Distribution with Independent Components for Uncertainty Quantification and Structural Reliability Analysis

This paper presents a novel method based on the Information Theory, Machine Learning and Independent Component analysis for Uncertainty Quantification and Structural Reliability Analysis. At first, it is shown that the optimal probabilistic model may be determined through minimum relative entropy and the theory of statistical learningit is also discussed that methods based on the maximum entropy may perform well for the evaluation of the marginal distributions, including the tails. To determine the joint distribution of the basic random variables it is introduced the multivariate probabilistic model of Distributions with Independent Components (DIC). It has same computational simplicity of Nataf, but it is more accurate, since it does not pursue any assumption about the tail dependency. The proposed framework is applied to determine the joint distribution of wave height and period of wave data. Its extension for high dimensional reliability analysis of complex structural systems is straightforward.This research was funded by the Republic of Singapores National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) program. BEARS has been established by the University of California, Berkeley, as a center for intellectual excellence in research and education in Singapore. K.M. Mosalam is a core principal investigator of Tsinghua-Berkeley Shenzhen Institute (TBSI). The authors acknowledge the funding support from Sin-BerBEST and the partial support from TBSI

Holistic Design Platform for Sustainable and Resilient Building Design

In this paper we introduce the Societal Holistic Design Platform (HDP) under uncertainty for sustainable and resilient building design. The integration of classical Risk Analysis, Stochastic Dynamics, Structural Health Monitoring, multicriteria Decision Making, Artificial Intelligence and IoT, gives rise to an innovative Cyber-Physical System under uncertainty centered around humans. The potential of the platform is presented through developed applications. Although the HDP is here applied to a building, it can be easily extended to any system of civil engineering. The proposed platform aims to lead the paradigm shift from the existing notion of Smart City to Resilient Engaged Community, targetting the sustainable development of the urban communitiesThis research was funded by the Republic of Singapores National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) program. BEARS has been established by the University of California, Berkeley, as a center for intellectual excellence in research and education in Singapore. K.M. Mosalam is a core principal investigator of Tsinghua-Berkeley Shenzhen Institute (TBSI). The authors acknowledge the funding support from Sin-BerBEST and the partial support from TBSI

Response Spectrum Code-Conforming PEER PBEE using Stochastic Dynamic Analysis and Information Theory

In this paper, the tools of the stochastic dynamic analysis are adopted for Performance-Based Earthquake Engineering (PBEE). The seismic excitation is defined through a evolutionary Power Spectral Density compatible with the response spectrum given by mandatory codes. In this way, the performance-based design is applied considering the excitation coherent with the codes. Inside the framework, the seismic fragility curves are determined through the Kernel Density Maximum Entropy Method (KDMEM), recently proposed by the authors. It is a novel statistical method capable to reconstruct the seismic fragility curves, including the tails, from a small number of code-conforming artificial ground motions. Moreover, KDMEM is based on the Maximum Entropy (ME) principle and it provides the least biased probability distribution given the available information. Comparison between stationary and nonstationary artificial accelerograms is analyzed, and the corresponding model uncertainty discussed. KDMEM provides also credible bounds of the uncertain performances, which is beneficial for risk-informed decisions. The proposed formulation does not require the selection of a suitable set of ground motions. Accordingly, it can be adopted for optimal design in current engineering practice. Therefore, it fills the gap between the classical code-conforming designs and the enhanced performance-based designs

Risk-Informed Digital Twin (RDT) for the Decarbonization of the Built Environment: The Australian Residential Context

Urban communities are complex systems. According to the holistic perspective of the systems thinking theory, the “whole is not the sum of its parts, but rather is a product of the parts’ interactions”. This systems-thinking approach is commonly applied to analyse urban systems and developments. This study introduces the Risk-informed Digital Building Twin (RDBT) based on the Risk-informed Digital Twin (RDT), a novel digitalization technology incorporating an integrated multi-dimensional multi-stakeholders decision-making system under uncertainty. In the RDBT, energy-efficient, resilient, and sustainable systems/subsystems of civil engineering can be considered at the scale of the single building to assess different needs. Monitored data are critical to performing comprehensive near real-time lifecycle holistic analyses through the framework of Sustainable and Resilient Based Engineering. An apartment building located in Sydney, Australia, has been selected for future deployment of the RDBT

