A hybrid inversion method of damped least squares with simulated annealing used for Rayleigh wave dispersion curve inversion

Lu, Jianqi; Li, Shanyou; Li, Wei; Tang, Lihua

doi:10.1007/s11803-014-0208-2

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…The global optimization algorithm is a hot topic, but it also has the defects of slow convergence and premature when in the face of multi-parameter problems. Consequently, combining various inversion algorithms becomes a reasonable choice and tendency (Lei et al, 2019;Li et al, 2020;Lu et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO

Luo

2023

Preprint

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Rayleigh wave is widely applied in engineering exploration and geotectonic research. While how to reconstruct the corresponding geological information via Rayleigh wave is the critical process and difficulty. This paper presents an inversion method of Rayleigh wave dispersion curves based on BP neural network and PSO. In this work, a sample set that referring to the actual stratum distribution is firstly generated. Then, BP neural network is adopted to train the nonlinear mapping relationship between the dispersion curves and the shear wave velocity of each stratum. The trained BP neural network can quickly output a predicted value with rationality but poor precision, which can be utilized as the initial model of PSO inversion. PSO will then be adopted to further optimize the inversion result on the basis of BP neural network prediction. The combination of BP neural network and PSO aims at overcoming the defects of BP neural network that unable to carry out continual optimization and the slow optimization of PSO in the absence of reasonable initial solution. Finally, the effectiveness of the proposed algorithm is verified by a series of synthetic models and an active-source Rayleigh wave experiment carried out in a new railway project from Baotou, Inner Mongolia to Yinchuan, Ningxia.

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Section: Introductionmentioning

confidence: 99%

Inversion of Rayleigh Wave Dispersion Curves Via BP Neural Network and PSO

Luo

2023

Preprint

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“…Seepage parameters could be calibrated with seepage monitoring data, and numerical simulation using the calibrated parameters would yield more realistic results. Typical calibration methods for geotechnical parameters include simulated annealing technique [9,10], particle swarm optimization [11][12][13], neural network and genetic algorithm [14][15][16][17], Nelder-Mead algorithm [13,18], response surface method (RSM) [19][20][21], and support vector machine [22].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Sluice Foundation Seepage Using Monitoring Data and Numerical Simulation

Fan

Liu

et al. 2019

Advances in Civil Engineering

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For sluices built on soil foundations, seepage safety of the foundation is one of the most concerns during operation of sluices. Monitoring data could reflect the real seepage behavior in the foundation, but of which the shortcoming is that generally only the local seepage states can be measured. The seepage field in the whole foundation can be analyzed by numerical simulation. The permeability coefficients of the foundation materials significantly affect the numerical simulation results; however, it is difficult to accurately determine the values of permeability coefficients. In this paper, an approach based on response surface method (RSM) for calibration of permeability coefficients was proposed, and the efficiency of parameter calibration is improved by constructing the response surface equation instead of time-consuming finite element calculation of foundation seepage. The seepage in a sluice foundation was analyzed using monitoring data and numerical simulation. The monitoring data showed that the seepage pressure in the foundation periodically varies with high value in flood season and low value in dry season. After calibration of the permeability coefficients of the foundation materials using the measured seepage pressure, the seepage fields in the foundation for different water levels were numerically simulated to investigate the cause for the periodical variation of the seepage pressure and the seepage safety of the foundation was assessed with the calculated seepage gradients. The methods adopted in this study could be applied to seepage analysis for sluice foundations with similar geologic conditions and antiseepage measures.

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