The analysis of big data to predict future trends in sustainable and smart construction
کلمات کلیدی:
sustainable construction, smart buildingچکیده
The idea of sustainable development will become increasingly important which has been globally evolved. Big data has been collected through a variety of sensors and channels worldwide, both structured and unstructured, is regarded as one of the most crucial instruments for developing policy and identifying the future trend of sustainable and smart buildings. As a result, it is essential to comprehend the global parallels and differences in sustainable and smart construction in order to learn about technological innovation. In order to analyze and predict trends in the sustainable and smart building industry, this study aims to identify the area of data in a qualitative fashion and extract it from big data.
Four civil engineering specialists and one data scientist were chosen for interviews for a study on civil construction that used the qualitative research methodology.
In this research, 12 environmental fields, 10 social fields, and 7 economic fields in the three aspects of sustainable construction, as well as 14 fields regarding the characteristics of smart buildings, have been identified through interviews.
The results indicate that a more in-depth content interpretation and analysis based on gathering data from each point of the world through collecting big data about types of construction, materials, construction waste, and demolition may improve predictions about future trends and strategies in sustainable and smart buildings.
مراجع
Aseel, A., Roy, R., & Sunil, P. (2023). Predictive big data analytics for drilling downhole problems: A review. Energy Reports, 9, 5863-5876. https://doi.org/10.1016/j.egyr.2023.05.028
Al-Sai, Z. A., & Abdullah, R. (2019, April). A review on big data maturity models. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 156-161). IEEE. https://doi.org/10.1109/JEEIT.2019.8717398
Belhadi, A., Kamble, S. S., Zkik, K., Cherrafi, A., & Touriki, F. E. (2020). The integrated effect of Big Data Analytics, Lean Six Sigma and Green Manufacturing on the environmental performance of manufacturing companies: The case of North Africa. Journal of Cleaner Production, 252, 119903. https://doi.org/10.1016/j.jclepro.2019.119903
Bhathal, G. S., & Singh, A. (2019). Big Data: Hadoop framework vulnerabilities, security issues and attacks. Array, 1, 100002. https://doi.org/10.1016/j.array.2019.100002
Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., ... & Pasha, M. (2016). Big Data in the construction industry: A review of present status, opportunities, and future trends. Advanced engineering informatics, 30(3), 500-521. https://doi.org/10.1016/j.aei.2016.07.001
Chen, G., Hou, J., Liu, C., Hu, K., & Wang, J. (2022). Visualization analysis of cross research between big data and construction industry based on knowledge graph. Buildings, 12(11), 1812. https://doi.org/10.3390/buildings12111812
Chinowsky, P. S., & Meredith, J. E. (2000). Strategic management in construction. Journal of Construction Engineering and Management, 126(1), 1-9. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:1(1)
Daissaoui, A., Boulmakoul, A., Karim, L., & Lbath, A. (2020). IoT and big data analytics for smart buildings: A survey. Procedia computer science, 170, 161-168. https://doi.org/10.1016/j.procs.2020.03.021
Dedić, N., & Stanier, C. (2017). Towards differentiating business intelligence, big data, data analytics and knowledge discovery. In Innovations in Enterprise Information Systems Management and Engineering: 5th International Conference, ERP Future 2016-Research, Hagenberg, Austria, November 14, 2016, Revised Papers 5 (pp. 114-122). Springer International Publishing. https://doi.org/10.1007/978-3-319-49944-4_17
Faroukhi, A. Z., El Alaoui, I., Gahi, Y., & Amine, A. (2020). A Multi-Layer Big Data Value Chain Approach for Security Issues. Procedia Computer Science, 175, 737-744. https://doi.org/10.1016/j.procs.2020.07.109
Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision, 57(8), 1923-1936. https://doi.org/10.1108/MD-07-2018-0825
Garyaev, N., & Garyaeva, V. (2019). Big data technology in construction. In E3S Web of Conferences (Vol. 97, p. 01032). EDP Sciences. https://doi.org/10.1051/e3sconf/20199701032
Huseien, G. F., & Shah, K. W. (2022). A review on 5G technology for smart energy management and smart buildings in Singapore. Energy and AI, 7, 100116. https://doi.org/10.1016/j.egyai.2021.100116
Kandt, J., & Batty, M. (2021). Smart cities, big data and urban policy: Towards urban analytics for the long run. Cities, 109, 102992. https://doi.org/10.1016/j.cities.2020.102992
Kang, Y., Yu, J., & Chang, J. (2017). Big data analytics in civil engineering: the case of China. SSRG International Journal of Civil Engineering, 4(10), 1-6. https://doi.org/10.14445/23488352/IJCE-V4I10P101
Karatas, M., Eriskin, L., Deveci, M., Pamucar, D., & Garg, H. (2022). Big Data for Healthcare Industry 4.0: Applications, challenges and future perspectives. Expert Systems with Applications, 200, 116912. https://doi.org/10.1016/j.eswa.2022.116912
Lewis, D. J., & Martin, T. P. (2015). Managing vagueness with fuzzy in hierarchical big data. Procedia Computer Science, 53, 19-28. https://doi.org/10.1016/j.procs.2015.07.275
Li, Y., & Zhang, S. (2022). Applied Research Methods in Urban and Regional Planning. Springer. https://doi.org/10.1007/978-3-030-93574-0
Mortati, M., Magistretti, S., Cautela, C., & Dell’Era, C. (2023). Data in design: How big data and thick data inform design thinking projects. Technovation, 122, 102688. https://doi.org/10.1016/j.technovation.2022.102688
Munawar, H. S., Ullah, F., Qayyum, S., & Shahzad, D. (2022). Big data in construction: current applications and future opportunities. Big Data and Cognitive Computing, 6(1), 18. https://doi.org/10.3390/bdcc6010018
Rabhi, L., Falih, N., Afraites, A., & Bouikhalene, B. (2019). Big data approach and its applications in various fields. Procedia Computer Science, 155, 599-605. https://doi.org/10.1016/j.procs.2019.08.084
Ridzuan, F., & Zainon, W. M. N. W. (2022). Diagnostic analysis for outlier detection in big data analytics. Procedia Computer Science, 197, 685-692. https://doi.org/10.1016/j.procs.2021.12.189
Rodriguez-Gracia, D., de las Mercedes Capobianco-Uriarte, M., Terán-Yépez, E., Piedra-Fernández, J. A., Iribarne, L., & Ayala, R. (2023). Review of artificial intelligence techniques in green/smart buildings. Sustainable Computing: Informatics and Systems, 38, 100861. https://doi.org/10.1016/j.suscom.2023.100861
Shin, M. H., Jung, J. H., & Kim, H. Y. (2022). Quantitative and Qualitative Analysis of Applying Building Information Modeling (BIM) for Infrastructure Design Process. Buildings, 12(9), 1476. https://doi.org/10.3390/buildings12091476
Todman, L. C., Bush, A., & Hood, A. S. (2023). ‘Small Data’ for big insights in ecology. Trends in Ecology & Evolution, 38(7), 615-622. https://doi.org/10.1016/j.tree.2023.01.015
Zhang, J., Wolfram, D., & Ma, F. (2023). The impact of big data on research methods in information science. Data and Information Management, 7(2), 100038. https://doi.org/10.1016/j.dim.2023.100038