@Article{cmes.2022.017967,
AUTHOR = {Fei Liu, Yunkai Zhang, Jian Zhang},
TITLE = {Foundation Treatment in Urban Underground Engineering Using Big Data Analysis for Smart City Applications},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {132},
YEAR = {2022},
NUMBER = {1},
PAGES = {153--172},
URL = {http://www.techscience.com/CMES/v132n1/48089},
ISSN = {1526-1506},
ABSTRACT = {A core element of the sustainable approach to global living quality improvement can now become the intensive
and organized usage of underground space. There is a growing interest in underground building and growth
worldwide. The reduced consumption of electricity, effective preservation of green land, sustainable wastewater
and sewage treatment, efficient reverse degradation of the urban environment, and reliable critical infrastructure
management can improve the quality of life. At the same time, technological innovations such as artificial
intelligence (AI), cloud computing (CC), the internet of things (IoT), and big data analytics (BDA) play a
significant role in improved quality of life. Hence, this study aims to integrate the technological innovations in
urban underground engineering to ensure a high quality of life. Thus, this study uses big data analytics to carry
out the status quo of foundation treatment and proposes a conceptual framework named the BDA with IoT on
urban underground engineering (BI0T-UUE). This framework connects hidden features with various high-level
sensing sources and practical predictive model characterization to lower building costs, productive infrastructure
management, preparedness for disasters, and modern community smart services. The IoT integration gives an
optimum opportunity to work towards the functionality of ‘‘digital doubles’’ of secret infrastructure, both economical and scalable, with the increasing sophistication and tooling of the underworld. The simulation analysis
ensures the highest efficiency and cost-effectiveness of the underground engineering with a value of 96.54% and
97.46%.},
DOI = {10.32604/cmes.2022.017967}
}