Settlement Prediction in a Vertical Drainage-Installed Soft Clay Deposit Using the Genetic Algorithm (GA) Back-Analysis

Park, Hyun I.; Park, Borinara; Kim, Yun T.; Hwang, Dae J.

doi:10.1080/10641190802620198

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…Furthermore, the creep and destructuration-related parameters have also been identified by Ye et al (2016). A stochastic optimization technique generic algorithm (GA) has also been widely used to identify parameters in various constitutive models (Pal et al, 1996;Mašín et al, 2006), pile group optimization (Chan et al, 2009), determination of soil hydraulic properties (Mahbod & Zand-Parsa, 2010), settlement prediction of soft clays (Park et al, 2009), slope stability analysis (Zolfaghari et al, 2005;Goh, 1999). Particle swarm optimization (PSO) is another populationbased stochastic optimization algorithm that has been a valuable tool employed in inverse problems in geotechnical engineering.…”

Section: Inverse Analysis Methods In Geotechnical Engineeringmentioning

confidence: 99%

Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA

Ayizula

2024

Preprint

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<p>The Finite Element Method (FEM) has been routinely used in geotechnical engineering. However, its realistic simulation ofsoil behavior depends on the accurate model input parameters. This study aims to determine through an inverse analysis on the compressibility of fine-grained soils in the Greater Toronto Area (GTA) according to the Hardening Soil Model (HSM). A series of oedometer test results is collected from a local transit project and back analyzed by employing UCODE, auniversal inversemodeling tool,which can adjust model parametersto fit the simulated results with the test values. First, a sensitivity analysis is performed to select the most critical model parameters to simplify the problem. Second, the selected HSM parameters are calibrated by combining UCODE with geotechnical FEM software, PLAXIS. Third, a statistical analysis is conducted on the compressibility parameters according to the soil types. In the end, a series of correlation formulas are derived to estimate the compressibility properties from soil indices.</p>

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Section: Inverse Analysis Methods In Geotechnical Engineeringmentioning

confidence: 99%

Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA

Ayizula

2024

Preprint

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“…where f (•) represents the fitness function that correspond to Eq. (14). By using the DE, the GP hyper-parameters can be obtained quickly, as well as the training samples with the parameters, so the GP respond surface mapping with the input consisted of the jointed parameters, and the output consisted of displacement and stress values can be constructed.…”

Section: The Gp Respond Surface Optimized By Dementioning

confidence: 99%

“…The main reason is that the complex constitutive model and 3D numerical calculation are time-consuming, which makes the parameters identification more difficult [12,13]. In order to improve the computing speed, more and more evolutionary intelligent optimization algorithms, including the genetic algorithm (GA) [14], evolution strategy algorithm (ESA) [15], and particle swarm optimization (PSO) [16,17], have been combined with numerical algorithms for back analysis. Furthermore, the nonlinear respond surface methodology (RSM) has been employed to map the nonlinear relation between the mechanical parameters and displacement of surrounding rock.…”

Section: Introductionmentioning

confidence: 99%

Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm

Jiang¹,

Guo²,

Zheng³

et al. 2021

Computer Modeling in Engineering &Amp; Sciences

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Due to the geological body uncertainty, the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability. The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes, but conventional methods have certain problems, such as a large number of parameters and large time consumption. To solve the problems, this study combines the orthogonal design, Gaussian process (GP) regression, and difference evolution (DE) optimization, and it constructs the parameters identification method of the jointed surrounding rock. The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps. First, a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes, where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress. Then, the GP regress model optimized by DE is trained by the data samples. Finally, the GP model is integrated into the DE algorithm, and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function, while the parameters of the jointed surrounding rock are used as variables and identified. The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel, which is located in Dalian, China. The obtained calculation and analysis results are as follows: CR = 0.9, F = 0.6, NP = 100, and the difference strategy DE/Best/1 is recommended. The results of the back analysis are compared with the field monitored values, and the relative error is 4.58%, which is satisfactory. The algorithm influencing factors are also discussed, and it is found that the local correlation coefficient σ f and noise standard deviation σ n affected the prediction accuracy of the GP model. The results show that the proposed method is feasible and can achieve high identification precision. The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel.

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“…In geotechnical engineering, the GAs have been widely used to solve various problems such as parameter identification of constitutive models, 12,[18][19][20][21][22][23]45 prediction of soil hydraulic parameters, [46][47][48] identification of critical slip surfaces in slope stability analysis, [49][50][51][52][53] prediction of vertical settlement, 54 optimization of pile group design, 34,55 reliability analysis, 56 and prediction of soil-water characteristic curves for unsaturated soils. 57…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement

Yin

Jin

Shen

et al. 2017

Num Anal Meth Geomechanics

236

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A comparative study of optimization techniques for identifying soil parameters in geotechnical engineering was first presented. The identification methodology with its 3 main parts, error function, search strategy, and identification procedure, was introduced and summarized. Then, current optimization methods were reviewed and classified into 3 categories with an introduction to their basic principles and applications in geotechnical engineering. A comparative study on the identification of model parameters from a synthetic pressuremeter and an excavation tests was then performed by using 5 among the mostly common optimization methods, including genetic algorithms, particle swarm optimization, simulated annealing, the differential evolution algorithm and the artificial bee colony algorithm. The results demonstrated that the differential evolution had the strongest search ability but the slowest convergence speed. All the selected methods could reach approximate solutions with very small objective errors, but these solutions were different from the preset parameters. To improve the identification performance, an enhanced algorithm was developed by implementing the Nelder-Mead simplex method in a differential algorithm to accelerate the convergence speed with strong reliable search ability. The performance of the enhanced optimization algorithm was finally highlighted by identifying the Mohr-Coulomb parameters from the 2 same synthetic cases and from 2 real pressuremeter tests in sand, and ANICREEP parameters from 2 real pressuremeter tests in soft clay.

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Settlement Prediction in a Vertical Drainage-Installed Soft Clay Deposit Using the Genetic Algorithm (GA) Back-Analysis

Cited by 10 publications

References 12 publications

Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA

Inverse-analysis of Compressibility Parameters for Fine-grained Soils in GTA

Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm

Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement

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