A probabilistic assessment is performed using different seismic fragility analysis approaches for structures under non-stationary stochastic ground motions. Four commonly used probabilistic seismic ...fragility analysis (PSFA) approaches are adopted, which are least squares regression (LSR), maximum likelihood estimation (MLE), kernel density estimation (KDE) and Monte Carlo simulation (MCS). The principles of the four PSFA approaches are first introduced, and the theories of non-stationary stochastic acceleration time series are introduced in light of the spectral representation of random functions. Then an analytical procedure of different PSFA approaches is constructed for structures under non-stationary stochastic motions. After that a case study is carried out to analyze the results of the four PSFA approaches. The demand distributions of univariate random variable under certain intensity measure levels are discussed, and multiple fragility curves under different limit states for all the approaches are compared. In general, the MCS approach is set as the benchmark to verify the effectiveness of other approaches, but it is computationally consuming. The non-parametric KDE approach is recommended to estimate the univariate distribution under specified IM levels, because it can reflect the real distribution characteristics with combined efficiency and accuracy. For the seismic fragility under different limit states, the LSR approach is suitable for efficient data processing and general trend assessment, at the expense of accuracy. The MLE approach indicates a better applicability under a smaller response threshold and slighter damage conditions, while the KDE approach shows a better applicability under a larger response threshold and severer damage conditions. The paper compares the accuracy and applicability of various PSFA approaches under multiple conditions, and provides a basis to link probabilistic hazard and risk analyses for performance assessment in the future research.
AbstractThis paper develops a new damage-plasticity model considering the compression-softening effect of RC. The model is based on the framework of a two-scalar damage-plasticity model, and adopts ...the elastoplastic damage energy release rates as the driven force of damage. To account for the compression-softening effect caused by transverse cracks of RC under shear, a softening coefficient is introduced in the model. With the modification, the new damage model can be used to simulate the typical shear behavior of cracked RC structures, which may not be captured by those models developed for plain concrete. Some computational aspects are also discussed. Finally, the model is validated through material tests and a series of RC member tests, and the results indicate that the proposed model has good performance in nonlinear analysis of RC structures.
Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In ...this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3gkg−1 and 26.6gkg−1, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.
Six species of
Girault (Eulophidae: Tetrastichinae) from China are reviewed, including three new species:
,
,
and one new country record,
(Walker, 1839). New distributional data for
(Walker, 1839) ...and
(Szelényi, 1941), and a key to the Chinese species of
based on females, are included.
In this paper, a new species of
Dalman,
is described from Tibet and three species,
Kamijo,
Erdös, and
Kamijo are reported from China for the first time. A detailed description and illustrations of ...the new species are provided, as well as diagnoses and illustrations of the three newly recorded species.
Several recent clinical trials have assessed the effects of dupilumab in uncontrolled asthma, but reached no definite conclusion. We therefore conducted this meta-analysis to evaluate the overall ...efficacy and safety of dupilumab for the treatment of uncontrolled asthma.
All randomized controlled trials were included. Standard mean differences (SMD) or relative risks (RR) were calculated using Fixed-or random-effects models.
Five studies involving 3369 patients were identified. Pooled analysis showed significant improvements in the first-second forced expiratory volume (FEV
) (SMD = 4.29, 95% CI: 2.78-5.81) and Asthma Quality of Life Questionnaire scores (SMD = 4.39, 95% CI: 1.44-7.34). Dupilumab treatments were also associated with significantly decreased 5-item Asthma Control Questionnaire scores (SMD = - 4.95, 95% CI: - 7.30 to - 2.60), AM and PM asthma symptom scores (SMD = - 5.09, 95% CI: - 6.40 to - 3.77; SMD = - 4.92, 95% CI: - 5.98 to - 3.86, respectively), and severe exacerbation risk (RR = 0.73; 95% CI: 0.67-0.79) compared with placebo, with similar incidence of adverse events (RR = 1.0; 95% CI: 0.96-1.04).
Dupilumab treatment is relatively well-tolerated and could significantly improve FEV
, symptoms, asthma control, and quality of life, and reduced severe exacerbation risk in patients with uncontrolled asthma.
Generating random fields over irregular geometries (e.g., two‐dimensional (2D) manifolds embedded in the three‐dimensional (3D) Euclidean space) is a great challenge because the geometry structure is ...complex and the correlation function can hardly be derived, thus the traditional methods, for example, spatial discretization methods or series expansion methods, cannot be directly adopted. To solve this issue, the present paper develops a two‐stage strategy to simulate random fields over manifolds. The core idea is to map the manifolds into the 2D Euclidean space through Isometric feature mapping (Isomap), with which the geodesic distance between points in the mapped 2D Euclidean space and the original manifold space is kept as the same. Therefore, the correlation function can be readily derived and the conventional methods can be directly employed to generate random fields. To validate the proposed method, several different types of manifolds are dimensionally reduced to 2D planar domains, and the stochastic harmonic function method is used to generate the random fields. Finally, case studies on the stochastic finite element analysis of two different structures are also performed to demonstrate the applicability and efficiency of the proposed method.
In large heterogeneous areas the relationship between soil organic carbon (SOC) and environmental covariates may vary throughout the area, bringing about difficulty for accurate modeling of the ...regional SOC variation. The benefit of local, geographically weighted regression (GWR) coefficients was tested in a case study on soil organic carbon mapping across a 50,810km2 area in northwestern China. This area is composed of an alpine ecosystem in the upper reaches and oases in the middle reaches. The benefit was quantified by comparing the quality of the maps obtained by GWR and geographically weighted ridge regression (GWRR) on the one side and multiple linear regression (MLR) on the other side. In these methods spatial dependence of model residuals is ignored. The root mean squared error (RMSE) of predictions of natural log-transformed SOC obtained with GWR was smaller than with MLR: 0.565 versus 0.618g/kg. The use of a local ridge parameter in GWRR did not lead to an increase in accuracy. Besides we compared the quality of maps obtained by geographically weighted regression followed by simple kriging of model residuals (GWRSK) and kriging with an external drift (KED) with global regression coefficients. In these methods the spatial dependence of model residuals is incorporated in the model. The RMSE with KED was smaller than with GWRSK: 0.515 versus 0.546g/kg. We conclude that fitting regression coefficients locally as in GWR only paid when no spatial random effect was included in the model. When a spatial random effect was included, the flexibility of local, geographically weighted regression coefficients was not needed and even undesirable as it led to less accurate predictions than KED with global regression coefficients. In comparing the accuracy of prediction methods by leave-one-out cross-validation (LOOCV) of a non-probability sample it is important to account for possible autocorrelation of pairwise differences in the prediction errors. The effective sample sizes were substantially smaller than the total number of sampling points, so that most pairwise differences in MSE were not significant at a significance level of 10% in a two-sided paired t-test.
•Global and local regressions were compared assuming spatially independent and dependent model residuals.•KED with global regression coefficients outperformed GWR.•The effective sample size of paired t-test was smaller than the number of sampling points.
PDF
