Big Data and Analytics in the Deep Renovation Life Cycle
Author:
Koukaras Paraskevas,Krinidis Stelios,Ioannidis Dimosthenis,Tjortjis Christos,Tzovaras Dimitrios
Abstract
AbstractThe rising volume of heterogeneous data accessible at various phases of the construction process has had a significant impact on the construction industry. The availability of data is especially advantageous in the context of deep renovation, where it may significantly accelerate the decision-making process for building stock retrofit. This chapter covers Big Data and analytics in the context of deep renovation and shows how Machine Learning and Artificial Intelligence have affected the various phases of the deep renovation life cycle. It presents a review of the literature on Big Data and deep renovation and discusses a series of use cases, applications, advantages, and benefits as well as challenges and barriers. Finally, Big Data and deep renovation prospects are discussed, including future potential developments and guidelines.
Publisher
Springer International Publishing
Reference35 articles.
1. Androutsopoulos, A., Geissler, S., Charalambides, A. G., Escudero, C. J., Kyriacou, O., & Petran, H. (2020). Mapping the deep renovation possibilities of European buildings. IOP Conference Series: Earth and Environmental Science, 410(1), 12056. https://doi.org/10.1088/1755-1315/410/1/012056 2. Avramidou, A., & Tjortjis, C. (2021). In I. Maglogiannis, J. Macintyre, & L. Iliadis (Eds.), Predicting CO2 emissions for buildings using regression and classification BT—Artificial Intelligence applications and innovations (pp. 543–554). Springer International Publishing. 3. Bilal, M., Oyedele, L. O., Akinade, O. O., Ajayi, S. O., Alaka, H. A., Owolabi, H. A., Qadir, J., Pasha, M., & Bello, S. A. (2016b). Big Data architecture for Construction Waste Analytics (CWA): A conceptual framework. Journal of Building Engineering, 6, 144–156. 4. Bilal, M., Oyedele, L. O., Kusimo, H. O., Owolabi, H. A., Akanbi, L. A., Ajayi, A. O., Akinade, O. O., & Delgado, J. M. D. (2019). Investigating profitability performance of construction projects using Big Data: A project analytics approach. Journal of Building Engineering, 26, 100850. 5. Bilal, M., Oyedele, L. O., Qadir, J., Munir, K., Ajayi, S. O., Akinade, O. O., Owolabi, H. A., Alaka, H. A., & Pasha, M. (2016a). 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
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
|
|