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http://dx.doi.org/10.7843/kgs.2022.38.11.87

A Study on the Quality Control Method for Geotechnical Information Using AI  

Park, Ka-Hyun (Geotechnical Engrg. Research Department, Korea Institute of Civil and Building Technology)
Kim, Jongkwan (Geotechnical Engrg. Research Department, Korea Institute of Civil and Building Technology)
Lee, Seokhyung (Geotechnical Engrg. Research Department, Korea Institute of Civil and Building Technology)
Kim, Min-Ki (Metalogos)
Lee, Kyung-Ryoon (Metalogos)
Han, Jin-Tae (Geotechnical Engrg. Research Dept., Korea Institute of Civil and Building Technology)
Publication Information
Journal of the Korean Geotechnical Society / v.38, no.11, 2022 , pp. 87-95 More about this Journal
Abstract
The geotechnical information constructed in the National Geotechnical Information DB System has been extensively used in design, construction, underground safety management, and disaster assessment. However, it is necessary to refine the geotechnical information because it has nearly 300,000 established cases containing a lot of missing or incorrect information. This research proposes a method for automatic quality control of geotechnical information using a fully connected neural network. Significantly, the anomalies in geotechnical information were detected using a database combining the standard penetration test results and strata information of Seoul. Consequently, the misclassification rate for the verification data is confirmed as 5.4%. Overall, the studied algorithm is expected to detect outliers of geotechnical information effectively.
Keywords
Artificial Intelligence (AI); Deep learning; Geotechnical information; Neural Network (NN); Quality Control (QC); Standard Penetration Test (SPT);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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