DOI: 10.1007/s11069-020-04089-3
论文题名: Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods
作者: Das S.K. ; Mohanty R. ; Mohanty M. ; Mahamaya M.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 103, 期: 2 起始页码: 2371
结束页码: 2393
语种: 英语
中文关键词: ANN
; Feature selection
; In situ tests
; Liquefaction
; MARS
; MOSOS
; Multi-objective optimization
; NSGA-II
英文关键词: algorithm
; artificial neural network
; computer simulation
; earthquake prediction
; in situ test
; liquefaction
; parameter estimation
; regression analysis
; S-wave
; seismic velocity
英文摘要: The prediction of liquefaction susceptibility for highly unbalanced database with limited and important input parameters is a crucial issue. The proposed multi-objective feature selection algorithms (MOFS) were applied to highly unbalanced databases of in situ tests: standard penetration test (SPT), cone penetration test (CPT) and shear wave velocity (Vs) test.Two multi-objective algorithms, non-dominated sorting genetic algorithm (NSGA-II) and multi-objective symbiotic organisms search algorithm (MOSOS), were coupled with learning algorithms, artificial neural network (ANN) and multivariate adaptive regression spline (MARS) separately to effectively select the optimal parameters and simultaneously minimize the error. The obtained optimal point has approximately equal accuracy in both liquefiable and non-liquefiable conditions for training and testing. The important inputs found for models based on SPT are: (N1)60, amax and Mw; CPT: qc1, amax and CSR and Vs: Vs1, CSR, amax and Mw. The CPT-based models were found to be the most efficient. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168451
Appears in Collections: 气候变化与战略
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作者单位: Department of Civil Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand 826004, India; Department of Civil Engineering, National Institute of Technology Rourkela, Odisha, 769008, India
Recommended Citation:
Das S.K.,Mohanty R.,Mohanty M.,et al. Multi-objective feature selection (MOFS) algorithms for prediction of liquefaction susceptibility of soil based on in situ test methods[J]. Natural Hazards,2020-01-01,103(2)