BIG DATA, MACHINE LEARNING E CLOUD COMPUTING NA GESTÃO DE OBRAS: UMA REVISÃO SISTEMÁTICA DA LITERATURA
DOI:
https://doi.org/10.46421/entac.v18i.1186Keywords:
Big Data, Machine Learning, Cloud Computing, Works management, Civil constructionAbstract
This study sought to identify the main contributions of the use of Big Data, Machine Learning and Cloud Computing tools in the field of construction work management. For this, a systematic literature review was carried out in the main databases of international scientific research and 37 papers on the subject were selected. The analysis showed that the use of these tools in civil construction is incipient and related mostly to the use of BIM technology, mainly in Machine Learning and Big Data. It has been found that civil construction still needs to advance more in the use of such tools, in order to use in its entirety what technology has to offer.
References
ACATECH, National Academy of Science and Engineering. Recommendations for implementing the strategic initiative INDUSTRIE 4.0. 2013. Disponível em: https://www.acatech.de/Publikation/recommendations-for-implementing-the-strategicinitiative-industrie-4-0- final-report-of-the-industrie-4-0-working-group/. Acesso em: 10 maio 2019.
AKHAVIAN, Reza; BEHZADAN, Amir H.. Smartphone-based construction workers' activity recognition and classification. Automation In Construction, v. 71, p.198-209, nov. 2016.
AKINYEMI, Abiodun; SUN, Ming; GRAY, Alasdair J G. An ontology-based data integration framework for construction information management. Proceedings Of The Institution Of Civil Engineers - Management, Procurement And Law, v. 171, n. 3, p.111-125, jun. 2018.
ATUAHENE, Bernard Tuffour; KANJANABOOTRA, Sittimont; GAJENDRAM, Thayaparan. Towards na Integrated Framework of Big Data Capabilities in the Construction Industry: A Systematic Literature Review. In: 34th Association of Researchers in Construction Management (ARCOM), 34, Belfast, 2018. Anais... Newcastle: ARCOM, 2018.
Beach, T.H., Rana, O.F., Rezgui, Y. and Parashar, M. Cloud computing for the architecture, engineering & construction sector: requirements, prototype & experience. Journal of Cloud Computing, v. 2, n. 8, p.1–16, 2013.
BILAL, M. et al. Big data architecture for construction waste analytics (CWA): A conceptual framework. Journal of Building Engineering, v. 6, p. 144-156, 2016.
CHENG, Chieh-feng et al. Activity analysis of construction equipment using audio signals and support vector machines. Automation In Construction, v. 81, p.240-253, set. 2017.
DAVIES, A.; SHARP, D. RICS Strategic Facilities Management. Case studies, International Workplace, Cambridge, Report, 2014.
DRATH, R.; HORCH, A. Industrie 4.0: Hit or hype? IEEE industrial electronics magazine, v. 8, n. 2, p. 56– 58, 2014.
DRESCH, Aline; LACERDA, Daniel Pacheco; ANTUNES JÚNIOR, José Antonio Valle. Design Science research: método de pesquisa para avanço da ciência e tecnologia. Porto Alegre: Bookman, 2020.
FANG et al. Case Study of BIM and Cloud–Enabled Real-Time RFID Indoor Localization for Construction Management Applications. Journal of Construction Engineering and Management, v. 142, n. 7, 2016.
FATHI, Mohamad Syazli; RAWAI, Norshakila; ABEDI, Mohammad. Mobile Information System forSustainable Project Management. Applied Mechanics And Materials, v. 178-181, p.2690-2693, mai. 2012.
GALVÃO, Taís Freire; PEREIRA, Mauricio Gomes. Revisões sistemáticas da literatura: passos para sua elaboração. Epidemiologia e Serviços de Saúde, v. 23, n. 1, p.183-184, mar. 2014.
GETULI, Vito et al. A BIM-based Construction Supply Chain Framework for Monitoring Progress and Coordination of Site Activities. Procedia Engineering, v. 164, p.542-549, 2016.
GONG, Jie; AZAMBUJA, Marcelo. Visualizing Construction Supply Chains with Google Cloud Computing Tools. International Conference on Sustainable Design and Construction, p.671-678, nov. 2012. Anais... United States: ICSDES, 2012.
HEMANTH, Guggilla et al. AHP analysis for using cloud computing in supply chain management in the construction industry. 2nd International Conference For Convergence In Technology, abr. 2017. Anais... Índia: I2CT, 2017LEE, S.; LEE, H. A study on estimation method of concrete compressive strength based on machine learning algorithm considering mixture factor. Journal of the Korea Institute of Building Construction, 17(1):152– 153, 2017.
PORTO, Gabriele de Bonis Patekoski; KADLEC, Thalita Malucelli de Moraes. Mapeamento de estudos prospectivos de tecnologias na revolução 4.0: um olhar para a indústria da construção civil. 2018. 70 f. TCC (Graduação) - Curso de Engenharia Civil, Departamento Acadêmico de Construção Civil, Universidade TecnolÓgica Federal do ParanÁ, Curitiba, 2018.
POSENATO, D. et al. Methodologies for model-free data interpretation of civil engineering structures. Computers & structures, v. 88, n. 7, p. 467-482, 2010.
RAM, J.; AFRIDI, N. K.; KHAN, K. A. (2019). Adoption of Big Data analytics in construction: development of a conceptual model. Built Environment Project and Asset Management, v. 9, n. 4, 2019.
SAHIN, Meral et al. A simulation case study on supply chain management of a construction firm adopting cloud computing and RFID. International Journal Of Industrial And Systems Engineering, [s.l.], v. 27, n. 2, p.233- 254, 2017.
SAKURAI, R.; ZUCHI, J. D. As revoluções industriais até a indústria 4.0. Interface Tecnológica, v.15, n.2, 2018.
SANTOS, Asaffe C. M. dos. Aprendizado de máquina aplicado ao diagnóstico de Dengue.
ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL, 13., 2016, Recife.12p.
SHUANG, Dong et al. An experimental study of intrusion behaviors on construction sites: The role of age and gender. Safety Science, [s.l.], v. 115, p.425-434, jun. 2019.
SOUSA, F. R. C.; MOREIRA, L. O.; MACHADO, J. C. Computação em nuvem: conceitos, tecnologias, aplicações e desafios. Anais da II Escola Regional de Computação Ceará, Maranhão e Piauí (ERCEMAPI). 2009. Cap. 7, p. 150-175.
TANG, Shu et al. A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends. Automation In Construction, [s.l.], v. 101, p.127-139, maio 2019.
TIXIER, Antoine J.-p. et al. Application of machine learning to construction injury prediction.
Automation In Construction, [s.l.], v. 69, p.102-114, set. 2016.
YANG, Jun; SHI, Zhongke; WU, Ziyan. Vision-based action recognition of construction workers using dense trajectories. Advanced Engineering Informatics, [s.l.], v. 30, n. 3, p.327-336, ago. 2016.
ZHENG, Rongyue et al. BcBIM: A Blockchain-Based Big Data Model for BIM Modification Audit and Provenance in Mobile Cloud. Mathematical Problems In Engineering, [s.l.], v. 2019, p.1-13, 18 mar. 2019.
