GPCE-based stochastic inverse methods: A benchmark study from a civil engineer’s perspective

In civil and mechanical engineering, Bayesian inverse methods may serve to calibrate the uncertain input parameters of a structural model given the measurements of the outputs. Through such a Bayesian framework, a probabilistic description of parameters to be calibrated can be obtained; this approach is more informative than a deterministic local minimum point derived from a classical optimization problem. In addition, building a response surface surrogate model could allow one to overcome computational difficulties. Here, the general polynomial chaos expansion (gPCE) theory is adopted with this objective in mind. Owing to the fact that the ability of these methods to identify uncertain inputs depends on several factors linked to the model under investigation, as well as the experiment carried out, the understanding of results is not univocal, often leading to doubtful conclusions. In this paper, the performances and the limitations of three gPCE-based stochastic inverse methods are compared: the Markov Chain Monte Carlo (MCMC), the polynomial chaos expansion-based Kalman Filter (PCE-KF) and a method based on the minimum mean square error (MMSE). Each method is tested on a benchmark comprised of seven models: four analytical abstract models, a one-dimensional static model, a one-dimensional dynamic model and a finite element (FE) model. The benchmark allows the exploration of relevant aspects of problems usually encountered in civil, bridge and infrastructure engineering, highlighting how the degree of non-linearity of the model, the magnitude of the prior uncertainties, the number of random variables characterizing the model, the information content of measurements and the measurement error affect the performance of Bayesian updating. The intention of this paper is to highlight the capabilities and limitations of each method, as well as to promote their critical application to complex case studies in the wider field of smarter and more informed infrastructure systems

Solid-State Nuclear Magnetic Resonance of Triple-Cation Mixed-Halide Perovskites

Mixed-cation lead mixed-halide perovskites are the best candidates for perovskite-based photovoltaics, thanks to their higher efficiency and stability compared to the single-cation single-halide parent compounds. TripleMix (Cs0.05MA0.14FA0.81PbI2.55Br0.45 with FA = formamidinium and MA = methylammonium) is one of the most efficient and stable mixed perovskites for single-junction solar cells. The microscopic reasons why triplecation perovskites perform so well are still under debate. In this work, we investigated the structure and dynamics of TripleMix by exploiting multinuclear solid-state nuclear magnetic resonance (SSNMR), which can provide this information at a level of detail not accessible by other techniques. 133Cs, 13C, 1 H, and 207Pb SSNMR spectra confirmed the inclusion of all ions in the perovskite, without phase segregation. Complementary measurements showed a peculiar longitudinal relaxation behavior for the 1 H and 207Pb nuclei in TripleMix with respect to single-cation single-halide perovskites, suggesting slower dynamics of both organic cations and halide anions, possibly related to the high photovoltaic performances

Applications of solid-state NMR spectroscopy to the study of perovskites

In this dissertation, the opportunities offered by solid-state NMR (SSNMR) spectroscopy to study perovskite materials for photovoltaic application are discussed and analysed. The attention will be focused on the possibility that information obtained thanks to SSNMR could provide crucial knowledge on the link between structural and dynamic properties, on a nano- and microscopic level, and photovoltaic efficiency. Recent scientific literature about NMR studies of perovskites systems will be analysed and discussed to enlighten the materials features that can be probed via SSNMR (compositional variations and incorporation of ions into the lattice, local dynamics and diffusion, degradation mechanisms and stability) and to suggest possible directions for future studies

A comparison of stochastic inverse methods with sampling and functionalbased linear and non-linear update procedures

In this paper we focus on inverse methods enabling the calibration of input parameters when measurement of the re-sponse of an engineering system is available. Considering only stochastic approaches, different methods can be used to perform the update. In the paper, a comparison of some of these numerical procedures is presented in order to evaluate the capability of the different methods. In particular, simple analysis have been carried out focusing the attention on those aspects that are more crucial in engineering application, such as the linearity/non-linearity of the model and the influence of the prior quality. The results obtained with some toy-examples show that these aspects highly influence the performance of the methods. The Markov Chain Monte Carlo (MCMC) method is computationally expensive, due to slow convergence rate, but it is competitive for capturing multi-modal Bayesian posterior distribution. Efficient methods, such as the Kalman Filter, are suitable for linear models but have limitations when updating the parameters of non-linear models. Non-linear filters, such as the Non Linear Minimum Mean Squared Error (NL-MMSE), lead to better results for highly nonlin-ear models

Snow Load on Structures under Changing Climate Conditions

The effect of climate change on climatic actions could significantly affect, in the mid-term future, the design of new structures actions as well as the reliability of existing ones designed according the provisions of current codes, which are derived from past observations under the assumption of stationary climate conditions. An original technique for snow loads definition is proposed, based on a non-stationary model for extreme values, able to take into account information provided by the outcomes of climate models. Temporal trends are assessed directly on location and scale parameters of Extreme Values Type I distribution, considering moving time windows of thirty years shifted ten years by ten years. The analysis are performed for the Italian Mediterranean region, suitably elaborating observed data series of daily temperatures and precipitation and climate projections of the same variables provided by Regional Climate Models for different greenhouse gas emission scenarios. The results are then compared in terms of characteristic values of ground snow load (0.98 quantile of annual extremes), that serves as basis for structural design, assessing its variability with time. Finally, an advanced Bayesian method for the definition of trend parameters is presented. This method combines climate change information present in climate models with observed temporal trends and will lead to a more trustable definition of future trend of ground snow loads

