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비탈면 안전성 평가를 위한 가우시안 프로세스 활용 변위 모니터링
( Sirojiddin Nuriev ),( Lee Jong-jae ) 한국구조물진단유지관리공학회 2021 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.25 No.1
This study demonstrates a slope stability evaluation monitoring method based on displacement sensors and Gaussian processes (GPs), which is a popular machine learning algorithm for nonlinear data. Slope displacement monitoring data prediction model based on GPs is constructed, and the main structural parameters of the GPs are optimized to predict the slope monitoring data with confidence intervals. Finally, slope stability is evaluated based on prediction and confidence interval of GPs and measurement of the displacement data.
비탈면 안전성 평가를 위한 가우시안 프로세스 활용 변위 모니터링
( Sirojiddin Nuriev ),( Lee Jong-jae ) 한국구조물진단유지관리공학회 2021 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.25 No.1
This study demonstrates a slope stability evaluation monitoring method based on displacement sensors and Gaussian processes (GPs), which is a popular machine learning algorithm for nonlinear data. Slope displacement monitoring data prediction model based on GPs is constructed, and the main structural parameters of the GPs are optimized to predict the slope monitoring data with confidence intervals. Finally, slope stability is evaluated based on prediction and confidence interval of GPs and measurement of the displacement data.
( Sirojiddin Nuriev ),이종재 ( Lee Jong-jae ),최현호 ( Choi Hyunho ) 한국구조물진단유지관리공학회 2021 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.25 No.2
AutoSHM is a real-time health monitoring system and data analysis platform for bridges. The platform monitors the bridge over time using periodically sampled dynamic response measurements from various IoT devices. Inspectors can easily visualize historical data. The platform is analyzing the way inspectors use data to solve problems. The platform provides a powerful bridge health monitoring model based on automated or manually designed machine learning models trained with the bridge’s historical data.
기계학습 기법을 이용한 건전도 모니터링 시스템의 센서결함 탐지
김세훈 ( Kim Sehoon ),( Sirojiddin Nuriev ),이종재 ( Lee Jong-jae ) 한국구조물진단유지관리공학회 2019 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.23 No.1
The widespread sensors in a structural monitoring system provide vital support to its operation. Data is obtainedf rom sensors in a structural health monitoring system for integrity assessment of the structure, and false alarm will be frequently triggered if a faulty sensor is detected. In this study, a proposed method based on machine learning algorithm and Gaussian distribution is present to identify sensor fault.