Evaluation of ASCE 7 equations for designing acceleration‐sensitive nonstructural components using data from instrumented buildings

This study uses instrumented buildings and models of code‐based designed buildings to validate the results of previous studies that highlighted the need to revise the ASCE 7 F_p equation for designing nonstructural components (NSCs) through utilizing oversimplified linear and nonlinear models. The evaluation of floor response spectra of a large number of instrumented buildings illustrates that, unlike the ASCE 7 approach, the in‐structure and the component amplification factors are a function of the ratio of NSC period to the supporting building modal periods, the ground motion intensity, and the NSC location. It is also shown that the recorded ground motions at the base of instrumented buildings in most cases are significantly lower than design earthquake (DE) ground motions. Because ASCE 7 is meant to provide demands at a DE level, for a more reliable evaluation of the F_p equation, 2 representative archetype buildings are designed based on the ASCE 7‐16 seismic provisions and exposed to various ground motion intensity levels (including those consistent with the ones experienced by instrumented buildings and the DE). Simulation results of the archetype buildings, consistent with previous numerical studies, illustrate the tendency of the ASCE 7 in‐structure amplification factor, [1 + 2(z/h)] , to significantly overestimate demands at all floor levels and the ASCE 7 limit of to in many cases underestimate the calculated NSC amplification factors. Furthermore, the product of these 2 amplification factors (that represents the normalized peak NSC acceleration) in some cases exceeds the ASCE 7 equation by a factor up to 1.50.     

Spatio‐temporal tracer variability in the glacier melt end‐member — How does it affect hydrograph separation results?

Geochemical and isotopic tracers were often used in mixing models to estimate glacier melt contributions to streamflow, whereas the spatio‐temporal variability in the glacier melt tracer signature and its influence on tracer‐based hydrograph separation results received less attention. We present novel tracer data from a high‐elevation catchment (17 km², glacierized area: 34%) in the Oetztal Alps (Austria) and investigated the spatial, as well as the subdaily to monthly tracer variability of supraglacial meltwater and the temporal tracer variability of winter baseflow to infer groundwater dynamics. The streamflow tracer variability during winter baseflow conditions was small, and the glacier melt tracer variation was higher, especially at the end of the ablation period. We applied a three‐component mixing model with electrical conductivity and oxygen‐18. Hydrograph separation (groundwater, glacier melt, and rain) was performed for 6 single glacier melt‐induced days (i.e., 6 events) during the ablation period 2016 (July to September). Median fractions (±uncertainty) of groundwater, glacier melt, and rain for the events were estimated at 49±2%, 35±11%, and 16±11%, respectively. Minimum and maximum glacier melt fractions at the subdaily scale ranged between 2±5% and 76±11%, respectively. A sensitivity analysis showed that the intraseasonal glacier melt tracer variability had a marked effect on the estimated glacier melt contribution during events with large glacier melt fractions of streamflow. Intra‐daily and spatial variation of the glacier melt tracer signature played a negligible role in applying the mixing model. The results of this study (a) show the necessity to apply a multiple sampling approach in order to characterize the glacier melt end‐member and (b) reveal the importance of groundwater and rainfall–runoff dynamics in catchments with a glacial flow regime.     

Effect of irregular structural configuration on floor acceleration demand in hill‐side buildings

A suite of reinforced‐concrete frame buildings located on hill sides, with 2 different structural configurations, viz step‐back and split‐foundation, are analyzed to study their floor response. Both step‐back and split‐foundation structural configurations lead to torsional effects in the direction across the slope due to the presence of shorter columns on the uphill side. Peak floor acceleration and floor response spectra are obtained at each storey's center of rigidity and at both its stiff and flexible edges. As reported in previous studies as well, it is observed that the floor response spectra are better correlated with the ground response spectrum. Therefore, the floor spectral amplification functions are obtained as the ratio of spectral ordinates at different floor levels to the one at the ground level. Peaks are observed in the spectral amplification functions corresponding to the first 2 modes in the upper portion of the hill‐side buildings, whereas a single peak corresponding to a specific kth mode of vibration is observed on the floors below the uppermost foundation level. Based on the numerical study for the step‐back and split‐foundation hill‐side buildings, simple floor spectral amplification functions are proposed and validated. The proposed spectral amplification functions take into account both the buildings' plan and elevation irregularities and can be used for seismic design of acceleration‐sensitive nonstructural components, given that the supporting structure's dynamic characteristics, torsional rotation, ground‐motion response spectrum, and location of the nonstructural components within the supporting structure are known, because current code models are actually not applicable to hill‐side buildings.     

