| Title |
Foundation Treatment in Urban Underground Engineering Using Big Data Analysis for Smart City Applications |
| ID_Doc |
38742 |
| Authors |
Liu, F; Zhang, YK; Zhang, J |
| Title |
Foundation Treatment in Urban Underground Engineering Using Big Data Analysis for Smart City Applications |
| Year |
2022 |
| Published |
Cmes-Computer Modeling In Engineering & Sciences, 132, 1 |
| DOI |
10.32604/cmes.2022.017967 |
| 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%. |
| Author Keywords |
Underground engineering; internet of things; big data analytics; status quo; economic and social; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000813034100010 |
| WoS Category |
Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications |
| Research Area |
Engineering; Mathematics |
| PDF |
https://file.techscience.com/uploads/attached/file/20220609/20220609101536_43139.pdf
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