A Study on Condition Analysis of Revised Project Level of Gravity Port Facility using Big Data

doi:10.15683/kosdi.2021.6.30.254

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2021 Vol.17, Issue 2 Preview Page Next Page

Original Article

A Study on Condition Analysis of Revised Project Level of Gravity Port Facility using Big Data 빅데이터 분석을 통한 중력식 항만시설 수정프로젝트 레벨의 상태변화 특성 분석: 나용현¹ㆍ박미연²*ㆍ장신우³
Yong Hyoun Na¹, Mi Yeon Park²*, Shinwoo Jang³; ¹Chief Researcher, SH Tech & Policy Institute Co., Seongnam, Republic of Korea
²PH.D, SH Tech & Policy Institute Co., Seongnam, Republic of Korea
³Application Engineer, RaonX Solutions Inc., Seongnam, Republic of Korea

30 June 2021. pp. 254-265

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Abstract

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R2 in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

Keywords

Big Data

Gaussian process

Condition evaluation

influence factor

Machine learning

연구목적: 국내 항만시설의 진단을 통한 성능 및 안전에 대한 점검과 진단을 20년 넘게 진행되었지만 그 진단 이력과 결과를 활용한 중장기적인 시설개선과 성능개선을 위한 발전전략이나 방향이 현실적으로 작동하지 않고 있다. 특히, 사용년수가 오래된 항만구조물의 경우, 선박의 대형화와 사용빈도 증가, 기후변화로 인한 자연재해의 영향 등으로 안전성능과 기능적 면에서 상당히 많은 문제점을 내포하고 있다. 연구방법: 본 연구에서는 중력식 안벽에 대한 부재수준의 유지관리 이력 데이터를 수집하여 이를 빅데이터로써 정의하고 해당 데이터를 바탕으로 프로젝트 수준의 시설물의 노후화 패턴 및 열화를 추정하기 위한 예측근사모델을 도출하였다. 특히 GP 및 SGP 기법의 머신러닝 알고리즘을 통하여 생성된 상태기반 노후도 패턴 및 열화 근사모델에 대한 유효성 검토를 통해 빅데이터 활용에 적합한 모델을 상호비교하고 제안하였다. 연구결과: 제안된 기법의 적합성을 검토한 결과 GP기법은 RMSE 및 R2는 0.9854와 0.0721, SGP기법은 0.7246과 0.2518로 GP기법을 적용한 예측모델이 적합한 것으로 검토 되었다. 결론: 머신러닝 기법을 통해 이러한 연구는 향후 항만시설 데이터취합이 지속적으로 이루어진다면 향후 항만시설 투자의사결정에 중요한 역할을 할 것으로 기대한다.

키워드

빅데이터

가우시안 프로세스

상태평가

영향인자

머신러닝

References

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Belyaev, M., Burnaev, E., Kapushev, Y. (2016). "Computationally efficient algorithm for Gaussian Process regression in case of structured samples." Computational Mathematics and Mathematical Physics, Vol. 56, No. 4, pp. 499-513. 10.1134/S0965542516040163
Burnaev, E., Panov, M.. Zaytsev, A. (2016). "Regression on the basis of non-stationary gaussian processes with bayesian regularization." Journal of Communications Technology and Electronics, Vol. 61, pp. 661-671. 10.1134/S1064226916060061
Burnaev, E., Zaytsev, A. (2015). "Surrogate modeling of mutlidelity data for large samples." Journal of Communications Technology & Electronics, Vol. 60, pp. 1348-1355. 10.1134/S1064226915120037
Cha, K.H., Kim, S.W., Kim, J.H., Park, M.Y., Kong, J.S. (2015). "Development of the deterioration models for the port structures by the multiple regression analysis and Markov chain." Computational Structural Engineering Institute of Korea, Vol. 28, No. 3, pp. 229-239. 10.7734/COSEIK.2015.28.3.229
Kim, H.J., Ju, G.N., Yun, C.H. (2012). "Science big data processing technology trend." The Korea Institute of Information and Commucation Engineering, Vol. 29, No. 11, pp. 11-23.
Kim, S.W., Cha, K.H., Kim, J.H., Park, M.Y., Kong, J.S. (2012). "Development of condition state model for open type wharf considering deterioration factors." Korean Society of Civil Engineers, Vol. 28, No.3, pp. 2138-2141.
Rasmussen, C.E. (2004). Gaussian Processes for Machine Learning. MIT Press, Massachusetts, USA, pp. 63-71. 10.1007/978-3-540-28650-9_4
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Port Association (2010). Unit Model Development for LCC Analysis Program at Port Facility Design Stage, p. 40.

Information

Publisher :The Korean Society of Disaster Information
Publisher(Ko) :한국재난정보학회
Journal Title :Journal of the Society of Disaster Information
Journal Title(Ko) :한국재난정보학회논문집
Volume : 17
No :2
Pages :254-265
DOI :https://doi.org/10.15683/kosdi.2021.6.30.254

[1] Belyaev, M., Burnaev, E., Kapushev, Y. (2015). "Gaussian process regression for structured data sets." Lecture Notes in Artificial Intelligence, International Symposium on Statistical Learning and Data Sciences, Vol. 9047, pp. 106-115. 10.1007/978-3-319-17091-6_6

[2] Belyaev, M., Burnaev, E., Kapushev, Y. (2016). "Computationally efficient algorithm for Gaussian Process regression in case of structured samples." Computational Mathematics and Mathematical Physics, Vol. 56, No. 4, pp. 499-513. 10.1134/S0965542516040163

[3] Burnaev, E., Panov, M.. Zaytsev, A. (2016). "Regression on the basis of non-stationary gaussian processes with bayesian regularization." Journal of Communications Technology and Electronics, Vol. 61, pp. 661-671. 10.1134/S1064226916060061

[4] Burnaev, E., Zaytsev, A. (2015). "Surrogate modeling of mutlidelity data for large samples." Journal of Communications Technology & Electronics, Vol. 60, pp. 1348-1355. 10.1134/S1064226915120037

[5] Cha, K.H., Kim, S.W., Kim, J.H., Park, M.Y., Kong, J.S. (2015). "Development of the deterioration models for the port structures by the multiple regression analysis and Markov chain." Computational Structural Engineering Institute of Korea, Vol. 28, No. 3, pp. 229-239. 10.7734/COSEIK.2015.28.3.229

[6] Kim, H.J., Ju, G.N., Yun, C.H. (2012). "Science big data processing technology trend." The Korea Institute of Information and Commucation Engineering, Vol. 29, No. 11, pp. 11-23.

[7] Kim, S.W., Cha, K.H., Kim, J.H., Park, M.Y., Kong, J.S. (2012). "Development of condition state model for open type wharf considering deterioration factors." Korean Society of Civil Engineers, Vol. 28, No.3, pp. 2138-2141.

[8] Rasmussen, C.E. (2004). Gaussian Processes for Machine Learning. MIT Press, Massachusetts, USA, pp. 63-71. 10.1007/978-3-540-28650-9_4

[9] Rasmussen, C.E., Williams, C.K.I. (2005). Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning), The MIT Press, Massachusetts, US. 10.7551/mitpress/3206.001.0001

[10] Song, M.G., Kim, S.B. (2013). "A study of improving reliability on prediction model by analyzing method big data." The Journal of Digital Policy & Management, Vol. 11, pp. 103-112.

[11] Port Association (2010). Unit Model Development for LCC Analysis Program at Port Facility Design Stage, p. 40.

Journal of the Society of Disaster InformationISSN:1976-2208(Print) 2671-5287(Online)한국재난정보학회

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