Improvement of user’s accuracy through classification of principal component images and stacked temporal images

The classification accuracy of the various categories on the classified remotely sensed images are usually evaluated by two different measures of accuracy, namely, producer’s accuracy (PA) and user’s accuracy (UA). The PA of a category indicates to what extent the reference pixels of the category are correctly classified, whereas the UA of a category represents to what extent the other categories are less misclassified into the category in question. Therefore, the UA of the various categories determines the reliability of their interpretation on the classified image and is more important to the analyst than the PA. The present investigation has been performed in order to determine if there occurs improvement in the UA of the various categories on the classified image of the principal components of the original bands and on the classified image of the stacked image of two different years. We performed the analyses using the IRS LISS III images of two different years, i.e., 1996 and 2009, that represent the different magnitude of urbanization and the stacked image of these two years pertaining to Ranchi area, Jharkhand, India, with a view to assessing the impacts of urbanization on the UA of the different categories. The results of the investigation demonstrated that there occurs significant improvement in the UA of the impervious categories in the classified image of the stacked image, which is attributable to the aggregation of the spectral information from twice the number of bands from two different years. On the other hand, the classified image of the principal components did not show any improvement in the UA as compared to the original images.     

Assessing model of highway slope stability based on optimized SVM

Considering the geological hazards attributed to the highway slope, using a common simple model cannot accurately assess the stability of the slope. First, principal component analysis (PCA) was conducted to extract the principal components of six factors (namely, bulk density, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio) affecting the slope stability. Second, four principal components were adopted as input variables of the support vector machine (SVM) model optimized by genetic algorithm (GA). The output variable was slope stability. Lastly, the assessing model of highway slope stability based on PCA-GA-SVM is established. The maximal absolute error of the model is 0.0921 and the maximal relative error is 9.21% by comparing the assessment value and the practical value of the test sample. The above studies are conducive to enrich the assessing model of highway slope stability and provide some reference for highway slope engineering treatment.     

Investigating the vibration properties of integrated ceiling systems considering interactions with surrounding equipment

Studies on recent earthquakes highlighted that buildings with minimal structural damage still suffer from extensive damage and failure of nonstructural components. The dropping and damage of suspended ceiling systems, which typically consist of acceleration-sensitive nonstructural elements, resulted in lengthy functional disruptions and extended recovery time. This article experimentally and analytically examined the vibration properties of an integrated ceiling system considering the interactions with surrounding electrical equipment. The theoretical stiffness and corresponding frequency of electrical equipment were initially derived and then verified by subsequent vibration tests and numerical analyses. The seismic performance of the air conditioner (AC) was evaluated with different installment configurations based on design spectra and floor response spectra. Vibration tests of the suspended integrated ceiling system considering the interactions with surrounding equipment showed that the inclusion of peripheral constraints increased the first horizontal vibration frequency of the ceiling system by a factor of approximately 6. The natural frequencies of all components in the integrated ceiling system were almost identical, which was attributed to the coupled behavior between the ceiling panels and surrounding equipment, emphasizing the effect of interactions between adjacent components during dynamic analysis. Based on the above experimental investigation, an associated numerical model of the integrated ceiling system was created. Finally, corresponding parametric studies that included the interactions with surrounding equipment, reinforcing braces of ACs and strengthening members at the rise-up location between two elevations were performed.     

