Prof. Yuen (PhD, Caltech) is the Dean of Graduate School and a Distinguished Professor at the University of Macau. His expertise includes Bayesian inference, model selection, system identification structural health monitoring, vibration control and reliability analysis.
In this study, a simplified axisymmetric model is built to simulate a column supported embankment... more In this study, a simplified axisymmetric model is built to simulate a column supported embankment system. The model is based on a cylindrical unit cell that contains one column with the surrounding soil and a layer of overlying embankment fill. The deformation of the column with the surrounding soil is simulated using a deformed shape function. The embankment fill is divided into an inner cylinder and an outer hollow cylinder to simulate the soil arching effect. The stress continuity and volume deformation continuity are applied to combine the behavior of the embankment fill and that of the column-reinforced foundation together. A semi-analytical solution is obtained, and it is verified using a finite element analysis and a case study. After that, parametric studies are put forward to evaluate the load transfer mechanism within the embankment fill, the shear stress at the interface between the column and the surrounding soil, and the vertical stress distribution within the column. The influences of the column modulus, the spacing between columns, the height of the embankment fill, and the length of the column on the soil arching effect are investigated and discussed. It is concluded that when the column modulus becomes larger, the stress ratio between the column and the surrounding soil increases correspondingly. The height of equal settlement plane is close to the net spacing between columns, but it changes slightly with a change in the column modulus.
Records of precipitation in Macau are collected from three local monitoring stations from 1993 to... more Records of precipitation in Macau are collected from three local monitoring stations from 1993 to 2003. Chemical compositions of precipitation are analyzed to reveal the causes of wet deposition inMacau. The review of volume weighted annual pH values over this period shows that the entire area of Macau 2 is subjected to acidic precipitation. The concentrations of three ionic species H+, SO42- and NO3- are analyzed statistically and their distributions are found to have positive skewness and kurtosis. Lognormal cumulative distribution function fits each sample cumulative distribution function for each ionic species well implying that their temporal behaviours are governed according to the lognormal distribution. Nonetheless, the distribution of each ionic species exhibits spatial variability across the stations. Therefore, it is suspected that the sources of wet acid deposition indifferent parts of Macau are different and further correlation analysis on the values of pH, sulfate, and...
Computer-Aided Civil and Infrastructure Engineering, 2019
In this article, a novel Bayesian framework is proposed for real-time system identification with ... more In this article, a novel Bayesian framework is proposed for real-time system identification with calibratable model classes. This self-calibrating scheme adaptively reconfigures the model classes to achieve reliable real-time estimation for the system state and model parameters. At each time step, the plausibilities of the model classes are computed and they serve as the cue for calibration. Once calibration is triggered, all model classes will be reconfigured. Thereafter, identification will continue to propagate with the calibrated model classes until the next recalibration. Consequently, the model classes will evolve and their deficiencies can be corrected adaptively. This remarkable feature of the proposed framework stimulates the accessibility of reliable real-time system identification. Examples are presented to demonstrate the efficacy of the proposed approach using noisy response measurement of linear and nonlinear time-varying dynamical systems under stationary condition.
Computer Methods in Applied Mechanics and Engineering, 2019
A general framework for quantifying bounded field uncertainties in loading conditions, material p... more A general framework for quantifying bounded field uncertainties in loading conditions, material properties and geometrical dimensions is developed in this study. By using a nonprobabilistic series expansion (NPSE) method similar as the Expansion Optimal Linear Estimator (EOLE), the bounded field uncertainties with certain spatial correlation characteristic are modelled with a reduced set of uncertain-but-bounded coefficients. Further, it is shown that these coefficients are bounded by a multi-ellipsoid convex model. The gradient-based mathematical programming algorithm combined with an efficient adjoint variable sensitivity scheme is then employed to evaluate the upper and lower bounds of structural performance. The proposed method allows spatially varying uncertainties as well as their dependencies to be described in a non-probabilistic framework, which ensures the objectivity and accuracy of representations of bounded field uncertainties. Moreover, it provides an efficient way to evaluate the variation range of structural performance with a significant reduction of computational cost compared to direct treatments. Numerical examples regarding the performance bound evaluation of structures with bounded field uncertainties are presented to illustrate the validity and applicability of this method.
