지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Structural Engineering and Mechanics, An Int'l Journal Structural Engineering and Mechanics, An Int'l Journal Vol.83 No.3
- 발행연도
- 2022.8
- 수록면
- 327 - 340 (14page)
이용수
초록· 키워드
오류제보하기
The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.
목차
등록된 정보가 없습니다.
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
A neuro-fuzzy approach to predict the shear contribution of end-anchored FRP U-jackets
Computers and Concrete, An International Journal
2020 .01
-
Response Simulation, Data Cleansing and Restoration of Dynamic and Static Measurements Based on Deep Learning Algorithms
International Journal of Concrete Structures and Materials
2019 .02
-
Application of Adaptive Neuro-Fuzzy Inference System for Evaluating Compressive Strength of Concrete
INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS
2021 .06
-
Runoff estimation using modified adaptive neuro-fuzzy inference system
Environmental Engineering Research
2020 .08
-
Identification of the most influencing parameters on the properties of corroded concrete beams using an Adaptive Neuro-Fuzzy Inference System (ANFIS)
Steel and Composite Structures, An International Journal
2020 .01
-
Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes
Environmental Engineering Research
2020 .06
-
Nitrogen removal, nitrous oxide emission and microbial community in sequencing batch and continuous-flow intermittent aeration processes
Environmental Engineering Research
2019 .03
-
Machine learning and RSM models for prediction of compressive strength of smart bio-concrete
Smart Structures and Systems, An International Journal
2021 .10
-
An Optimal Fuzzy Neural Network Prediction Model for Student Performance Prediction in Online Education
IEIE Transactions on Smart Processing & Computing
2024 .10
-
Intelligent fuzzy inference system approach for modeling of debonding strength in FRP retrofitted masonry elements
Structural engineering and mechanics : An international journal
2017 .01
-
Solving Inverse Kinematics of DeltaRobot Using Anfis
제어로봇시스템학회 국제학술대회 논문집
2015 .10
-
적응형뉴로퍼지시스템을 이용한 인코넬718 밀링가공시 표면상태감시
한국생산제조학회지
2017 .12
-
Computation of daily solar radiation using adaptive neuro-fuzzy inference system in Illinois
한국수자원학회 학술발표회
2015 .01
-
돼지 심근경색 모델: CMR과 4D flow MRI를 이용한 선행연구
대한기계학회 춘추학술대회
2020 .12
-
적응형 뉴로-퍼지(ANFIS)를 이용한 도시철도 시스템 위험도 평가
대한기계학회 춘추학술대회
2022 .03
-
Analyzing behavior of circular concrete-filled steel tube column using improved fuzzy models
Steel and Composite Structures, An International Journal
2022 .06
-
ANFIS를 이용한 철근콘크리트 부재 보강철근의 부착강도 평가
대한건축학회 논문집 - 구조계
2016 .09
-
고층 RC 벽식 건물의 지진 취약도 분석
한국지진공학회 논문집
2016 .01
-
Designing fuzzy systems for optimal parameters of TMDs to reduce seismic response of tall buildings
Smart Structures and Systems, An International Journal
2017 .01
-
Semi-active seismic control of a 9-story benchmark building using adaptive neural-fuzzy inference system and fuzzy cooperative coevolution
Smart Structures and Systems, An International Journal
2019 .01
이 논문의 저자 정보
최근 본 자료
댓글(0)
0