主成分监督分类及其在水质特征遥感图像识别中的应用

建立了一种水域水质状况图像识别的主成分监督分类方法,首先通过ＴＭ水域图像数据的主成分分析,将原有各波段图谱的显著且独立的信息集中在数目尽可能少的合成图象中,再依据不同类型水体的光谱特征,分析各主成分图像的构成及其环境生态学含义,由此对整个研究区域内存在的不同标志类型及其分布特征有所了解,在此基础上,选定训练样本集,从而人有清楚的环境生态意义的标志类型,应用监督法得到较好的识别分类结果,分析表明,这     

Using multivariate statistical analysis of groundwater major cation and trace element concentrations to evaluate groundwater flow in a regional aquifer

Groundwater samples were collected from 11 springs in Ash Meadows National Wildlife Refuge in southern Nevada and seven springs from Death Valley National Park in eastern California. Concentrations of the major cations (Ca, Mg, Na and K) and 45 trace elements were determined in these groundwater samples. The resultant data were subjected to evaluation via the multivariate statistical technique principal components analysis (PCA), to investigate the chemical relationships between the Ash Meadows and Death Valley spring waters, to evaluate whether the results of the PCA support those of previous hydrogeological and isotopic studies and to determine if PCA can be used to help delineate potential groundwater flow patterns based on the chemical compositions of groundwaters. The results of the PCA indicated that groundwaters from the regional Paleozoic carbonate aquifers (all of the Ash Meadows springs and four springs from the Furnace Creek region of Death Valley) exhibited strong statistical associations, whereas other Death Valley groundwaters were chemically different. The results of the PCA support earlier studies, where potentiometric head levels, δ¹⁸O and δD, geological relationships and rare earth element data were used to evaluate groundwater flow, which suggest groundwater flows from Ash Meadows to the Furnace Creek springs in Death Valley. The PCA suggests that Furnace Creek groundwaters are moderately concentrated Ash Meadows groundwater, reflecting longer aquifer residence times for the Furnace Creek groundwaters. Moreover, PCA indicates that groundwater may flow from springs in the region surrounding Scotty's Castle in Death Valley National Park, to a spring discharging on the valley floor. The study indicates that PCA may provide rapid and relatively cost‐effective methods to assess possible groundwater flow regimes in systems that have not been previously investigated. Copyright © 1999 John Wiley & Sons, Ltd.     

Prediction of diagenetic facies using well logs – A case study from the upper Triassic Yanchang Formation,Ordos Basin,China

Understanding diagenetic heterogeneity in tight sandstone reservoirs is vital for hydrocarbon exploration. As a typical tight sandstone reservoir, the seventh unit of the Upper Triassic Yanchang Formation in the Ordos Basin (Chang 7 unit), central China, is an important oil-producing interval. Results of helium porosity and permeability and petrographic assessment from thin sections, X-ray diffraction, scanning electron microscopy and cathodoluminescence analysis demonstrate that the sandstones have encountered various diagenetic processes encompassing mechanical and chemical compaction, cementation by carbonate, quartz, clay minerals, and dissolution of feldspar and lithic fragments. The sandstones comprise silt-to medium-grained lithic arkoses to feldspathic litharenites and litharenites, which have low porosity (0.5%–13.6%, with an average of 6.8%) and low permeability (0.009 × 10⁻³ μm² to 1.818 × 10⁻³ μm², with an average of 0.106 × 10⁻³ μm²).This study suggests that diagenetic facies identified from petrographic observations can be up-scaled by correlation with wire-line log responses, which can facilitate prediction of reservoir quality at a field-scale. Four diagenetic facies are determined based on petrographic features including intensity of compaction, cement types and amounts, and degree of dissolution. Unstable and labile components of sandstones can be identified by low bulk density and low gamma ray log values, and those sandstones show the highest reservoir quality. Tightly compacted sandstones/siltstones, which tend to have high gamma ray readings and relatively high bulk density values, show the poorest reservoir quality. A model based on principal component analysis (PCA) is built and show better prediction of diagenetic facies than biplots of well logs. The model is validated by blind testing log-predicted diagenetic facies against petrographic features from core samples of the Upper Triassic Yanchang Formation in the Ordos Basin, which indicates it is a helpful predictive model.