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| 1 | Uncertainty analysis of correlated non-normal geotechnical parameters using Gaussian copula显示文摘Determining the joint probability distribution of correlated non-normal geotechnical parameters based on incomplete statistical data is a challenging problem.This paper proposes a Gaussian copula-based method for modelling the joint probability distribution of bivariate uncertain data.First,the concepts of Pearson and Kendall correlation coefficients are presented,and the copula theory is briefly introduced.Thereafter,a Pearson method and a Kendall method are developed to determine the copula parameter underlying Gaussian copula.Second,these two methods are compared in computational efficiency,applicability,and capability of fitting data.Finally,four load-test datasets of load-displacement curves of piles are used to illustrate the proposed method.The results indicate that the proposed Gaussian copula-based method can not only characterize the correlation between geotechnical parameters,but also construct the joint probability distribution function of correlated non-normal geotechnical parameters in a more general way.It can serve as a general tool to construct the joint probability distribution of correlated geotechnical parameters based on incomplete data.The Gaussian copula using the Kendall method is superior to that using the Pearson method,which should be recommended for modelling and simulating the joint probability distribution of correlated geotechnical parameters.There exists a strong negative correlation between the two parameters underlying load-displacement curves.Neglecting such correlation will not capture the scatter in the measured load-displacement curves.These results substantially extend the application of the copula theory to multivariate simulation in geotechnical engineering. | LI DianQing TANG XiaoSong ZHOU ChuangBing PHOON Kok-Kwang | 2012 | Science China(Technological Sciences)2012，55，11： | 10 |
| 2 | Determination of site-specific soil-water characteristic curve from a limited number of test data-A Bayesian perspective显示文摘Determining soilewater characteristic curve(SWCC) at a site is an essential step for implementing unsaturated soil mechanics in geotechnical engineering practice, which can be measured directly through various in-situ and/or laboratory tests. Such direct measurements are, however, costly and timeconsuming due to high standards for equipment and procedural control and limits in testing apparatus. As a result, only a limited number of data points(e.g., volumetric water content vs. matric suction)on SWCC at some values of matric suction are obtained in practice. How to use a limited number of data points to estimate the site-specific SWCC and to quantify the uncertainty(or degrees-of-belief) in the estimated SWCC remains a challenging task. This paper proposes a Bayesian approach to determine a site-specific SWCC based on a limited number of test data and prior knowledge(e.g., engineering experience and judgment). The proposed Bayesian approach quantifies the degrees-of-belief on the estimated SWCC according to site-specific test data and prior knowledge, and simultaneously selects a suitable SWCC model from a number of candidates based on the probability logic. To address computational issues involved in Bayesian analyses, Markov Chain Monte Carlo Simulation(MCMCS), specifically Metropolis-Hastings(M-H) algorithm, is used to solve the posterior distribution of SWCC model parameters, and Gaussian copula is applied to evaluating model evidence based on MCMCS samples for selecting the most probable SWCC model from a pool of candidates. This removes one key limitation of the M-H algorithm, making it feasible in Bayesian model selection problems. The proposed approach is illustrated using real data in Unsaturated Soil Database(UNSODA) developed by U.S. Department of Agriculture. It is shown that the proposed approach properly estimates the SWCC based on a limited number of site-specific test data and prior knowledge, and reflects the degrees-of-belief on the estimated SWCC in a rational and quantitative manner. | Lin Wang Zi-Jun Cao Dian-Qing Li Kok-Kwang Phoon Siu-Kui Au | 2018 | Geoscience Frontiers2018，9，6： | 6 |
| 3 | Editorial for Advances and applications of deep learning and soft computing in geotechnical underground engineering显示文摘We are privileged to be invited by the Honorary Editor-in-Chief,Professor Qihu Qian,Editor-in-Chief,Professor Xia-Ting Feng,and the editorial staff of the Journal of Rock Mechanics and Geotechnical Engineering(JRMGE),to serve as Guest Editors for this Special Issue(SI).The purpose of this SI is to review the latest development of machine learning(ML)techniques including the soft computing(SC)and deep learning(DL)methods as well as their key applications in geotechnical underground engineering problems. | Wengang Zhang Kok-Kwang Phoon | 2022 | Journal of Rock Mechanics and Geotechnical Engineering2022，14，3： | 3 |
| 4 | The largest outbreak of hand; foot and mouth disease in Singapore in 2008: The role of enterovirus 71 and coxsackievirus A strains显示文摘 | Yan Wu Andrea Yeo M.C. Phoon E.L. Tan C.L. Poh S.H. Quak Vincent T.K. Chow | 2010 | International Journal of Infectious Diseases2010，，： | 3 |
