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The paper portrays a comprehensive computational procedure for determining the global structural resistances of two existing bridges made of I‐73 and KA‐61 precast post‐tensioned concrete girders using advanced statistical assessment methods in combination with nonlinear fracture mechanics‐based finite element method analysis. Although this combination is a powerful tool for realistic modeling of structures, its practical application is still very time consuming. Therefore, a statistical sampling approach for the determination of the structural design resistance is compared to selected efficient semi‐probabilistic methods based on the estimation of coefficient of variation—estimation of coefficient of variation (ECoV) method according to fib Model Code 2010 and improved approach called Eigen ECoV method. Load‐bearing capacity is determined for the ultimate as well as several serviceability limit states. The sensitivity of the input parameters burdened with uncertainties on the response of the structure is quantified using a sensitivity analysis supported by a surrogate model based on polynomial chaos expansion. The paper shows that the applicability of nonlinear modeling with respect to uncertainties is possible when using these ECoV methods and a surrogate model and can be applied in a routine manner. The shortcomings and advantages of all the used safety design/assessment methods are discussed. [ABSTRACT FROM AUTHOR]

To compare changes in oesophageal (T-Oeso) and rectal (T-Rec) temperature in dogs during general anaesthesia and premedicated with fentanyl, medetomidine–fentanyl or acepromazine–fentanyl. Prospective, randomized, blind clinical study. A total of 120 healthy dogs, aged 2–10 years and weighing 5–20 kg. Dogs were randomly allocated to one of three groups. Animals of F group were premedicated with fentanyl (0.01 mg kg–1), MF group with medetomidine (0.005 mg kg–1) and fentanyl (0.01 mg kg–1) and AF group with acepromazine (0.01 mg kg–1) and fentanyl (0.01 mg kg–1). Anaesthesia was induced with propofol and maintained with isoflurane in oxygen–air mixture. Fentanyl was administered continuously (0.01 mg kg–1 hour–1). The T-Oeso, T-Rec and ambient temperatures were recorded after induction (T0) and subsequently at 10 minute intervals for 60 minutes (T10–T60). Data were analysed using anova or their non-parametric equivalents (p < 0.05). Median T-Oeso was significantly higher in MF group between T0–T20 compared with other groups. Median T-Oeso significantly decreased in F group from 38.0 °C (T0) to 37.4 °C (T30), 37.1 °C (T40), 36.9 °C (T50) and 36.6 °C (T60), in MF group from 38.3 °C (T0) to 37.7 °C (T30), 37.5 °C (T40), 37.2 °C (T50) and 37.1 °C (T60) and in AF group from 37.7 °C (T0) to 37.3 °C (T40), 37.2 °C (T50) and 37.1 °C (T60). The T-Rec significantly decreased in F group from 38.0 °C (T0) to 37.4 °C (T40), 37.2 °C (T50) and 36.9 °C (T60), in MF group from 38.3 °C (T0) to 37.5 °C (T50) and 37.4 °C (T60) and in AF group from 38.2 °C (T0) to 37.6 °C (T40), 37.5 °C (T50) and 37.4 °C (T60). Premedication with fentanyl, medetomidine–fentanyl or acepromazine–fentanyl in the doses used decreased the T-Oeso and T-Rec. The T-Oeso at the beginning of anaesthesia was higher after premedication with medetomidine–fentanyl. However, this difference was not clinically significant. [ABSTRACT FROM AUTHOR]

The article assesses the psychometric properties of the Czech version of the Overall Depression Severity and Impairment Scale (ODSIS), a transdiagnostic measure for depression severity and functional impairment. Topics include evaluating dimensionality via Confirmatory Factor Analysis (CFA), exploring Item Response Theory (IRT) for item analysis, and determining reliability and validity through various measures across different sample groups.

The paper is focused on recent advances in uncertainty quantification using polynomial chaos expansion (PCE). PCE is a well-known technique for approximation of costly mathematical models with random inputs - surrogate model. Although PCE is a widely used technique and it has several advantages over various surrogate models, it has still several limitations and research gaps. This paper reviews some of the recent theoretical developments in PCE. Specifically a new active learning method optimizing the experimental design and an extension of analytical statistical analysis using PCE will be reviewed. These two topics represent crucial tools for efficient applications: active learning leads generally to a significantly more efficient construction of PCE and improved statistical analysis allows for analytical estimation of higher statistical moments directly from PCE coefficients. Higher statistical moments can be further used for the identification of probability distribution and estimation of design quantiles, which is a crucial task for the probabilistic analysis of structures. Selected applications of the theoretical methods are briefly presented in a context of civil engineering as well as some preliminary results of further research. A part of the paper also presents UQPy package containing state-of-the-art implementation of the PCE theory. [ABSTRACT FROM AUTHOR]

This paper is focused on uncertainty quantification (UQ) of an existing bridge structure represented by non-linear finite element model (NLFEM). The 3D model was created according to the original drawings and recent inspections of the bridge. In order to reflect the realistic mechanical behavior, the mathematical model is based on non-linear fracture mechanics and the calculation consists of the three construction stages. The single calculation of the NLFEM is very costly and thus even the elementary task of stochastic analysis - the propagation of uncertainties through a mathematical model - is not feasible by Monte Carlotype approach. Thus, UQ is performed via efficient surrogate modeling technique - Polynomial Chaos Expansion (PCE). PCE is a well-known technique for approximation of the costly mathematical models with random inputs, reflecting their distributions and offering fast and accurate post-processing including statistical and sensitivity analysis. Once the PCE was constructed, it was possible to analyze all quantities of interest (QoIs) and analytically estimate Sobol indices as well as the first four statistical moments. Sobol indices directly measure the influence of the input variability to a variability of QoIs. Statistical moments were used for reconstruction of the probability distributions of QoIs, which will be further used for semi-probabilistic assessment. Moreover, once the PCE is available it could be possible to use it for further standard probabilistic or reliability analysis as a computationally efficient approximation of the original mathematical model. [ABSTRACT FROM AUTHOR]

The paper describes several parts of complex stochastic modelling of precast prestressed concrete girders failing in shear. Experimental studies have been performed on scaled elements as well as on full-scale girders. These tests serve as basis for developing the deterministic nonlinear model and subsequent probabilistic assessment of structural resistance. The combination of nonlinear finite element method and probabilistic analysis is a strong tool for the realistic modelling of structures, but it is extremely time consuming, especially when 3D nonlinear model with many input random parameters is analysed. Therefore, sensitivity analyses have been performed and a surrogate model has been developed. Safety formats are utilized and compared with fully probabilistic approach to determine design value of ultimate capacity of girders.

This paper presents the comparison of advanced semi‐probabilistic methods for the design and assessment of concrete structures represented by mathematical models solved by non‐linear finite element methods. The special attention is given to the advanced methods focused on the estimation of the coefficient of variation of structural resistance. Numerical examples represent a replication of laboratory experiments of beams with different failure modes. The obtained results are discussed with respect to the accuracy of the employed methods and the influence of the assumed statistical correlation among basic variables. Simplified methods give a good estimation of the design values, though their accuracy is dependent on the type of the failure mechanism. Moreover, it is shown that mutual correlations among random variables may significantly affect the design value of resistance, and they should be carefully defined and modeled. [ABSTRACT FROM AUTHOR]