Browse

Application of Recurrent Neural Network to Prediction of Structural Deterioration
구조물 노후화 예측을 위한 순환신경망 방법론의 적용

DC Field Value Language
dc.contributor.advisor송준호-
dc.contributor.author최수빈-
dc.date.accessioned2018-05-29T03:06:30Z-
dc.date.available2018-05-29T03:06:30Z-
dc.date.issued2018-02-
dc.identifier.other000000150513-
dc.identifier.urihttp://hdl.handle.net/10371/141312-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 공과대학 건설환경공학부, 2018. 2. 송준호.-
dc.description.abstractDegradation and obsolescence of structures shows limitations to preemptive forecasting and corresponding response because of various uncertainties. In order to overcome this challenge, this study proposes a methodology for combining Structural Health Monitoring (SHM) data obtained during operation, which is one of the deep learning algorithms, and deterioration progress models based on mechanistic knowledge by using Recurrent Neural Network (RNN). Recurrent neural network is one kind of deep learning methodology used to learn the input data accepted in time order, and it is currently actively applied the areas of language awareness and modelling. Accurate prediction and evaluation of the aging of the structure can be possible by recursively updating the monitoring data through the recurrent neural network. Accurate prediction and evaluation of a structure's obsolescence can be achieved by updating it recursively with monitoring data via a recurrent neural network. It can be confirmed by comparing with the results of the existing aging prediction model and additionally the accuracy of the recurrent neural network model is determined by checking the results regarding to the number of base training data sets. It also verifies its performance and enables better forecasting by proposing new algorithms that can be applied to the obsolescence of structures as well as applying existing recurrent neural network.-
dc.description.tableofcontentsChapter 1. Introduction 1
1.1. Objectives, Framework and Importance of the Research 1
1.2. Study Background 2


Chapter 2. Recurrent Neural Network(RNN) and Long-short Term Memory (LSTM) 6
2.1. Recurrent Neural Network (RNN) 6
2.2. Limits of Recurrent Neural Network (RNN) 7
2.3. Long Short-Term Memory (LSTM) 8
2.4. Window LSTM & Stacked LSTM 10

Chapter 3. LSTM approach of Corrosion Progress Model 12
3.1. Corrosion Progress Model (Engelhardt and McDonald, 2004) 12
3.2. LSTM approach – Effect of Number of Base Training Data Sets 16

Chapter 4. Application of LSTM to Prediction of Corrosion Progress 20
4.1. Prediction of LSTM model 20
4.2. Comparison of Natural, Window, Stacked LSTM model 22
4.3. Comparison with Particle Filter 24

Chapter 5. Conclusion 26

Appendix A 27
References 29
Abstract in Korean 31
-
dc.formatapplication/pdf-
dc.format.extent2229856 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectRecurrent Neural Network-
dc.subjectMachine Learning-
dc.subjectStructural Deterioration and Obsolescence-
dc.subjectDecision Making-
dc.subject.ddc624-
dc.titleApplication of Recurrent Neural Network to Prediction of Structural Deterioration-
dc.title.alternative구조물 노후화 예측을 위한 순환신경망 방법론의 적용-
dc.typeThesis-
dc.contributor.AlternativeAuthorChoi Soobin-
dc.description.degreeMaster-
dc.contributor.affiliation공과대학 건설환경공학부-
dc.date.awarded2018-02-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Civil & Environmental Engineering (건설환경공학부)Theses (Master's Degree_건설환경공학부)
Files in This Item:
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse