construction equipment using non-dominated sorting genetic algorithm (NSGA-III). Automation in Construction, 143, pp. 104587. https://doi.org/10.1016/j.autcon.2022.104587
Marques, R.; Gomes Correia, A.; Cortez, P. (2008). Data Mining Applied to Compaction of Geomaterials. Eight Int. Conf. Bear. Capacit. Roads, Railw. Airfields (Eds: Tutumluer, E.; Al-Qad, I.L.), 1, pp. 597-605. Londres, Taylor & Francis Group.
Mersmann, O.; Trautmann, H.; Steuer, D. (2014). Package "mco": Multiple Criteria Optimization Algorithms and Related Functions. R package version.
Montaser, A.; Moselhi, O. (2014). Truck + for Earthmoving Operations. Journal of Information Technology in Construction, 19, pp. 412–433.
Nassar, K.; Hosny, O. (2012). Solving the Least-Cost Route Cut and Fill Sequencing Problem Using Particle Swarm. Journal of Construction Engineering and Management, 138 (8) , pp. 931–942. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000512
Parente, M.; Gomes Correia, A. (2013). Compaction Management: Results of a Demonstration Project. Advanced Materials Research, 779, pp. 1697-1700. https://doi.org/10.4028/www.scientific.net/AMR.779-780.1697
Parente, M.; Gomes Correia, A.; Cortez, P. (2014). Artificial Neural Networks Applied to an Earthwork Construction Database. 2nd International Conference on Information Technology in Geo-Engineering (ICITG 2014), Durham, UK.
Parente, M.; Cortez, P.; Gomes Correia, A. (2015). An evolutionary multi-objective optimization system for earthworks. Expert Systems With Applications, 42 (19), pp. 6674-6685. https://doi.org/10.1016/j.eswa.2015.04.051
Parente, M.; Gomes Correia, A.; Cortez, P. (2016). Metaheuristics, data mining and geographic information systems for earthworks equipment allocation. Advances in Transportation Geotechnics III, 143, pp. 506-513. https://doi.org/10.1016/j.proeng.2016.06.064
Parente, M.; Gomes Correia, A.; Figueira, G.; Mehrsai, A. (2018). Towards improving earthworks production from an Industry 4.0 perspective: the role of remote information technologies and dynamic optimization techniques. Proceedings of 7th Transport Research Arena (TRA 2018), Vienna, Austria. https://doi.org/10.5281/zenodo.1491368
Parente, M.; Figueira, G.; Amorim, P.; Marques, A. (2020). Production scheduling in the context of Industry 4.0: review and trends. International Journal of Production Research, 58 (17), pp. 5401-5431. https://doi.org/10.1080/00207543.2020.1718794
Pereira, G.; Parente, M.; Moutinho, J.; Sampaio, M. (2021). Fuel Consumption Prediction for Construction Trucks: A Noninvasive Approach Using Dedicated Sensors and Machine Learning. Infrastructures, 6 (11), pp. 157. https://doi.org/10.3390/infrastructures6110157
Salem, A.; Moselhi, O. (2020). AI-Based Cloud Computing Application for Smart Earthmoving Operations. Canadian Journal of Civil Engineering, 48 (3), pp. 312-327. https://doi.org/10.1139/cjce-2019-0681
SETRA; LCPC (2000) Guide des Terrassements Routiers – Réalisation des Semblais et des Couches de Forme. Guia técnico.
Silva, R.; Parente, M.; Neves, J. (2023). Machine learning for the analysis of equipment sensor data in road construction projects. Aceite na 4th International Society for Intelligent Construction (ISIC 2024), Orlando, USA.
You, K.; Ding, L.; Zhou, C.; Dou, Q.; Wang, X.; Hu, B. (2021). 5G-based earthwork monitoring system for an unmanned bulldozer. Automation in Construction, 131, pp. 103891. https://doi.org/10.1016/j.autcon.2021.103891