Stochastic dynamic analysis of a marine riser using the First-Order Reliability Method

The dynamic analysis of a deepwater floating production systems has many complexities, such as the dynamic coupling between the vessel and the riser, the coupling between the first-order and second-order wave forces, several sources of nonlinearities. These complexities can be captured by fully coupled time domain analyses. Moreover, the sea state is random, hence the need of stochastic dynamic analysis. In this paper the non-Gaussian responses of the system are obtained through the well-known First-Order Reliability Method (FORM) of the structural reliability analysis. The application to a simplified 2 degrees-of-freedom model shows the accuracy and effectiveness of the presented procedure.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.FacultyResearche

First-Order Reliability Method for Structural Reliability Analysis in the Presence of Random and Interval Variables

This paper presents a novel procedure based on first-order reliability method (FORM) for structural reliability analysis in the presence of random parameters and interval uncertain parameters. In the proposed formulation, the hybrid problem is reduced to standard reliability problems, where the limit state functions are defined only in terms of the random variables. Monte Carlo simulation (MCS) for hybrid reliability analysis (HRA) is presented, and it is shown that it requires a tremendous computational effort; FORM for HRA is more efficient but still demanding. The computational cost is significantly reduced through a simplified procedure, which gives good approximations of the design points, by requiring only three classical FORMs and one interval analysis (IA), developed herein through an optimization procedure. FORM for HRA and its simplified formulation achieve a much improved efficiency than MCS by several orders of magnitude, and it can thus be applied to real-world engineering problems. Representative examples of stochastic dynamic analysis and performance-based engineering are presented

Informational probabilistic sensitivity analysis and active learning surrogate modelling

International audienceIn this paper, information theory is applied for probabilistic sensitivity analysis and surrogate modelling with active learning. One of the authors has recently proposed the adoption of the informational coefficient of correlation as a measure of dependence between random variables, in place of the largely adopted linear coefficient of correlation. Here, it is shown that the informational coefficient of correlation can be used for probabilistic sensitivity analysis based on the Value of Information (VoI). Effective Informational sensitivity indices based on the mutual information are presented. Moreover, two novel learning functions for adaptive sampling are proposed. The first, called -function, gives rise to the method AK-H (Adaptive Kriging-Entropy), which describes the epistemic uncertainty through the entropy metric. The second, called -function, gives rise to the method AL-MI (Active Learning-Mutual Information), which describes the model error through the Mutual Information. The peculiarity of AL-MI is that it allows the implementation of active learning in any kind of surrogate modelling, even different from Kriging. The two learning functions are applied for two different categories of problems: (i) regression and (ii) evaluation of failure probability within the framework of structural reliability analysis. Numerical examples show its features and its potential for application of the proposed approach

Implications of high-dimensional geometry for structural reliability analysis and a novel linear response surface method based on SVM

The geometry of high-dimensional spaces is very different from low dimensional spaces and possesses some counter-intuitive features. It is shown that, for high dimensions, the sampling points fall far away from the origin and concentrate within an intersection between a very thin shell and a suitable equatorial slab. The well-known First-Order Reliability Method (FORM), originally formulated for low dimensions, may work well in many engineering problems of high dimension. But it is not able to reveal the level of achieved accuracy. Considering the features of high-dimensional geometry, a novel linear response surface based on Support Vector Method (SVM) is proposed for structural reliability problems of high dimension. The method is shown to outperform FORM for structural reliability problems of high dimension in terms of robustness and accuracy

A new sampling strategy for SVM-based response surface for structural reliability analysis

To evaluate failure probability of structures in the most general case is computationally demanding. The cost can be reduced by using the Response Surface Methodology, which builds a surrogate model of the target limit state function. In this paper authors consider a specific type of response surface, based on the Support Vector Method (SVM). Using the SVM the reliability problem is treated as a classification approach and extensive numerical experimentation has shown that each type of limit state can be adequately represented; however it could require a high number of sampling points. This work demonstrates that, by using a novel sampling strategy based on sampling directions, it is possible to obtain a good approximation of the limit state without high computational complexity. A second-order polynomial SVM model has been adopted, so the need of determining free parameters has been avoided. However, if needed, higher-order polynomial or Gaussian kernel can be adopted to approximate any kind of limit state. Some representative numerical examples show the accuracy and effectiveness of the presented procedure