Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but ...Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.展开更多
Innovation ability is an important component of the core quality in the 21st century. This paper firstly introduces the difficulty of cultivating innovation ability through "Needham's Problem" and "...Innovation ability is an important component of the core quality in the 21st century. This paper firstly introduces the difficulty of cultivating innovation ability through "Needham's Problem" and "Qian Xuesen's Question". Then from the society, school curriculum and teachers of the analysis of primary school students’ innovation ability training obstacles, and then put forward the corresponding solutions. Finally, with the help of SCS maker teaching method, the teaching attempt was made to cultivate students' innovative ability based on the extracurricular activities of primary school science.展开更多
基金supported by Korea Research Fellowship Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Grant No.2019H1D3A1A01102993)the Inha University Research Grant(2022).
文摘Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.
文摘Innovation ability is an important component of the core quality in the 21st century. This paper firstly introduces the difficulty of cultivating innovation ability through "Needham's Problem" and "Qian Xuesen's Question". Then from the society, school curriculum and teachers of the analysis of primary school students’ innovation ability training obstacles, and then put forward the corresponding solutions. Finally, with the help of SCS maker teaching method, the teaching attempt was made to cultivate students' innovative ability based on the extracurricular activities of primary school science.
