Recognition method of typical anomalies during karst tunnel construction using GPR attributes and Gaussian processes-Reference-Cited by-同舟云学术

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Recognition method of typical anomalies during karst tunnel construction using GPR attributes and Gaussian processes

Published:2020-08 Issue:16 Volume:13 Page:
ISSN:1866-7511
Container-title:Arabian Journal of Geosciences
language:en
Short-container-title:Arab J Geosci

Author:

Liu Maomao,Liu Zonghui,Zhou Dong,Lan Riyan,Wu Heng

Funder

National Natural Science Foundation of China

Science Foundation for Distinguished Young Scholars of Guangxi

Systematic Project of Guangxi Key Laboratory of Disaster Prevention and Engineering Safety

Guangxi Science and Technology Base and Talents Project

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s12517-020-05782-0.pdf

Reference44 articles.

1. Ahmadi R, Fathianpour N, Norouzi G (2015) Detecting physical and geometrical parameters of some common geotechnical targets through their effects on GPR responses. Arab J Geosci 8(7):4843–4854. https://doi.org/10.1007/s12517-014-1517-5

2. Baek S, Kim S, Kwon J, Um ES (2017) Ground penetrating radar for fracture mapping in underground hazardous waste disposal sites: a case study from an underground research tunnel, South Korea. J Appl Geophys 141:24–33. https://doi.org/10.1016/j.jappgeo.2017.03.017

3. Brahim-Belhouari S, Bermak A (2004) Gaussian process for nonstationary time series prediction. Comput Stat Data Anal 47(4):705–712. https://doi.org/10.1016/j.csda.2004.02.006

4. Cassidy NJ (2008) GPR attenuation and scattering in a mature hydrocarbon spill: a modeling study. Vadose Zone J 7(1):140–159. https://doi.org/10.2136/vzj2006.0142

5. Chen G, Wu Z, Wang F, Ma Y (2011) Study on the application of a comprehensive technique for geological prediction in tunneling. Environ Earth Sci 62(8):1667–1671. https://doi.org/10.1007/s12665-010-0651-y

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1. A deep learning-based algorithm for intelligent prediction of adverse geologic bodies in tunnels;Measurement Science and Technology;2024-06-14

2. Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in Linings;Sustainability;2023-08-01

3. Applied research of comprehensive advance geological prediction in Daluoshan water diversion tunnel;Scientific Reports;2023-06-06

4. Data acquisition-interpretation-aggregation for dynamic design of rock tunnel support;Automation in Construction;2022-11

5. Mechanism of the rock stratum resisting inrush in filling-type karst tunnels and its application;Arabian Journal of Geosciences;2022-10-22

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