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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.26 No.2
- 발행연도
- 2020.1
- 수록면
- 241 - 251 (11page)
이용수
초록· 키워드
오류제보하기
Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the bestfitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.
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참고문헌 (73)
참고문헌 신청
Rabeh Abbassi / 2019 / An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models / Energy Conversion and Management 179:362~372
Abrams, D. A. / 1927 / Water-cement ratio as a basis of concrete quality / J. Proceedings 23(2):452~457
Ahmed, S. / 2018 / Feature selection using salp swarm algorithm with chaos / Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence 3:65~69
Akande, K. O. / 2014 / Performance comparison of SVM and ANN in predicting compressive strength of concrete / IOSR J. Comput. Eng 16(5):88~94
Onur Akın / 2017 / Active neuro-adaptive vibration suppression of a smart beam / Smart Structures and Systems, An International Journal 20(6):657~668
Abrams, D. A. / 1927 / Water-cement ratio as a basis of concrete quality / J. Proceedings 23(2):452~457
Ahmed, S. / 2018 / Feature selection using salp swarm algorithm with chaos / Proceedings of the 2nd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence 3:65~69
Akande, K. O. / 2014 / Performance comparison of SVM and ANN in predicting compressive strength of concrete / IOSR J. Comput. Eng 16(5):88~94
Onur Akın / 2017 / Active neuro-adaptive vibration suppression of a smart beam / Smart Structures and Systems, An International Journal 20(6):657~668
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