In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

http://www.ripid.ethz.ch/Paper/DerKiureghian_paper.pdf

Aleatory or epistemic? Does it matter?

Dynamics of structures

Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This presentation will summarize the outcome of an updated review covering the years 1996 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which addresses four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling for Feature Discrimination. The literature has been reviewed based on how a particular study addresses these four topics. A significant observation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond demonstration problems carried out in laboratory setting.

A review of structural health monitoring literature 1996-2001

A Bayesian probabilistic approach is presented for selecting the most plausible class of models for a structural or mechanical
system within some specified set of model classes, based on system response data. The crux of the approach is to rank the classes of
models based on their probabilities conditional on the response data which can be calculated based on Bayes’ theorem and an asymptotic
expansion for the evidence for each model class. The approach provides a quantitative expression of a principle of model parsimony or
of Ockham’s razor which in this context can be stated as "simpler models are to be preferred over unnecessarily complicated ones."
Examples are presented to illustrate the method using a single-degree-of-freedom bilinear hysteretic system, a linear two-story frame, and
a ten-story shear building, all of which are subjected to seismic excitation.

/pdf/model-selection-using-response-measurements-bayesian-3avee0ip8q.pdf

Model Selection using Response Measurements: Bayesian Probabilistic Approach

Contents Preface Nomenclature 1 Introduction 1.1 Thomas Bayes and Bayesian Methods in Engineering 1.2 Purpose of Model Updating 1.3 Source of Uncertainty and Bayesian Updating 1.4 Organization of the Book 2 Basic Concepts and Bayesian Probabilistic Framework 2.1 Conditional Probability and Basic Concepts 2.2 Bayesian Model Updating with Input-output Measurements 2.3 Deterministic versus Probabilistic Methods 2.4 Regression Problems 2.5 Numerical Representation of the Updated PDF 2.6 Application to Temperature Effects on Structural Behavior 2.7 Application to Noise Parameters Selection for Kalman Filter 2.8 Application to Prediction of Particulate Matter Concentration 3 Bayesian Spectral Density Approach 3.1 Modal and Model Updating of Dynamical Systems 3.2 Random Vibration Analysis 3.3 Bayesian Spectral Density Approach 3.4 Numerical Verifications 3.5 Optimal Sensor Placement 3.6 Updating of a Nonlinear Oscillator 3.7 Application to Structural Behavior under Typhoons 3.8 Application to Hydraulic Jump 4 Bayesian Time-domain Approach 4.1 Introduction 4.2 Exact Bayesian Formulation and its Computational Difficulties 4.3 Random Vibration Analysis of Nonstationary Response 4.4 Bayesian Updating with Approximated PDF Expansion 4.5 Numerical Verification 4.6 Application to Model Updating with Unmeasured Earthquake Ground Motion 4.7 Concluding Remarks 4.8 Comparison of Spectral Density Approach and Time-domain Approach 4.9 Extended Readings 5 Model Updating Using Eigenvalue-Eigenvector Measurements 5.1 Introduction 5.2 Formulation 5.3 Linear Optimization Problems 5.4 Iterative Algorithm 5.5 Uncertainty Estimation 5.6 Applications to Structural Health Monitoring 5.7 Concluding Remarks 6 Bayesian Model Class Selection 6.1 Introduction 6.2 Bayesian Model Class Selection 6.3 Model Class Selection for Regression Problems 6.4 Application to Modal Updating 6.5 Application to Seismic Attenuation Empirical Relationship 6.6 Prior Distributions - Revisited 6.7 Final Remarks A Relationship between the Hessian and Covariance Matrix for Gaussian Random Variables B Contours of Marginal PDFs for Gaussian Random Variables C Conditional PDF for Prediction C.1 Two Random Variables C.2 General Cases References Index

Bayesian Methods for Structural Dynamics and Civil Engineering

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.

/pdf/overview-of-environment-perception-for-intelligent-vehicles-7s8l9oth23.pdf

Overview of Environment Perception for Intelligent Vehicles

Strain-based non-intrusive approaches for measuring the pressure of pipes have attracted widespread attention due to their great convenience and ability to avoid destroying the integrity of structures. However, the mentioned method usually measures the dynamic pressure based only on the static strain–pressure sensitivity coefficients (SSSCs) instead of the dynamic strain–pressure sensitivity coefficients (DSSCs) due to its complicated calibration, which will inevitably affect the accuracy significantly. To address this issue, a model-driven scheme with dual stages is proposed in the present study to compensate the dynamic pressure measurement. The DSSCs are analytically derived for the first time from the axial governing equations of the pipe, considering the general boundary conditions for the thin-walled and thick-walled pipes simultaneously. In the first stage, the physical parameters involved in the DSSCs are calibrated by minimizing the residual of the experimental results and the theoretical counterparts. In the second stage, the DSSCs calculated from the calibrated analytical model are utilized to compensate the dynamic pressure based on the relationship between the DSSCs and the SSSCs. The proposed method is applied to an industrial hydraulic pipe system, and the experimental results show that the relative error is reduced greatly after the compensation is implemented, demonstrating the validity of the proposed compensation method.

A Model-Driven Scheme to Compensate the Strain-Based Non-Intrusive Dynamic Pressure Measurement for Hydraulic Pipe

A methodology is presented for Bayesian structural model updating using noisy incomplete modal data
corresponding to natural frequencies and partial mode shapes of some of the modes of a structural system.
The procedure can be used to find the most probable model within a specified class of structural models,
based on the incomplete modal data, as well as the most probable values of the system natural frequencies
and the full system mode shapes. The method does not require matching measured modes with
corresponding modes from the structural model, which is in contrast to many existing methods. To find the
most probable values of the structural model parameters and system modal parameters, the method uses an
iterative scheme involving a series of coupled linear optimization problems. Furthermore, it does not
require solving the eigenvalue problem of any structural model; instead, the eigenvalue equations appear in
the prior probability distribution to provide soft constraints. The method appears to be computationally
efficient and robust, judging from its successful application to noisy simulated data for a ten-storey
building model and for a three-dimensional braced-frame model. This latter example is also used to
demonstrate an application to structural health monitoring.

Ka-Veng Yuen

Papers

Model Selection using Response Measurements: Bayesian Probabilistic Approach

Bayesian Methods for Structural Dynamics and Civil Engineering

Overview of Environment Perception for Intelligent Vehicles

A Model-Driven Scheme to Compensate the Strain-Based Non-Intrusive Dynamic Pressure Measurement for Hydraulic Pipe

Efficient model updating and health monitoring methodology using incomplete modal data without mode matching