Assessing structural vulnerability against earthquakes using multi-dimensional fragility surfaces: A Bayesian framework

The present paper advocates a probabilistic framework for assessing structural vulnerability against earthquakes. This is justified by the significant randomness that characterizes not only the earthquake excitation (amplitude, frequency content, duration), but also the structural system itself (i.e. stochastic variations in the material properties). Performance predictions can readily be summarized in the form of fragility curves which express the probability of exceeding various damage levels (from minor to collapse) with respect to a metric of the earthquake intensity. In this paper, a Bayesian framework is proposed for the derivation of fragility curves which can produce estimates irrespective of the amount of data available. It is particularly flexible when combined with Markov Chain Monte Carlo (MCMC) techniques and can efficiently provide credible intervals for the estimates. Furthermore, a general procedure based on logistic regression is illustrated that can lead in a principled manner to the derivation of fragility surfaces which express the probability of exceeding a damage level with respect to several measures of the earthquake load and can thus produce more accurate predictions. The methodologies presented are illustrated using data generated from computational simulations for a structure on top of a saturated sand deposit which is susceptible to liquefaction.     

Frontiers in Statistical Quality Control 3

Computer model calibration is the process of determining input parameter settings to a computational model that are consistent with physical observations. This is often quite challenging due to the computational demands of running the model. In this article, we use the ensemble Kalman filter (EnKF) for computer model calibration. The EnKF has proven effective in quantifying uncertainty in data assimilation problems such as weather forecasting and ocean modeling. We find that the EnKF can be directly adapted to Bayesian computer model calibration. It is motivated by the mean and covariance relationship between the model inputs and outputs, producing an approximate posterior ensemble of the calibration parameters. While this approach may not fully capture effects due to nonlinearities in the computer model response, its computational efficiency makes it a viable choice for exploratory analyses, design problems, or problems with large numbers of model runs, inputs, and outputs.     

Bayesian analysis of computer code outputs: A tutorial

The Bayesian approach to quantifying, analysing and reducing uncertainty in the application of complex process models is attracting increasing attention amongst users of such models. The range and power of the Bayesian methods is growing and there is already a sizeable literature on these methods. However, most of it is in specialist statistical journals. The purpose of this tutorial is to introduce the more general reader to the Bayesian approach.     

A computational multi-scale approach for the stochastic mechanical response of foam-filled honeycomb cores

This paper investigates the uncertainty in the mechanical response of foam-filled honeycomb cores by means of a computational multi-scale approach. A finite element procedure is adopted within a purely kinematical multi-scale constitutive modelling framework to determine the response of a periodic arrangement of aluminium honeycomb core filled with PVC foam. By considering uncertainty in the geometric properties of the microstructure, a significant computational cost is added to the solution of a large set of microscopic equilibrium problems. In order to tackle this high cost, we combine two strategies. Firstly, we make use of symmetry conditions present in a representative volume element of material. Secondly, we build a statistical approximation to the output of the computer model, known as a Gaussian process emulator. Following this double approach, we are able to reduce the cost of performing uncertainty analysis of the mechanical response. In particular, we are able to estimate the 5th, 50th, and 95th percentile of the mechanical response without resorting to more computationally expensive methods such as Monte Carlo simulation. We validate our results by applying a statistical adequacy test to the emulator.     

A Perspective on Smart Process Manufacturing Research Challenges for Process Systems Engineers

The challenges posed by smart manufacturing for the process industries and for process systems engineering (PSE) researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, but benchmarking would give greater confidence. Technical challenges confronting process systems engineers in developing enabling tools and techniques are discussed regarding flexibility and uncertainty, responsiveness and agility, robustness and security, the prediction of mixture properties and function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to drive agility will require tackling new challenges, such as how to ensure the consistency and confidentiality of data through long and complex supply chains. Modeling challenges also exist, and involve ensuring that all key aspects are properly modeled, particularly where health, safety, and environmental concerns require accurate predictions of small but critical amounts at specific locations. Environmental concerns will require us to keep a closer track on all molecular species so that they are optimally used to create sustainable solutions. Disruptive business models may result, particularly from new personalized products, but that is difficult to predict.     

Acoustic chemometric prediction of total solids in bioslurry: A full-scale feasibility study for on-line biogas process monitoring

Dry matter is an important process control parameter in the bioconversion application field. Acoustic chemometrics, as a Process Analytical Technology (PAT) modality for quantitative characterisation of dry matter in complex bioslurry systems (biogas fermentation), has not been successful despite several earlier dedicated attempts. A full-scale feasibility study based on standard addition experiments involving natural plant biomass was conducted using multivariate calibration (Partial Least Squares Regression, PLS-R) of acoustic signatures against dry matter content (total solids, TS). Prediction performance of the optimised process implementation was evaluated using independent test set validation, with estimates of accuracy (slope of predicted vs. reference values) and precision (squared correlation coefficient, r²) of 0.94 and 0.97 respectively, with RMSEP of 0.32% w/w (RMSEP_rel = 3.86%) in the range of 5.8-10.8% w/w dry matter. Based on these excellent prediction performance measures, it is concluded that acoustic chemometrics has come of age as a full grown PAT approach for on-line monitoring of dry matter (TS) in complex bioslurry, with a promising application potential in other biomass processing industries as well.