Incremental dynamic analysis with consideration of modeling uncertainties

This research investigates the collapse responses of a concrete moment frame considering modeling uncertainties. These modeling uncertainties are considered for evaluating a collapse response related to the modified Ibarra-Krawinkler moment-rotation parameters for beam and column elements of a given structure. To analyze these uncertainties, the correlations between the model parameters in one component and between two structural components were considered. Latin Hypercube Sampling (LHS) method was employed to produce independent random variables. Moreover, Cholesky decomposition was adopted to produce correlated random variables. Performing 281 simulations for the uncertainties involved considering their inter-correlations, incremental dynamic analysis (IDA) was done using 44 far-field accelerograms to determine structural collapse responses. Collapse responses of each simulation, including mean collapse capacity, mean collapse drift, and mean annual frequency, were obtained. Then, the collapse responses were predicted using the response surface method and artificial neural network. The results show that the Correlation coefficients (R) between the target data resulted from incremental dynamic analysis (IDA), output data resulted from response surface method (RSM), and artificial neural network (ANN) were obtained for the collapse responses above 0.98. The maximum prediction errors for mean collapse capacity and mean collapse drift are less than 5% and for mean annual frequency less than 10% under response surface method (RSM), and artificial neural network (ANN).

Robust performance‐based earthquake assessment should consider the effects of both aleatory and epistemic uncertainty on seismic response parameters. While the effects of modelling uncertainty on specific engineering demand parameters and collapse fragility curves have been widely investigated to date on bare frame structural typologies, little attention has been paid to the impact of masonry infill properties variability on the uncertainty estimation in existing infilled RC frames. Previous studies available in the literature are mainly related to seismically designed RC buildings and the suggestions provided in international guidelines cannot be reasonably adopted for poorly detailed RC existing frames. As regards the uncertainty in existing RC frames, the role of masonry infills was often neglected or their interaction with the surrounding frames was not rigorously accounted for in the uncertainty estimation. With such limitations in mind, the aim of this study is to provide more realistic modelling uncertainty quantities to be adopted in seismic assessment of existing infilled RC frames, including, in a thorough manner, the impact of the variability of the masonry infill typologies and the occurrence of RC frame shear failure. Both the aleatory and epistemic uncertainties are estimated, in terms of the overall dispersion on specific engineering demand parameters and collapse fragility curves. Finally, the impact of such uncertainties on seismic risk estimates, quantified through expected annual losses, is also thoroughly investigated. The inclusion of additional random variables, not considered so far, in the uncertainty estimation framework, led to importantly higher modelling uncertainty quantities than those currently available from previous studies.

The Monte Carlo method exhibits generality and insensitivity to the number of stochastic variables, but is expensive for accurate Average Quality Measure (AQI) or Parametric Yield estimation of MOS VLSI circuits. In this contribution a new method of variance reduction technique, viz. the Latin Hypercube Sampling (LHS) method is presented which improves the efficiency of AQI estimation in integrated circuits especially for MOS digital circuits. This method is similar to the Primitive Monte Carlo (PMC) method except in samples generation step where the Latin Hypercube Sampling method is used. This sampling method is very simple and does not involve any further simulations. Moreover, it has a smaller variance with respect to the PMC estimator. Encouraging results have thus far been obtained. A 3-dimensional quadratic function, a high pass filter, and a CMOS delay circuit examples are included to demonstrate the efficiency of this technique.

Take a typical cross-section of rockfill embankment slope in Yaan-Luku highway as the research object, reliability analysis is studied under the condition of water table of 840.85m, 851.50m, and loading condition of natural state and horizontal seismic acceleration of 0.2g, respectively. Raw data use Kolmogorov-Smirnov test (K-S test) to determine the distribution type of parametric variation. And the parameters were sampling with Latin hypercube sampling (LHS) method and Monte Carlo (MC) method, respectively, to obtain state function and determine safety factors and reliability indexes. A conclusion is drawn that the times of simulation based on LHS method were less than Monte Carlo method. Also the convergence of failure probability is better than the Monte Carlo method. The safety factor is greater than one and the failure probability has reached to 35.45% in condition of earthquake, which indicating that the instability of rockfill embankment slope is still possible.

