Incorporating geotechnical and geophysical investigations for underground obstruction detection: A case study

Liu, Yaohui; Ng, Yannick Choy Hing; Zhang, Yunhuo; Yang, Ping; Ku, Taeseo

doi:10.1016/j.undsp.2022.12.003

Cited by 5 publications

(2 citation statements)

References 55 publications

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“…Excavated pits, trenches, or boreholes are used in geotechnical investigations to permit close assessment, sampling for further laboratory analyses, in-situ testing, and description of vertical profiles below the surface of the earth. Observations from these techniques are commonly collected and represented in one-dimensional (1D) borehole log ( Liu et al, 2023;Baluch et al, 2022;Bo, 2022;Look, 2007). Although 1D representation gives accurate geotechnical investigations at the sampled pit/borehole locations, it does not show proper indication of subsurface conditions in between sampled pits/boreholes.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning and 3D Modelling to Improve Geotechnical Site Characterization

Ng'eno,

Omuto,

Biamah

2023

Preprint

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Geotechnical investigation is an important site characterization process in engineering construction. Excavation pits and boreholes are popular geotechnical investigation methods for accessing subsurface soil and rock characteristics. Although they are accurate, they are limited to the discrete locations where tests are carried out. The demand for 3D representation has been increasing as an alternative for improved visualization of geotechnical investigations. This study developed a procedure for 3D modelling of geotechnical investigations using open-source software. It improves 3D visualization of geotechnical investigations and characterization of accuracy and uncertainty assessment. Presently, most geotechnical investigations do not regularly report uncertainty assessment despite its importance in risk analysis of the investigations. The procedure developed in this study was tested in a proposed site for construction of an Inland Container Depot where it depicted geotechnical soil properties with more than 80% accuracy on holdout samples. It was able to model both horizontal and vertical variations of the soil properties with quantification of uncertainty. More testing and wide applications are recommended.

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Section: Introductionmentioning

confidence: 99%

Application of Machine Learning and 3D Modelling to Improve Geotechnical Site Characterization

Ng'eno,

Omuto,

Biamah

2023

Preprint

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“…The expansion of urban underground space, extending from shallow to deep levels, has heightened the importance of studying efficient and precise geophysical exploration technology. This technology is crucial for the development, utilization, and safety management of urban underground space [1,2].…”

Section: Introductionmentioning

confidence: 99%

Identification of Underground Artificial Cavities Based on the Bayesian Convolutional Neural Network

Xia,

Peng,

et al. 2023

Sensors

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The development of underground artificial cavities plays an important role in the exploitation of urban spatial resources. As the rapidly growing number of underground artificial cavities with different depths and scales increases, the detection and identification of underground artificial cavities has become a key issue in underground engineering studies. Geophysical techniques have been widely used for the construction, management, and maintenance of underground artificial cavities. In this study, we present two identification methods for underground artificial cavities. Apparent resistivity imaging is the most popular technique for quickly identifying underground artificial cavities, using the forward simulation results of a three-dimensional earth model and comparing these with the preset positions of artificial cavities, as demonstrated in the experiment. To further improve the efficiency of underground artificial cavity identification, we developed a fast recognition approach for underground artificial cavities based on the Bayesian convolutional neural network (BCNN). Compared to a traditional convolutional neural network, the performance of the BCNN method was greatly improved in terms of the classification accuracy and efficiency of identifying underground artificial cavities with apparent resistivity image datasets.

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Rapid profiling rock mass quality underneath tunnel face for Sichuan-Xizang Railway

Wu,

Qiu,

Yang

et al. 2024

Underground Space

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Incorporating geotechnical and geophysical investigations for underground obstruction detection: A case study

Cited by 5 publications

References 55 publications

Application of Machine Learning and 3D Modelling to Improve Geotechnical Site Characterization

Application of Machine Learning and 3D Modelling to Improve Geotechnical Site Characterization

Identification of Underground Artificial Cavities Based on the Bayesian Convolutional Neural Network

Rapid profiling rock mass quality underneath tunnel face for Sichuan-Xizang Railway

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