| 5 | Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning显示文摘Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances(i.e.,outliers)that do not conform with the expected pattern of regular data instances.With sparse multivariate data obtained from geotechnical site investigation,it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity.This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation.The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5.It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts,rationally,for the statistical uncertainty by Bayesian machine learning.Moreover,the proposed approach also suggests an exclusive method to determine outlying components of each outlier.The proposed approach is illustrated and verified using simulated and real-life dataset.It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner.It can significantly reduce the masking effect(i.e.,missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty).It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification.This emphasizes the necessity of data cleaning process(e.g.,outlier detection)for uncertainty quantification based on geoscience data. | Shuo Zheng Yu-Xin Zhu Dian-Qing Li Zi-Jun Cao Qin-Xuan Deng Kok-Kwang Phoon | 2021 | Geoscience Frontiers2021，12，1： | 2 |
| 6 | Deep learning-based evaluation of factor of safety with confidence interval for tunnel deformation in spatially variable soil显示文摘The random finite difference method(RFDM) is a popular approach to quantitatively evaluate the influence of inherent spatial variability of soil on the deformation of embedded tunnels.However,the high computational cost is an ongoing challenge for its application in complex scenarios.To address this limitation,a deep learning-based method for efficient prediction of tunnel deformation in spatially variable soil is proposed.The proposed method uses one-dimensional convolutional neural network(CNN) to identify the pattern between random field input and factor of safety of tunnel deformation output.The mean squared error and correlation coefficient of the CNN model applied to the newly untrained dataset was less than 0.02 and larger than 0.96,respectively.It means that the trained CNN model can replace RFDM analysis for Monte Carlo simulations with a small but sufficient number of random field samples(about 40 samples for each case in this study).It is well known that the machine learning or deep learning model has a common limitation that the confidence of predicted result is unknown and only a deterministic outcome is given.This calls for an approach to gauge the model’s confidence interval.It is achieved by applying dropout to all layers of the original model to retrain the model and using the dropout technique when performing inference.The excellent agreement between the CNN model prediction and the RFDM calculated results demonstrated that the proposed deep learning-based method has potential for tunnel performance analysis in spatially variable soils. | Jinzhang Zhang Kok Kwang Phoon Dongming Zhang Hongwei Huang Chong Tang | 2021 | Journal of Rock Mechanics and Geotechnical Engineering2021，13，6： | 2 |
| 7 | Seroepidemiology of human enterovirus 71, Singapore 显示文摘 | Ooi EE Phoon MC Ishak B | 2002 | Emerg Infect Dis2002，8，9： | 1 |
| 8 | A systematic approach to noise reduction in observed chaotic time series显示文摘 | Sivakumar B Phoon K K Liong S Y | 1999 | Journal of Hydrology1999，219，34： | 1 |
| 9 | Convergence study of the truncated Karhunen-Loeve expansion for simulation of stochastic processes 显示文摘 | Huang S P Quek S T Phoon K K | 2001 | International Journal for Numerical Methods in Engineering2001，52，9： | 1 |
| 10 | Probabilistic an-alysis of soil-water characteristic curves显示文摘 | Phoon K K Santoso A Quek S T | | 0，，： | 1 |
| 11 | Left atrial isomerism detected in fetal life显示文摘 | Phoon CK Villegas MD Ursell PC | | 0，，12： | 1 |
| 12 | Signal transducer and activation of transcription 6 (STAT6) regulates T helper type 1 (Thl) and Thl7 nephritogenic immunity in experimental crescentic glomerulonephritis显示文摘 | Summers SA Phoon RK Odobasic D | 2011 | Clin Exp Immunol2011，166，2： | 1 |
| 13 | A modified SSOR preconditioner for sparse symmetric indefinite linear systems of equations 显示文摘 | Chen X Toh K C Phoon K K | 2006 | International Journal for Numerical Methods in Engineering2006，65，6： | 1 |
| 14 | Lack of association between chronic Chlamydophila pneumoniae infection and lung cancer among nonsmoking Chinese women in Singapore显示文摘 | Koh WP Chow VT Phoon MC | 2005 | Int J Cancer2005，114，3： | 1 |
| 15 | Bivariate simu- lation using copula and its application to probabilistic pile settlement analysis 显示文摘 | Li D Q Tang X S Phoon K K | 2013 | International Journal for Numerical and Analytical Methods in Geomechanics2013，37，6： | 1 |
| 16 | Bivariate simulation using Copula and its application to probabilistic pile settlement analysis显示文摘 | LI D Q TANG X S PHOON K K | 2013 | International Journal for Numerical and Analytical Methods in Geomechanics2013，37，6： | 1 |
| 17 | Seroepidemiology of human entero virus 71 S i ngapore 显示文摘 | Ooi EE Phoon MC lshak B | 2002 | Emerg Infect Dis2002，8，9： | 1 |
| 18 | Seroepidemiology of human enterovirus 71, Singapore显示文摘 | Ooi EE Phoon MC Ishak B | 2002 | Emerg Infect Dis2002，8，9： | 1 |
| 19 | A modified Jacobi preconditioner for solving ill-conditioned Biot's consolidation equations using symmetric quasi-minimal residual method显示文摘 | CHAN S H PHOON K K LEE F H | 2001 | International Journal for Numerical and Analytical Methods in Geomechanics2001，25，10： | 1 |
| 20 | Characterization of model uncertainty in the static pile design formula显示文摘 | DITHINDE M PHOON K K WET M D | 2011 | Journal of Geoteehnical and Geoenvironmental Engineering2011，137，1： | 1 |
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