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임소람(Lim, Soram),Jeong, H.S. “David” 대한건축학회 2022 대한건축학회 학술발표대회 논문집 Vol.42 No.1
In order to establish time-effective and cost-effective maintenance and replacement strategies, it is crucial to understand current condition states of structures and how the states will change in the future. This study established an artificial intelligence model to predict condition states of Reinforced Concrete (RC) Slab using bridge inspection data in Texas, USA. The model was designed in two stages, and the first stage was to derive factors affecting the RC Slab"s state using Extreme Gradient Boosting (XGBoost). The second stage was developed to predict the ratio of RC Slabs in each condition state using Long-Short Term Memory (LSTM), resulting in 64.74% precision in the model. In the future study, the prediction accuracy is planned to be further improved by adjusting input variables and utilizing other learning algorithms.
연관규칙분석을 활용한 교량 부재별 손상 패턴 도출 모델 개발에 관한 기초 연구
임소람(Lim, Soram),정세환(Chung, Sehwan),지석호(Chi, Seokho),송준호(Song, Junho) 대한토목학회 2016 대한토목학회 학술대회 Vol.2016 No.10
교량의 생애주기적 유지관리 비용을 절감시키기 위하여 교량 손상에 대한 예방적 유지관리가 필요하며, 이를 위해 교량의 성능 저하 가능성과 크기를 정량화하는 것이 필요하다. 다수의 선행연구에서는 회귀모형에 기반하여 몇 가지 대표 변수를 이용한 성능열화모델을 수립하였으나 이는 일부 대표 부재에 대한 것이며 적은 변수로 인해 부재의 개별 특성을 반영하기 어려웠다. 따라서 본 연구에서는 개별 부재들의 특성을 반영할 수 있는 다양한 변수를 고려하고 교량 손상을 부재별 손상 유형 수준에서 추정하는 패턴들을 도출하는 모델을 수립하기 위해, 빅데이터 분석 기법 중 하나인 연관규칙분석의 이용 가능성을 확인하고자 한다. 예비적 결과를 통해 확인한 일부 패턴들은 선행연구와 현실 상황에 부합하여 연관규칙분석 방법론의 적용 가능성을 확인하였으며, 향후 연구에는 사용한 Apriori의 속도와 성능 향상을 위해 군집분석 등의 방법을 추가적으로 고려할 것이다.
규칙 기반 분류 기법을 활용한 도로교량 안전등급 추정 모델 개발
정세환(Chung Se hwan),임소람(Lim So ram),지석호(Chi Seok ho) 한국BIM학회 2016 KIBIM Magazine Vol.6 No.2
Road bridges are deteriorating gradually, and it is forecasted that the number of road bridges aging over 30 years will increase by more than 3 times of the current number. To maintain road bridges in a safe condition, current safety conditions of the bridges must be estimated for repair or reinforcement. However, budget and professional manpower required to perform in-depth inspections of road bridges are limited. This study proposes an estimation model for safety rating of road bridges by analyzing the data from Facility Management System (FMS) and Yearbook of Road Bridges and Tunnel. These data include basic specifications, year of completion, traffic, safety rating, and others. The distribution of safety rating was imbalanced, indicating 91% of road bridges have safety ratings of A or B. To improve classification performance, five safety ratings were integrated into two classes of G (good, A and B) and P (poor ratings under C). This rearrangement was set because facilities with ratings under C are required to be repaired or reinforced to recover their original functionality. 70% of the original data were used as training data, while the other 30% were used for validation. Data of class P in the training data were oversampled by 3 times, and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was used to develop the estimation model. The results of estimation model showed overall accuracy of 84.8%, true positive rate of 67.3%, and 29 classification rule. Year of completion was identified as the most critical factor on affecting lower safety ratings of bridges.