In this study, a new dual beam model was proposed for a geosynthetic-reinforced granular fill wit... more In this study, a new dual beam model was proposed for a geosynthetic-reinforced granular fill with an upper pavement. This dual beam model was subjected to a uniform surcharge loading and resting on an elastic foundation which was simulated by a Pasternak model. The upper pavement was modeled by an Euler-Bernoulli beam while the geosynthetic reinforced granular fill was simulated by a reinforced Timoshenko beam. The explicit derivation process for the behavior of this dual beam-foundation system was presented and an exact solution was obtained. A two-dimensional finite element analysis and a Pasternak model for simulating the granular fill were carried out to validate the reliability of the proposed dual beam model. A parametric analysis was put forward to investigate the behavior of this dual beam-foundation system. It was found that the length of the pavement structure and vertical uniform loading, the stiffness and shear modulus of the foundation soil had significant influences on the behavior of the dual beam-foundation system.
Abstract Extended Kalman filter (EKF) is widely adopted for state estimation and parametric ident... more Abstract Extended Kalman filter (EKF) is widely adopted for state estimation and parametric identification of dynamical systems. In this algorithm, it is required to specify the covariance matrices of the process noise and measurement noise based on prior knowledge. However, improper assignment of these noise covariance matrices leads to unreliable estimation and misleading uncertainty estimation on the system state and model parameters. Furthermore, it may induce diverging estimation. To resolve these problems, we propose a Bayesian probabilistic algorithm for online estimation of the noise parameters which are used to characterize the noise covariance matrices. There are three major appealing features of the proposed approach. First, it resolves the divergence problem in the conventional usage of EKF due to improper choice of the noise covariance matrices. Second, the proposed approach ensures the reliability of the uncertainty quantification. Finally, since the noise parameters are allowed to be time-varying, nonstationary process noise and/or measurement noise are explicitly taken into account. Examples using stationary/nonstationary response of linear/nonlinear time-varying dynamical systems are presented to demonstrate the efficacy of the proposed approach. Furthermore, comparison with the conventional usage of EKF will be provided to reveal the necessity of the proposed approach for reliable model updating and uncertainty quantification.
The soil–water characteristic curve (SWCC) of fine-grained soils is usually determined experiment... more The soil–water characteristic curve (SWCC) of fine-grained soils is usually determined experimentally. In the design of mine waste covers and landfill liners, the unsaturated hydraulic conductivity function, k(h), is often derived theoretically from the measured SWCC. Implicit in these derivations is the transformation of the SWCC to a pore-size distribution (PSD), typically assumed to be constant and monomodal. However, PSD measurements of a clayey till compacted at various water contents after compaction, after flexible-wall permeability testing and before and after SWCC tests show that the PSD of the same material varies significantly under the stated physical conditions. Predictions of the SWCCs using PSDs measured both before and after the SWCC tests significantly underpredicted the values measured. By applying a simple transformation to the PSD to account for the scaling effect from the porosimetry samples (approximately 1 g dry weight) to the SWCC test samples (approximately 200 g dry weight), the ...
AbstractThe pullout resistance of soil nail is a key parameter in soil nailing design. Pullout re... more AbstractThe pullout resistance of soil nail is a key parameter in soil nailing design. Pullout resistance is affected by many factors, such as overburden pressure, grouting pressure, soil dilation, and degree of saturation of soil. Because of the complexity of the pullout mechanism, some factors have not been well incorporated in the current soil nail design methods. In this study, Bayesian analysis is performed to investigate the relative importance of several key factors and to build a new design formula to estimate maximum pullout shear stress of grouted soil nails. By using a series of laboratory soil nail pullout test data, Bayesian analysis is performed to select a predictive formula with suitable complexity and to identify its parameters. It is found that the most important factors are the degree of saturation and the product of grouting pressure and overburden pressure. It is shown that the proposed optimal model exhibits significantly stronger correlation with measurements than the existing effec...