Seismic Reliability Assessment of a Concrete Water Tank Based on the Bayesian Updating of the Finite Element Model

Failure or malfunction of complex engineered networks involves relevant social and economic aspects, so that their maintenance is of primary importance. In assessing the reliability of such networks, it should be duly considered that they are a whole made of different parts, and that some of these individual parts or structures are often crucial to assure the proper operation of the entire network. Moreover, each of these structures can be considered a complex system by itself: structural reliability theory should be thus combined with advanced numerical analysis tools in order to obtain realistic estimates of the probability of failure. Accurate estimations are especially required in seismic zones, aiming to efficiently plan future interventions. This paper presents a method for the reliability assessment of a critical element of engineered networks. The method is discussed with special reference to a relevant case study: a concrete water tank, which is a key component of a water supply system. Special attention is devoted to the reliability assessment of the tank under seismic loads, based on a structural identification approach. The calibration of the finite element model (FEM) of the structure is carried out on probabilistic basis, applying the Bayes theorem and response surface methods. The proposed approach allows to significantly speed up the structural identification process, leading to sounder estimate of the input parameters. Finally, the seismic fragility curves of the structure are developed according to the relevant limit states, demonstrating that information regarding the global structural behavior and local checks can be effectively combined in structural reliability assessments

Effect of climate change on snow load on ground: Bayesian approach for snow map refinement

Alteration of ground snow loads due to the climate change may significantly impact the reliability of existing structures, as well as design Codes for new ones. In the paper a novel technique for snow load map refinement is proposed where ground snow loads derived starting from gridded climate data provided by climate models are combined with observed point measurements of snow loads and then suitably updated. First, an a priori random field of characteristic ground snow loads at the sea level is deduced from the analysis of gridded climate data. This prior random field is discretized by the truncated Karhunen-Loeve expansion, to separate the spatial and the stochastic domain and to reduce the dimension of the problem. The distribution of the resulting standard normal random variables are then updated incorporating point measurements of ground snow loads collected in the past and using the Markov Chain Monte Carlo method to sample the posterior. The Bayesian approach results in a more trustable, refined snow load map, and furthermore prospects a dynamic, sequential model updating procedure as new observed data becomes available

On Bayesian identification methods for the analysis of existing structures

The paper explores three stochastic inverse methods based on a functional approximation of the system response: the Markov Chain Monte Carlo method, the Polynomial Chaos Expansion based Kalman Filter, and the parameter update with the Minimum Mean Squared Error estimator. The algorithms were implemented to update the probability distribution function of the input parameters of a finite element model with observable response of the structure. The different methods were tested on a simple case study, where some properties of a concrete water tank from the 60s' were updated. Advantages and drawbacks of each procedure have been discussed according to the obtained results. Attention is drawn on the prospective that the given methods may be applied for better assessing the reliability of existing structures

Chemical investigations of bitumen from Neolithic archaeological excavations in Italy by GC/MS combined with principal component analysis

An analytical protocol involving microwave assisted solvent extraction and fractionation on silica gel columns followed by gas chromatography/mass spectrometry (GC/MS) and principal component analysis (PCA) of the chromatographic data was used for the characterization of bituminous residues sampled from Neolithic flint flakes and potsherds recovered from archaeological excavations in Abruzzo and Apulia (Italy). The analytical protocol was optimized and primarily tested in the study of geological bitumen (used as reference materials) from rocks and sediments of central-southern Italy (Abruzzo, Sicily and Lazio), and subsequently used to characterize the archaeological bitumen. Since bitumen is usually present in very low amounts in archaeological objects, we paid attention to improve the extraction efficiency of terpanes and steranes, the main soluble components of bitumen. The highest efficiency was obtained using microwave assisted extraction with a mixture of n-hexane/dichloromethane/methanol (80:15:5, v/v/v). Given that the composition of the bitumen varies depending on the area of origin, the results obtained from the archaeological materials allowed us not only to draw hypotheses on the possible function of tools/objects from which the bitumen is sampled, but also to obtain information on its geographical origin. In particular, PCA, used as a tool for an extensive analysis of chromatographic data, enabled us to correlate the quantitative chemical composition and the geographical origin of the samples, and finally to distinguish bitumen originating from the different Italian sites, based on their molecular profiles

Early Prognostic Stratification of Clostridioides difficile Infection in the Emergency Department: The Role of Age and Comorbidities

Clostridioides difficile infection (CDI) represents a significant cause of morbidity and mortality, mainly in older and frail subjects. Early identification of outcome predictors, starting from emergency department (ED) admission, could help to improve their management. In a retrospective single-center study on patients accessing the ED for diarrhea and hospitalized with a diagnosis of CDI infection, the patients’ clinical history, presenting symptoms, vital signs, and laboratory exams at ED admission were recorded. Quick sequential organ failure assessments (qSOFA) were conducted and Charlson’s comorbidity indices (CCI) were calculated. The primary outcomes were represented by all-cause in-hospital death and the occurrence of major cumulative complications. Univariate and multivariate Cox regression analyses were performed to establish predictive risk factors for poor outcomes. Out of 450 patients, aged > 81 years, dyspnea at ED admission, creatinine > 2.5 mg/dL, white blood cell count > 13.31 × 109/L, and albumin < 30 µmol/L were independently associated with in-hospital death and major complications (except for low albumin). Both in-hospital death and major complications were not associated with multimorbidity. In patients with CDI, the risk of in-hospital death and major complications could be effectively predicted upon ED admission. Patients in their 8th decade have an increased risk independent of comorbidities