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Study on Inversion of Damage Incentives of High Pile Wharf in Inland River Based on SEResNet

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DOI: 10.23977/jaip.2023.060409 | Downloads: 12 | Views: 457

Author(s)

Li Jia 1, Cai Fenglin 1, Zhu Qitao 1, Yang Tongxin 1, Zhang Qian 1

Affiliation(s)

1 School of Intelligent Technology and Engineering, Chongqing University of Sicence and Technology, Chongqing, 401331, China

Corresponding Author

Cai Fenglin

ABSTRACT

Based on SEResNet neural network algorithm, the inversion model of damage incentives of inland high-piled wharf is constructed. The stress data of pile foundation under the action of damage incentives of high-piled wharf are obtained by using solid element finite element model calculation and indoor model test methods. The parameterized finite element calculation model of high-piled wharf is established by using subprocess in Python program to call MANSYS module, and verified with solid element model. The parameterized simplified finite element model meets the needs of inversion calculation. Based on the stress data samples of the pile foundation of the high-piled wharf obtained from the model test, the inversion analysis of single and multiple damage incentives is carried out. The model can identify the location, size and type of injury causative agent with good generalization ability.

KEYWORDS

The SEResNet neural network algorithm; Inland high-pile wharf; Cause of injury; invert

CITE THIS PAPER

Li Jia, Cai Fenglin, Zhu Qitao, Yang Tongxin, Zhang Qian, Study on Inversion of Damage Incentives of High Pile Wharf in Inland River Based on SEResNet. Journal of Artificial Intelligence Practice (2023) Vol. 6: 65-78. DOI: http://dx.doi.org/10.23977/jaip.2023.060409.

REFERENCES

[1] Fang Jing, Zhang Yanchi, Zhu Yaxian, Pan Deqiang, Wang Shengnian. Damage situation and durability countermeasures and suggestions of high-piled wharf in seaport [C]. Proceedings of the Science and Technology Forum on Durability and Design Methods of Concrete Structures in Coastal Areas and the 6th National Symposium on Concrete Durability. 2004: 387-400. 
[2] Zuo Liangdong, et al. "Inversion of damage inducement of wharf pile foundation under heaped load based on parametric model." SN Applied Sciences 4. 3 (2022): 69. 
[3] Wang Yuanzhan, Li Da. Analysis of overall safety degree of high-piled wharf structure under stacking load [J]. Waterway Port, 2013, 34(5): 430-435. 
[4] Zhang Junhong, Li Shihai, Xu Likai, Hu Han. Experimental test and inversion study on mechanical properties of large pipe piles in harbor wharf [J]. Port Engineering Technology, 2006, 4: 21-23. 
[5] Jie Hu, Li Shen, Gang Sun; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7132-7141
[6] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778
[7] Zhang Zhilu, and Mert Sabuncu. "Generalized cross entropy loss for training deep neural networks with noisy labels." Advances in neural information processing systems 31 (2018). 
[8] Sun Xiaochao, Zhang Yinghui, Lu Qi, Cui Di, He Weidong. Simulation analysis of small-period dynamic meshing of cycloidal pinwheel drive based on APDL [J]. Journal of Dalian Jiaotong University, 2020, 18 (05): 12-16. 
[9] A. Hakan AKTAS. Development a Spectrophoto- metric of Fe(Ⅲ), Al(Ⅲ)and Cu(Ⅱ) Using Eriochrome Cyanine R Ligand and Assessment of the Obtained Data by Partial Least-Squares and Artificial Neural Network Method—Application to Natural Waters[J]. Spectroscopy and Spectral Analysis,2018, 18(08): 102-106. 
[10] Hao Cheng, Dorian J. Garrick,  Rohan L. Fernando. Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction [J]. Journal of Animal Science and Biotechnology, 2017, 18(03): 352-356. 
[11] Tianxiang Zhang,  Jinya Su,  Cunjia Liu, Wenhua Chen. Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture [J]. International Journal of utomation and Computing, 2019, 18(01): 182-188. 

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