Sistema de monitorização e gestão de terraplenagens baseado em digital twin e simulação de um caso de estudo
DOI:
https://doi.org/10.14195/2184-8394_159_3Palavras-chave:
terraplenagens, alocação de recursos, otimização multiobjectivo, sensorização, digital twinResumo
O planeamento de obras de terraplenagem procura fornecer um escalonamento dos recursos disponíveis de modo eficiente e económico. Porém, é comum ocorrerem situações imprevistas, como resultado de avarias dos recursos mecânicos, ou condições meteorológicas adversas, levando à necessidade de constante reorganização do fluxo de trabalho no estaleiro de construção. O presente trabalho descreve uma metodologia inovadora para apoiar o processo de tomada de decisão ao longo de todas as fases de construção, fornecendo soluções ótimas de alocação de recursos e correspondentes custos e duração. Durante a construção, o apoio à tomada de decisão é feito através da monitorização por meio de sensores, que permite avaliar o desempenho dos equipamentos de modo constante. O sistema pode fornecer sugestões de reajustes à alocação de recursos quando se verifique necessário para garantir que os trabalhos progridem otimamente. A metodologia é aplicada a um caso de estudo referente ao projeto de terraplenagens da construção de uma autoestrada em Portugal.
Downloads
Referências
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