It is known that the extradosed cable-stayed bridge, a hybrid bridge, possesses the virtues of both classic cable-stayed bridges and girder bridges in mechanical behaviors. In this paper, the sensitivity of seismic fragility demand parameters (SFDP) of a high-speed railway extradosed cable-stayed bridge is studied systematically along with the consideration of structural parameter uncertainty. Based on the probability distribution and correlation of random parameters, the Latin hypercube sampling method is adopted herein. The dynamic 3D finite element model of the employed bridge is established by using powerful and attractive OpenSEES nonlinear software. A nonlinear incremental dynamic analysis is performed to consider the randomness of structural parameters using sampling analysis. Some important conclusions are drawn indicating that the structural design parameter uncertainty predominantly has influence on the SFDP for fragility analysis of bridge structures. The design parameters of extradosed cable-stayed bridges are categorized and identified as primary, secondary and insensitive parameters. The high sensitivity parameters of extradosed cable-stayed bridges for fragility analysis include friction coefficient of bearing, concrete bulk density, damping ratio, peak compressive strength of confined concrete, component size and peak strain of confined concrete. Additionally, the strength and strain of unconfined concrete cannot be ignored. Furthermore, the uncertainty of structural design parameters fails to be responsible for the cable force responses due to larger girder stiffness. The structural design parameter uncertainty has a significant influence on the responses of extradosed cable-stayed bridges for seismic fragility analysis.

This paper studies the effects of spatial correlation between the structural components on probabilistic seismic performance of steel moment resisting frames, using extended incremental dynamic analysis (IDA) method to consider the effects of aleatoric uncertainties caused by ground motion records. In this probabilistic assessment, a combination of Latin hypercube sampling (LHS) and Cholesky decomposition (CD) methods are utilized to take the epistemic uncertainties of the numerical model into account. In case the target correlation matrix is not positive definite, two powerful optimization algorithms are employed to find the closest positive definite correlation matrix.

Functional failure analysis of a thermal–hydraulic passive system by means of Line Sampling

Based on Latin Hypercube Sampling method, the crossover operation in GA is redesigned; combined with immune mechanism, chromosome concentration is defined and clonal selection strategy is designed, thus an Immune Genetic Algorithm is given based on Latin Hypercube Sampling for solving the Maximum Clique Problem in this paper. The examples shows the new algorithm in solution quality, convergence speed, and other indicators is better than the classical genetic algorithm and good point set genetic algorithm. On the other hand, the new algorithm is not inferior to such classical algorithms as dynamic local search, and it got better solutions to some examples.KeywordsMaximum Clique Problem(MCP)Genetic Algorithm (GA)Latin Hypercube Sampling(LHS)Artificial Immune Systems(AIS)Immune Genetic Algorithm based on Latin Hypercube Sampling(LIGA)

On Latin Hypercube sampling for efficient uncertainty estimation of satellite rainfall observations in flood prediction

Applying analysis techniques developed for naturally occurring earthquakes to mine seismicity is common practice; however, these methodologies rarely consider the influence of blasting on the dynamic rock mass failure processes observed in mines. Due to the complex nature of bulk orebody extraction at depth, quantifying discrete seismic responses to mining can be challenging. This paper identifies seismic responses to mining by pairing single-link clustering with finite temporal windows bound by mine blasting practices. A methodology is presented to quantify the space−time characteristics of these responses using four seismic response parameters (SRPs): distance to blast, distance to centroid, time after blast, and time between events. Using SRPs, seismic responses to mining can be quantitatively classified as induced, complex, or triggered (with respect to discrete mine blasting). Because these response parameters do not require an extensive and (or) triaxial dense sensor array, they are applicable to a variety of underground mining operations. In this work, SRPs are applied to 189 discrete seismic responses occurring over 2 months of active mining, and a 2 week shutdown period, at Agnico Eagle’s LaRonde Mine.

Modeling and characterization of complex composite structures is challenging due to uncertainties inherent in these materials. Uncertainty is present at each length scale in composites and must be quantified in order to accurately model the mechanical response and damage progression of this material. The ability to pass information between length scales permits multiscale models to transport uncertainties from one scale to the next. Limitations in the physics and errors in numerical methods pose additional challenges for composite models. By replacing deterministic inputs with random inputs, stochastic methods can be implemented within these multiscale models making them more robust. This work focuses on understanding the sensitivity of multiscale models and damage progression variations to stochastic input parameters as well as quantifying these uncertainties within a modeling framework. A multiscale, sectional model is used due to its efficiency and capacity to incorporate stochastic methods with little difficulty. The sectional micromechanics in this model are similar to that of the Generalized Method of Cells with the difference being the discretization techniques of the unit cell and the continuity conditions. A Latin Hypercube sampling technique is used due to its reported computational savings over other methods such as a fully random Monte Carlo simulation. Specifically in the sectional model, the Latin Hypercube sampling method provides an approximate 35 % reduction in computations compared to the fully random Monte Carlo method. The Latin Hypercube sampling is a stratified technique which discretizes the distribution function and randomizes the input parameters within those discretized fields. Within this multiscale modeling framework, a progressive failure theory is implemented using these stochastic methods and a modified Hashin failure theory. With a combined stochastic method and progressive failure theory, this multiscale model is capable of modeling the uncertainty and material property variations for composite materials.

