Geotechnical stability analysis considering strain softening using micro-polar continuum finite element method

Chen, Xi; Wang, Dongyong; Tang, Jianbin; Ma, Wenchen; Liu, Yong

doi:10.1007/s11771-021-4603-3

Cited by 34 publications

(11 citation statements)

References 28 publications

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“…Considering the previous studies focus on investigation of the effect of drought phenomena on precipitation and temperature variables, less attention has been paid to the soil water demands in agriculture (Yang et al 2015(Yang et al , 2020a(Yang et al , 2020bLalehzari & Kerachian 2020a;Xu et al 2021). Conditional value at risk, fuzzy set theory, and Monte Carlo simulation were the techniques of uncertainty analysis in the previous studies (Chen & Guo 2019;Alam et al 2020;Lalehzari & Kerachian 2020b;Simos and Tsitouras 2020;Chen et al 2021). Application of these techniques is unable to present a predicted pattern of hydrological phenomena for generating a multivariate estimation to define the future events.…”

Section: Introductionmentioning

confidence: 99%

Expression of Concern: Application of probability decision system and particle swarm optimization for improving soil moisture content

2021

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Drought is one of the natural disasters having the highest degrees in comparison to the other natural disasters in terms of rate, intensity, incident duration, region expansion, life losses, economic damages, and long-term effects. Hence, the prediction of drought as a meteorological phenomenon should be evaluated to determine the groundwater exploitation strategies in agriculture. The present study aims at investigating the impact of the drought duration and severity on soil moisture supplement for agricultural activities in Baghmalek plain, Khuzestan province, Iran. For this objective, a non-dimensional index of precipitation depth was defined for quantifying the drought characteristics. Furthermore, marginal distribution functions, correlation coefficients and joint functions were incorporated to a probabilistic decision-making framework to predict the variables in different return periods from 2-year to 100-year. Results showed that t copula was the best function for constructing the multivariate distribution in the study area based on the goodness-of-fit tests. Moreover, soil moisture content in the root zone achieved by the predetermined amounts of precipitation could be increased in the seasonal average.

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Section: Introductionmentioning

confidence: 99%

Expression of Concern: Application of probability decision system and particle swarm optimization for improving soil moisture content

2021

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“…Uncertainty analysis is one of the main components in the decision-making systems of the different engineering sciences (Alam et al 2020a, Yang et al 2020aAbedini et al 2020;Abedini & Zhang 2021;Ouyang et al 2021;Chen et al 2021;Sabernezhad 2021;Rivera-Diaz et al 2021). In hydrological problems, probabilistic planning and fuzzy programming are included as two methods of uncertainty analysis (Wang et al 2018(Wang et al , 2019.…”

Section: Introductionmentioning

confidence: 99%

“…Hydrological uncertainties can be analyzed using univariate, bivariate, or multivariate distribution functions that numerous studies have been carried out based on this concept (Ahmad & Simonovic 2011;Chen & Guo 2019;Rahimi et al 2020). Probabilistic modeling to take into account the rainfall uncertainty in the design of urban runoff control system (Chen et al 2021), groundwater resources and water exploitation (Lalehzari & Kerachian 2020b), estimating the soil moisture balance in root zone under drought condition (Huang et al 2021), and energy systems (Ma et al 2021) has been evaluated.…”

Section: Introductionmentioning

confidence: 99%

Expression of Concern: Fuzzy-probabilistic modeling the flood characteristics using bivariate frequency analysis and α-cut decomposition

Sun

Khayatnezhad

2021

Water Supply

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Water allocation in agricultural lands, optimal design of hydraulic structures and climatic phenomena are the events in water management science that face hydrological uncertainties. The purpose of this study is to estimate the characteristics of surface runoff based on probabilistic and fuzzy analysis. Separation and generation of basic hydrological information, probabilistic modeling, fuzzy analysis, and optimization to achieve the solution were the main steps of the decision-making problem. Long-term hydrological data of the study area were collected, analyzed and used as a basis for the simulation model. In this study, a copula-based stochastic method was developed to deal with uncertainties related to rainfall and runoff characteristics as well as to address the nonlinear dependence between multiple random variables. The relationship between rainfall variables and flood characteristics was formulated through fuzzy set theory. The feasible domain of the fuzzy problem was searched using the non-dominated sorting genetic algorithm to find the optimal extreme points. The obtained solutions were used as a fuzzy response to calculate the flood of the Baghmalek plain in Khuzestan province in southwestern Iran. The results showed that the maximum model error occurred in predicting rainfall depth and flood volume, and the maximum rainfall rate and runoff flow could be calculated more accurately. Moreover, the developed fuzzy-probabilistic model was able to predict more than 90% of flood events within the defined fuzzy range.

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“…At present, identifying the temperature fluctuations can be considered as a decision-making parameter in agricultural and biological experiments (Li et al 2017(Li et al , 2019(Li et al , 2020Sun et al 2019;Zhang et al 2019aZhang et al , 2019bZhang et al , 2020Chen et al 2021;Jalali Sarvestani & Charehjou 2021;Liu et al 2021). In Figure 2 the maximum temperature changes for the period from 2020 to 2039 for climate change models in two scenarios RCP 4.5 and RCP 8.5 have been shown.…”

Section: Maximum Temperaturementioning

confidence: 99%

Evaluating the climate change effects on temperature, precipitation and evapotranspiration in eastern Iran using CMPI5

2021

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In this study, the trend of climate changes during a future period from 2020 to 2039 has been evaluated using the data of the Fifth Climate Change Report under two emission scenarios RCP 4.5 and RCP 8.5 for Neishabour plain, Iran. Eleven models such as CESM, EC EARTH, HADGEM, MPI, NORESM, CANESM, CSIROM, GFDLCM2, GISS E2, IPSL and MIROC ESM have been used to evaluate changes in minimum and maximum temperatures, precipitation, and evapotranspiration. The results showed that GFDLCM2, MPI and IPSL models were more accurate in terms of precipitation and GISS E2 and GFDLCM2 models were the suitable option for predicting the maximum and minimum temperatures and evapotranspiration. Considering the evaluated parameters, minimum temperature, maximum temperature and evapotranspiration had approximately the constant trends and were accompanied by a slight increase and decrease for the next two decades, but for the precipitation, large fluctuations were predicted for the next period. Moreover, in the study years for the four parameters in all simulated models, the RCP 8.5 scenario has estimated a higher amount than the RCP 4.5 scenario.

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Geotechnical stability analysis considering strain softening using micro-polar continuum finite element method

Cited by 34 publications

References 28 publications

Expression of Concern: Application of probability decision system and particle swarm optimization for improving soil moisture content

Expression of Concern: Application of probability decision system and particle swarm optimization for improving soil moisture content

Expression of Concern: Fuzzy-probabilistic modeling the flood characteristics using bivariate frequency analysis and α-cut decomposition

Evaluating the climate change effects on temperature, precipitation and evapotranspiration in eastern Iran using CMPI5

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