Earthquake Engineering and Engineering Vibration, 2016
Evaluation of the cyclic shear modulus of soils is a crucial but challenging task for many geotec... more Evaluation of the cyclic shear modulus of soils is a crucial but challenging task for many geotechnical earthquake engineering and soil dynamic issues. Improper determination of this property unnecessarily drives up design and maintenance costs or even leads to the construction of unsafe structures. Due to the complexities involved in the direct measurement, empirical curves for estimating the cyclic shear modulus have been commonly adopted in practice for simplicity and economical considerations. However, a systematic and robust approach for formulating a reliable model and empirical curve for cyclic shear modulus prediction for clayey soils is still lacking. In this study, the Bayesian model class selection approach is utilized to identify the most significant soil parameters affecting the normalized cyclic shear modulus and a reliable predictive model for normally to moderately over-consolidated clays is proposed. Results show that the predictability and reliability of the proposed model out performs the well-known empirical models. Finally, a new design chart is established for practical usage.
The present study introduces the novel Bayesian approach to solve a difficult problem that modell... more The present study introduces the novel Bayesian approach to solve a difficult problem that modellers face when doing linear regression for air quality prediction; i.e. the non-uniqueness of the input variables and the functional forms to be selected. Using the historical information of ozone, nitrogen dioxide, respirable particulates and eight meteorological elements recorded during the high ozone seasons (May-October) in Macau between 2006 and 2007 as the training data, the Bayesian approach was applied for model selection from 16383 candidates. The model with the best efficiency-robustness balance selected is not the most complicated one. It was then examined against the most complicated model with the data between 2008 and 2009. Results show that the selected model yields better performance with the root-mean-squared error (RMSE) and the coefficient of determination (r2) equal to 19.18 µg/m3 and 0.81, respectively. In addition, it is capable to capture 87% of the ozone episodes w...
The aim of this paper is to present a novel attempt for parametric estimation in the hydrostatic-... more The aim of this paper is to present a novel attempt for parametric estimation in the hydrostatic-season-time (HST) model. The empirical HST-model has been widely used for the analysis of different measurement data types on dams. The significance of individual parameters or their sub-groups for modelling the influence of the water level, air and water temperature, and irreversible deformations due to the ageing of the dam, depends on the structure itself. The process of finding an accurate HST-model for a given data set, which remains robust to outliers, cannot only be demanding but also time consuming. The Bayesian model class selection approach imposes a penalisation against overly complex model candidates and admits a selection of the most plausible HST-model according to the maximum value of model evidence provided by the data or relative plausibility within a set of model class candidates. The potential of Bayes interference and its efficiency in an HST-model are presented on geodetic time series as a result of a permanent monitoring system on a rock-fill embankment dam. The method offers high potential for engineers in the decision making process, whilst the HST-model can be promptly adapted to new information given by new measurements and can enhance the safety and reliability of dams.
This study presents a method of predicting the soil water retention curve (SWRC) of a soil using ... more This study presents a method of predicting the soil water retention curve (SWRC) of a soil using a set of measured SWRC data from a soil with the same texture but different initial void ratio. The relationships of the volumetric water contents and the matric suctions between two samples with different initial void ratios are established. An adjustment parameter (β) is introduced to express the relationships between the matric suctions of two soil samples. The parameter β is a function of the initial void ratio, matric suction or volumetric water content. The function can take different forms, resulting in different predictive models. The optimal predictive models of β are determined for coarse-grained and fine-grained soils using the Bayesian method. The optimal models of β are validated by comparing the estimated matric suction and measured data. The comparisons show that the proposed method produces more accurate SWRCs than do other models for both coarse-grained and fine-grained soils. Furthermore, the influence of the model parameters of β on the predicted matric suction and SWRC is evaluated using Latin Hypercube sampling. An uncertainty analysis shows that the reliability of the predicted SWRC decreases with decreasing water content in fine-grained soils, and the initial void ratio has no apparent influence on the reliability of the predicted SWRCs in coarse-grained and fine-grained soils.
A probabilistic approach for damage detection is presented using noisy incomplete input and respo... more A probabilistic approach for damage detection is presented using noisy incomplete input and response measurements that is an extension of a Bayesian system identification approach developed by the authors. This situation may be encountered, for example, during low-level ambient vibrations when a structure is instrumented with accelerometers that measure the input ground motion and structural response at a few locations but the wind excitation is not measured. A substructuring approach is used for the parameterization of the mass and stiffness distributions. Damage is defined to be a reduction of the substructure stiffness parameters compared with those of the undamaged structure. By using the proposed probabilistic methodology, the probability of various damage levels in each substructure can be calculated based on the available data. A four-story benchmark building subjected to wind and ground shaking is considered in order to demonstrate the proposed approach.
