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
- 테크노프레스 Structural engineering and mechanics : An international journal Structural engineering and mechanics : An international journal 제66권 제3호
- 발행연도
- 2018.1
- 수록면
- 399 - 409 (11page)
이용수
초록· 키워드
오류제보하기
This paper demonstrates the potential application of machine learning algorithms for approximate prediction of the load and deflection capacities of the novel type of Laced Steel Concrete-Composite (LSCC) beams proposed by Anandavalli et al. (Engineering Structures 2012). Initially, global and local responses measured on LSCC beam specimen in an experiment are used to validate nonlinear FE model of the LSCC beams. The data for the machine learning algorithms is then generated using validated FE model for a range of values of the identified sensitive parameters. The performance of four well-known machine learning algorithms, viz., Support Vector Regression (SVR), Minimax Probability Machine Regression (MPMR), Relevance Vector Machine (RVM) and Multigene Genetic Programing (MGGP) for the approximate estimation of the load and deflection capacities are compared in terms of well-defined error indices. Through relative comparison of the estimated values, it is demonstrated that the algorithms explored in the present study provide a good alternative to expensive experimental testing and sophisticated numerical simulation of the response of LSCC beams. The load carrying and displacement capacity of the LSCC was predicted well by MGGP and MPMR, respectively.
목차
등록된 정보가 없습니다.
참고문헌 (0)
참고문헌 신청함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Mechanics based analytical approaches to predict nonlinear behaviour of LSCC beams
Structural engineering and mechanics : An international journal
2017 .01
-
Short-Term Load Forecasting Based on Sequential Relevance Vector Machine
Industrial Engineering & Management Systems
2015 .09
-
Comparison of machine learning techniques to predict compressive strength of concrete
Computers and Concrete, An International Journal
2018 .01
-
Numerical study on the deflections of steel-concrete composite beams with partial interaction
Steel and Composite Structures, An International Journal
2021 .01
-
Design of Distributed Support Vector Machine Algorithm using Blended Dynamics Approach
대한전자공학회 학술대회
2021 .11
-
Software Structure Design for Implementing Machine Tool Feed Axis Control Algorithms
제어로봇시스템학회 국제학술대회 논문집
2023 .10
-
기계학습 알고리즘 이용한 약물 반응 예측
대한전자공학회 학술대회
2019 .11
-
건설산업 내 기계학습 알고리즘 (Machine Learning Algorithm) 활용 동향
건설관리 : 한국건설관리학회 학회지
2017 .01
-
기계학습을 이용한 공작기계 강성 예측 모델
한국정밀공학회 학술발표대회 논문집
2022 .10
-
대체공정이 있는 기계-부품 그룹의 형성 - 자기조직화 신경망을 이용한 해법 -
산업경영시스템학회지
2016 .01
-
Predictive maintenance architecture development for nuclear infrastructure using machine learning
Nuclear Engineering and Technology
2020 .01
-
Shear Strength of Prestressed Steel Fiber Concrete I-Beams
International Journal of Concrete Structures and Materials
2015 .09
-
Experimental, Numerical, and Analytical Investigation of the Reinforced Concrete Hidden and Wide Beams
International Journal of Concrete Structures and Materials
2025 .01
-
Numerical simulation of concrete beams reinforced with composite GFRP-Steel bars under three points bending
Structural engineering and mechanics : An international journal
2016 .01
-
PATTERN RECOGNITION AND MACHINE LEARNING
대한토목학회지
2021 .10
-
산업 데이터 분석을 위한 통계 및 기계학습 모형의 예측 성능 비교
한국생산제조학회 학술발표대회 논문집
2017 .04
-
Prediction of RC T‑Beams Shear Strength Based on Machine Learning
International Journal of Concrete Structures and Materials
2024 .09
-
머신러닝을 활용한 소형 압연 공정 온도 예측 신뢰성 평가
제어로봇시스템학회 논문지
2020 .06
-
Large deflection analysis of edge cracked simple supported beams
Structural engineering and mechanics : An international journal
2015 .01
-
자동차 부품 생산 산업을 위한 머신러닝 기반의 품질예측 알고리즘
한국생산제조학회 학술발표대회 논문집
2022 .12
이 논문의 저자 정보
최근 본 자료
댓글(0)
0