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http://dx.doi.org/10.15683/kosdi.2022.3.31.146

A Study on the Performance Degradation Pattern of Caisson-type Quay Wall Port Facilities  

Na, Yong Hyoun (SH Tech & Policy Institute Co.)
Park, Mi Yeon (SH Tech & Policy Institute Co.)
Jang, Shinwoo (RaonX Solutions Inc.)
Publication Information
Journal of the Society of Disaster Information / v.18, no.1, 2022 , pp. 146-153 More about this Journal
Abstract
Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.
Keywords
Big Data; Gaussian Process; Linear Interpolation; Condition Evaluation; Revised Project Level; Influence Factor; Machine Learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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