This research work presents a method of smokestack emission predicting model based on Latin hypercube sampling (LHS) and support vector machines (SVM). To solve the problem of predicting the concentration of smokestack emission, the Latin hypercube sampling method is adopted in a circular area above the smokestack to choose sample points, while the support vector machines regression analysis is carried out on the small data set and is evaluated by the mean square error. At the same time, an efficient parameter optimization method is proposed to reduce the mean square error and improve the prediction precision. With LIBSVM toolbox of MATLAB achieving the SVM regression analysis, SVM regression analysis and parameter optimization method were verified by a mathematical way. It turns out that the optimal parameters C and σ is respectively 2.8482 and 16, and the mean square error is 0.023011 which is acceptable. This study provides a reference sampling method and prediction model with parameters optimization for the detection of smokestack emission concentration.

We expand on existing probabilistic tsunami hazard analysis (PTHA) approaches by presenting a methodology wherein the epistemic uncertainties associated with tsunami timing, generation, and propagation are separated from aleatory uncertainties. The contributions of epistemic and aleatory uncertainties to the overall uncertainty in tsunami hazard are evaluated through a two-level Monte Carlo analysis. The total epistemic uncertainty contribution defines the variance of the tsunami inundation hazard distribution. Through Bayesian inference, the available catalog of tsunami runup data is used to refine the hazard estimate, where the prior distribution is weighted by epistemic uncertainties. The general PTHA framework is further modified to include a hazard function accounting for peak water elevation, velocity, duration of inundation, and warning time. We carry out a formal sensitivity analysis of the tsunami generation and propagation on the feasible model parameter space using the multi-objective generalized sensitivity analysis algorithm. Parameters showing a significant influence on tsunami hazard include the tidal elevation, moment magnitude (M w), the Gutenberg–Richter (G–R) distribution β parameter, epicenter location, and the rake angle. Parameters showing minimal influence on tsunami hazard include the G–R maximum seismic moment magnitude (M max) parameter, strike angle, dip angle, seismic depth and Manning’s roughness. As a proof of concept, the methodology is applied to a case study of tsunami hazard posed to National Oceanic and Atmospheric Administration Station 9411406 (Oil Platform Harvest, CA, USA) by seismic sources along the Aleutian Islands, AK.

The RAVEN code has been under development at the Idaho National Laboratory since 2012. Its main goal is to create a multi-purpose platform for the deploying of all the capabilities needed for Probabilistic Risk Assessment, uncertainty quantification, data mining analysis and optimization studies. RAVEN is currently equipped with three different sampling categories: Forward samplers (Monte Carlo, Latin Hyper Cube, Stratified, Grid Sampler, Factorials, etc.), Adaptive Samplers (Limit Surface search, Adaptive Polynomial Chaos, etc.) and Dynamic Event Tree (DET) samplers (Deterministic and Adaptive Dynamic Event Trees). The main subject of this document is to report the activities that have been done in order to: start the migration of the RAVEN/RELAP-7 control logic system into MOOSE, and develop advanced dynamic sampling capabilities based on the Dynamic Event Tree approach. In order to provide to all MOOSE-based applications a control logic capability, in this Fiscal Year an initial migration activity has been initiated, moving the control logic system, designed for RELAP-7 by the RAVEN team, into the MOOSE framework. In this document, a brief explanation of what has been done is going to be reported. The second and most important subject of this report is about the development of a Dynamic Event Tree (DET) sampler named “Hybrid Dynamic Event Tree” (HDET) and its Adaptive variant “Adaptive Hybrid Dynamic Event Tree” (AHDET). As other authors have already reported, among the different types of uncertainties, it is possible to discern two principle types: aleatory and epistemic uncertainties. The classical Dynamic Event Tree is in charge of treating the first class (aleatory) uncertainties; the dependence of the probabilistic risk assessment and analysis on the epistemic uncertainties are treated by an initial Monte Carlo sampling (MCDET). From each Monte Carlo sample, a DET analysis is run (in total, N trees). The Monte Carlo employs a pre-sampling of the input space characterized by epistemic uncertainties. The consequent Dynamic Event Tree performs the exploration of the aleatory space. In the RAVEN code, a more general approach has been developed, not limiting the exploration of the epistemic space through a Monte Carlo method but using all the forward sampling strategies RAVEN currently employs. The user can combine a Latin Hyper Cube, Grid, Stratified and Monte Carlo sampling in order to explore the epistemic space, without any limitation. From this pre-sampling, the Dynamic Event Tree sampler starts its aleatory space exploration. As reported by the authors, the Dynamic Event Tree is a good fit to develop a goal-oriented sampling strategy. The DET is used to drive a Limit Surface search. The methodology that has been developed by the authors last year, performs a Limit Surface search in the aleatory space only. This report documents how this approach has been extended in order to consider the epistemic space interacting with the Hybrid Dynamic Event Tree methodology.

Seismic risk assessment of reticulated shell structures considering multiple uncertainties