Sensitivity analysis of pounding between adjacent structures          下载免费PDF全文

Vincent Crozet  Ioannis Politopoulos  Mingguan Yang  Jean‐Marc Martinez  Silvano Erlicher 《地震工程与结构动力学》2018,47(1):219-235

This article deals with sensitivity of the response of pounding buildings with respect to structural and earthquake excitation parameters. A comprehensive sensitivity analysis is carried out by means of Monte Carlo simulations of adjacent single degree of freedom impacting oscillators. This sensitivity analysis, based on Sobol's method, computes sensitivity indexes which provide a consistent measure of the relative importance of parameters such as the dimensionless main excitation frequency, the mass and frequency ratios of the structures, and the coefficient of restitution. Moreover, the influence of nonlinear behavior of the impacting structures is also considered. The consequences of pounding on the structures themselves are analyzed in terms of maximum force and nonlinear demand amplification compared with the case without pounding. As for the influence of pounding on the floor response spectra, the quantity of interest is the maximum impact impulse. The overall conclusions of this analysis are that the frequency ratio is the most important parameter as far as the maximum force and nonlinear demand are concerned. Regarding the maximum impact impulse, the mass and frequency ratios are, in general, the most influential parameters, the mass ratio being predominant for low frequencies of the oscillator of interest.  相似文献   

Integrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field,south-western Barents Sea     

Honoré Yenwongfai  Nazmul Haque Mondol  Isabelle Lecomte  Jan Inge Faleide  Johan Leutscher 《Geophysical Prospecting》2019,67(4):1020-1039

Seismic petro-facies characterization in low net-to-gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro-facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro-facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies-dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro-gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms-Finnmark Fault Complex. The facies-based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies-based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.  相似文献   

An unusual porous,cryptocrystalline forsterite chondrule in Murchison     

Michael Zolensky  Tomoki Nakamura  James Martinez  Yuma Enokido 《Meteoritics & planetary science》2021,56(1):56-60

We describe a fragmented cryptocrystalline chondrule consisting solely of forsterite (Fo₉₈) in the Murchison CM2 chondrite, with a peculiar porous texture of enigmatic origin.  相似文献   

Spatial land use regression of nitrogen dioxide over a 5-year interval in Calgary,Canada     

Stefania Bertazzon  Isabelle Couloigner  Fox E. Underwood 《International journal of geographical information science》2019,33(7):1335-1354

We analysed the spatial distribution of nitrogen dioxide over Calgary (Canada) in summer 2010 and winter 2011 and in summer 2015 and winter 2016, and estimated land use regressions for 2015–16 (2010–11 models were estimated previously). As nitrogen dioxide exhibited spatial clustering, we evaluated the following spatial specifications against a linear model: spatially autoregressive (lag), spatially autoregressive (error), and geographically weighted regression. The spatially autoregressive (lag) specification performed best, achieving goodness-of-fit aligned with or greater than values reported in the literature. We compared the 2015–16 spatially autoregressive models with the 2010–11 models and reparametrized them on the 2010–11 and the 2015–16 data. Finally, we identified a single set of predictors to best fit the data. Nitrogen dioxide concentration decreased over the 5 years, retaining consistent spatial and seasonal patterns, with higher concentrations over traffic corridors and industrial areas, and greater variation in summer than winter. The multi-temporal analysis suggested that spatial land use regressions were robust over the time interval, despite moderate land use change. Multi-temporal spatial land use regressions yielded consistent predictors for each season over time, which can aid estimation of air pollution at fine spatial resolution over an extended time period.  相似文献   

Ontologies to interpret remote sensing images: why do we need them?     

Damien Arvor  Mariana Belgiu  Zoe Falomir  Isabelle Mougenot  Laurent Durieux 《地理信息系统科学与遥感》2019,56(6):911-939

The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts’ expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.  相似文献