In this study, a simplified axisymmetric model is built to simulate a column supported embankment... more In this study, a simplified axisymmetric model is built to simulate a column supported embankment system. The model is based on a cylindrical unit cell that contains one column with the surrounding soil and a layer of overlying embankment fill. The deformation of the column with the surrounding soil is simulated using a deformed shape function. The embankment fill is divided into an inner cylinder and an outer hollow cylinder to simulate the soil arching effect. The stress continuity and volume deformation continuity are applied to combine the behavior of the embankment fill and that of the column-reinforced foundation together. A semi-analytical solution is obtained, and it is verified using a finite element analysis and a case study. After that, parametric studies are put forward to evaluate the load transfer mechanism within the embankment fill, the shear stress at the interface between the column and the surrounding soil, and the vertical stress distribution within the column. The influences of the column modulus, the spacing between columns, the height of the embankment fill, and the length of the column on the soil arching effect are investigated and discussed. It is concluded that when the column modulus becomes larger, the stress ratio between the column and the surrounding soil increases correspondingly. The height of equal settlement plane is close to the net spacing between columns, but it changes slightly with a change in the column modulus.
Records of precipitation in Macau are collected from three local monitoring stations from 1993 to... more Records of precipitation in Macau are collected from three local monitoring stations from 1993 to 2003. Chemical compositions of precipitation are analyzed to reveal the causes of wet deposition inMacau. The review of volume weighted annual pH values over this period shows that the entire area of Macau 2 is subjected to acidic precipitation. The concentrations of three ionic species H+, SO42- and NO3- are analyzed statistically and their distributions are found to have positive skewness and kurtosis. Lognormal cumulative distribution function fits each sample cumulative distribution function for each ionic species well implying that their temporal behaviours are governed according to the lognormal distribution. Nonetheless, the distribution of each ionic species exhibits spatial variability across the stations. Therefore, it is suspected that the sources of wet acid deposition indifferent parts of Macau are different and further correlation analysis on the values of pH, sulfate, and...
Computer-Aided Civil and Infrastructure Engineering, 2019
In this article, a novel Bayesian framework is proposed for real-time system identification with ... more In this article, a novel Bayesian framework is proposed for real-time system identification with calibratable model classes. This self-calibrating scheme adaptively reconfigures the model classes to achieve reliable real-time estimation for the system state and model parameters. At each time step, the plausibilities of the model classes are computed and they serve as the cue for calibration. Once calibration is triggered, all model classes will be reconfigured. Thereafter, identification will continue to propagate with the calibrated model classes until the next recalibration. Consequently, the model classes will evolve and their deficiencies can be corrected adaptively. This remarkable feature of the proposed framework stimulates the accessibility of reliable real-time system identification. Examples are presented to demonstrate the efficacy of the proposed approach using noisy response measurement of linear and nonlinear time-varying dynamical systems under stationary condition.
Computer Methods in Applied Mechanics and Engineering, 2019
A general framework for quantifying bounded field uncertainties in loading conditions, material p... more A general framework for quantifying bounded field uncertainties in loading conditions, material properties and geometrical dimensions is developed in this study. By using a nonprobabilistic series expansion (NPSE) method similar as the Expansion Optimal Linear Estimator (EOLE), the bounded field uncertainties with certain spatial correlation characteristic are modelled with a reduced set of uncertain-but-bounded coefficients. Further, it is shown that these coefficients are bounded by a multi-ellipsoid convex model. The gradient-based mathematical programming algorithm combined with an efficient adjoint variable sensitivity scheme is then employed to evaluate the upper and lower bounds of structural performance. The proposed method allows spatially varying uncertainties as well as their dependencies to be described in a non-probabilistic framework, which ensures the objectivity and accuracy of representations of bounded field uncertainties. Moreover, it provides an efficient way to evaluate the variation range of structural performance with a significant reduction of computational cost compared to direct treatments. Numerical examples regarding the performance bound evaluation of structures with bounded field uncertainties are presented to illustrate the validity and applicability of this method.
