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ANN-Incorporated satin bowerbird optimizer for predicting uniaxial compressive strength of concrete

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成果类型:
期刊论文
作者:
Wu, Dizi;LI, Shuhua;Moayedi, Hossein;Cifci, Mehmet Akif;Li, Binh Nguyen
作者机构:
[Wu, Dizi] Changsha Univ Sci & Technol, Sch Architecture, Changsha 410004, Peoples R China.
[LI, Shuhua] China Construct Second Engn Buerau LTD, Beijing, Peoples R China.
[Li, Binh Nguyen; Moayedi, Hossein] Duy Tan Univ, Inst Res & Dev, Da Nang, Vietnam.
[Li, Binh Nguyen; Moayedi, Hossein] Duy Tan Univ, Sch Engn & Technol, Da Nang, Vietnam.
[Cifci, Mehmet Akif] Bandirma Onyedi Eylul Univ, Dept Comp Engn, TR-10200 Balikesir, Turkey.
语种:
英文
关键词:
CFSTC column;Concrete;Compression capacity;Neural computing;Satin bowerbird optimizer
期刊:
STEEL AND COMPOSITE STRUCTURES
ISSN:
1229-9367
年:
2022
卷:
45
期:
2
页码:
281-291
机构署名:
本校为第一机构
院系归属:
建筑学院
摘要:
Surmounting complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) with artificial neural network (ANN) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also ...

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