In this study, a new dual beam model was proposed for a geosynthetic-reinforced granular fill wit... more In this study, a new dual beam model was proposed for a geosynthetic-reinforced granular fill with an upper pavement. This dual beam model was subjected to a uniform surcharge loading and resting on an elastic foundation which was simulated by a Pasternak model. The upper pavement was modeled by an Euler-Bernoulli beam while the geosynthetic reinforced granular fill was simulated by a reinforced Timoshenko beam. The explicit derivation process for the behavior of this dual beam-foundation system was presented and an exact solution was obtained. A two-dimensional finite element analysis and a Pasternak model for simulating the granular fill were carried out to validate the reliability of the proposed dual beam model. A parametric analysis was put forward to investigate the behavior of this dual beam-foundation system. It was found that the length of the pavement structure and vertical uniform loading, the stiffness and shear modulus of the foundation soil had significant influences on the behavior of the dual beam-foundation system.
Abstract Extended Kalman filter (EKF) is widely adopted for state estimation and parametric ident... more Abstract Extended Kalman filter (EKF) is widely adopted for state estimation and parametric identification of dynamical systems. In this algorithm, it is required to specify the covariance matrices of the process noise and measurement noise based on prior knowledge. However, improper assignment of these noise covariance matrices leads to unreliable estimation and misleading uncertainty estimation on the system state and model parameters. Furthermore, it may induce diverging estimation. To resolve these problems, we propose a Bayesian probabilistic algorithm for online estimation of the noise parameters which are used to characterize the noise covariance matrices. There are three major appealing features of the proposed approach. First, it resolves the divergence problem in the conventional usage of EKF due to improper choice of the noise covariance matrices. Second, the proposed approach ensures the reliability of the uncertainty quantification. Finally, since the noise parameters are allowed to be time-varying, nonstationary process noise and/or measurement noise are explicitly taken into account. Examples using stationary/nonstationary response of linear/nonlinear time-varying dynamical systems are presented to demonstrate the efficacy of the proposed approach. Furthermore, comparison with the conventional usage of EKF will be provided to reveal the necessity of the proposed approach for reliable model updating and uncertainty quantification.
The soil–water characteristic curve (SWCC) of fine-grained soils is usually determined experiment... more The soil–water characteristic curve (SWCC) of fine-grained soils is usually determined experimentally. In the design of mine waste covers and landfill liners, the unsaturated hydraulic conductivity function, k(h), is often derived theoretically from the measured SWCC. Implicit in these derivations is the transformation of the SWCC to a pore-size distribution (PSD), typically assumed to be constant and monomodal. However, PSD measurements of a clayey till compacted at various water contents after compaction, after flexible-wall permeability testing and before and after SWCC tests show that the PSD of the same material varies significantly under the stated physical conditions. Predictions of the SWCCs using PSDs measured both before and after the SWCC tests significantly underpredicted the values measured. By applying a simple transformation to the PSD to account for the scaling effect from the porosimetry samples (approximately 1 g dry weight) to the SWCC test samples (approximately 200 g dry weight), the ...
AbstractThe pullout resistance of soil nail is a key parameter in soil nailing design. Pullout re... more AbstractThe pullout resistance of soil nail is a key parameter in soil nailing design. Pullout resistance is affected by many factors, such as overburden pressure, grouting pressure, soil dilation, and degree of saturation of soil. Because of the complexity of the pullout mechanism, some factors have not been well incorporated in the current soil nail design methods. In this study, Bayesian analysis is performed to investigate the relative importance of several key factors and to build a new design formula to estimate maximum pullout shear stress of grouted soil nails. By using a series of laboratory soil nail pullout test data, Bayesian analysis is performed to select a predictive formula with suitable complexity and to identify its parameters. It is found that the most important factors are the degree of saturation and the product of grouting pressure and overburden pressure. It is shown that the proposed optimal model exhibits significantly stronger correlation with measurements than the existing effec...
Earthquake Engineering and Engineering Vibration, 2016
Evaluation of the cyclic shear modulus of soils is a crucial but challenging task for many geotec... more Evaluation of the cyclic shear modulus of soils is a crucial but challenging task for many geotechnical earthquake engineering and soil dynamic issues. Improper determination of this property unnecessarily drives up design and maintenance costs or even leads to the construction of unsafe structures. Due to the complexities involved in the direct measurement, empirical curves for estimating the cyclic shear modulus have been commonly adopted in practice for simplicity and economical considerations. However, a systematic and robust approach for formulating a reliable model and empirical curve for cyclic shear modulus prediction for clayey soils is still lacking. In this study, the Bayesian model class selection approach is utilized to identify the most significant soil parameters affecting the normalized cyclic shear modulus and a reliable predictive model for normally to moderately over-consolidated clays is proposed. Results show that the predictability and reliability of the proposed model out performs the well-known empirical models. Finally, a new design chart is established for practical usage.
The present study introduces the novel Bayesian approach to solve a difficult problem that modell... more The present study introduces the novel Bayesian approach to solve a difficult problem that modellers face when doing linear regression for air quality prediction; i.e. the non-uniqueness of the input variables and the functional forms to be selected. Using the historical information of ozone, nitrogen dioxide, respirable particulates and eight meteorological elements recorded during the high ozone seasons (May-October) in Macau between 2006 and 2007 as the training data, the Bayesian approach was applied for model selection from 16383 candidates. The model with the best efficiency-robustness balance selected is not the most complicated one. It was then examined against the most complicated model with the data between 2008 and 2009. Results show that the selected model yields better performance with the root-mean-squared error (RMSE) and the coefficient of determination (r2) equal to 19.18 µg/m3 and 0.81, respectively. In addition, it is capable to capture 87% of the ozone episodes w...
The aim of this paper is to present a novel attempt for parametric estimation in the hydrostatic-... more The aim of this paper is to present a novel attempt for parametric estimation in the hydrostatic-season-time (HST) model. The empirical HST-model has been widely used for the analysis of different measurement data types on dams. The significance of individual parameters or their sub-groups for modelling the influence of the water level, air and water temperature, and irreversible deformations due to the ageing of the dam, depends on the structure itself. The process of finding an accurate HST-model for a given data set, which remains robust to outliers, cannot only be demanding but also time consuming. The Bayesian model class selection approach imposes a penalisation against overly complex model candidates and admits a selection of the most plausible HST-model according to the maximum value of model evidence provided by the data or relative plausibility within a set of model class candidates. The potential of Bayes interference and its efficiency in an HST-model are presented on geodetic time series as a result of a permanent monitoring system on a rock-fill embankment dam. The method offers high potential for engineers in the decision making process, whilst the HST-model can be promptly adapted to new information given by new measurements and can enhance the safety and reliability of dams.
This study presents a method of predicting the soil water retention curve (SWRC) of a soil using ... more This study presents a method of predicting the soil water retention curve (SWRC) of a soil using a set of measured SWRC data from a soil with the same texture but different initial void ratio. The relationships of the volumetric water contents and the matric suctions between two samples with different initial void ratios are established. An adjustment parameter (β) is introduced to express the relationships between the matric suctions of two soil samples. The parameter β is a function of the initial void ratio, matric suction or volumetric water content. The function can take different forms, resulting in different predictive models. The optimal predictive models of β are determined for coarse-grained and fine-grained soils using the Bayesian method. The optimal models of β are validated by comparing the estimated matric suction and measured data. The comparisons show that the proposed method produces more accurate SWRCs than do other models for both coarse-grained and fine-grained soils. Furthermore, the influence of the model parameters of β on the predicted matric suction and SWRC is evaluated using Latin Hypercube sampling. An uncertainty analysis shows that the reliability of the predicted SWRC decreases with decreasing water content in fine-grained soils, and the initial void ratio has no apparent influence on the reliability of the predicted SWRCs in coarse-grained and fine-grained soils.
A probabilistic approach for damage detection is presented using noisy incomplete input and respo... more A probabilistic approach for damage detection is presented using noisy incomplete input and response measurements that is an extension of a Bayesian system identification approach developed by the authors. This situation may be encountered, for example, during low-level ambient vibrations when a structure is instrumented with accelerometers that measure the input ground motion and structural response at a few locations but the wind excitation is not measured. A substructuring approach is used for the parameterization of the mass and stiffness distributions. Damage is defined to be a reduction of the substructure stiffness parameters compared with those of the undamaged structure. By using the proposed probabilistic methodology, the probability of various damage levels in each substructure can be calculated based on the available data. A four-story benchmark building subjected to wind and ground shaking is considered in order to demonstrate the proposed approach.
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Papers by Ka-veng Yuen