scholarly journals Gaussian Kernel Methods for Seismic Fragility and Risk Assessment of Mid-Rise Buildings

2021 ◽  
Vol 13 (5) ◽  
pp. 2973
Author(s):  
Somayajulu L. N. Dhulipala
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Kernel Methods ◽  
Structural Response ◽  
Gaussian Kernel ◽  
Seismic Fragility ◽  
Fragility Functions ◽  
Moment Frame ◽  
Damage States ◽  
Cloud Analysis

Seismic fragility functions can be evaluated using the cloud analysis method with linear regression which makes three fundamental assumptions about the relation between structural response and seismic intensity: log-linear median relationship, constant standard deviation, and Gaussian distributed errors. While cloud analysis with linear regression is a popular method, the degree to which these individual and compounded assumptions affect the fragility and the risk of mid-rise buildings needs to be systematically studied. This paper conducts such a study considering three building archetypes that make up a bulk of the building stock: RC moment frame, steel moment frame, and wood shear wall. Gaussian kernel methods are employed to capture the data-driven variations in the median structural response and standard deviation and the distributions of residuals with the intensity level. With reference to the Gaussian kernels approach, it is found that while the linear regression assumptions may not affect the fragility functions of lower damage states, this conclusion does not hold for the higher damage states (such as the Complete state). In addition, the effects of linear regression assumptions on the seismic risk are evaluated. For predicting the demand hazard, it is found that the linear regression assumptions can impact the computed risk for larger structural response values. However, for predicting the loss hazard with downtime as the decision variable, linear regression can be considered adequate for all practical purposes.

Analysis of Seismic Fragility Functions of Highway Embankments

2020 ◽  
Author(s):  
Balázs Hübner ◽  
András Mahler
Keyword(s):  
Finite Element ◽  
Earthquake Engineering ◽  
Seismic Fragility ◽  
Response Analysis ◽  
Fragility Functions ◽  
Ground Response ◽  
Ground Response Analysis ◽  
Fragility Function ◽  
Damage States ◽  
Log Normal

Vulnerability assessment of structures is a vitally important topic among earthquake engineering researchers. Generally, their primary focus is on the seismic performance of buildings. Less attention is paid to geotechnical structures, even though information about the performance of these structures (e.g. road embankments, levees, cuts) during an earthquake is essential for planning remediation and rescue efforts after disasters. In this paper the seismic fragility functions of a highway embankment are defined following an analytical methodolgy. The technique is a displacement-based evaluation of seismic vulnerability. Displacements of an embankment during a seismic event are approximated by a 2-D nonlinear ground response analysis using the finite element method. The numerical model was calibrated based on the results of a 1-D nonlinear ground response analysis. The expected displacements were calculated for 3 different embankment heights and Peak Ground Acceleration (PGA) values between 0,05 and 0,35g. Based on the results of the 2-D finite element analysis, the relationship between displacements and different seismic intensity measures (PGA, Arias-intensity) was investigated. Different damage states were considered, and the probability of their exceedance was investigated. The seismic fragility functions of the embankments were developed based on probability of exceedance of these different damage states based on a log-normal fragility function. The legitimacy of using a log-normal fragility function is also examined.

scholarly journals Enhanced Cloud Method (E-Cloud) for Efficient Seismic Fragility Assessment of Structures

2021 ◽  
Author(s):  
Lujie Zhuang ◽  
Yutao Pang
Keyword(s):  
Linear Regression ◽  
Fragility Curves ◽  
Time History ◽  
Frame Structure ◽  
Seismic Fragility ◽  
Seismic Responses ◽  
Demand Models ◽  
Dynamic Analyses ◽  
Cloud Points ◽  
Cloud Analysis

Abstract Cloud analysis is based on linear regression in the logarithmic space by using least squares, in which a large number of nonlinear dynamic analyses are usually suggested to ensure the accuracy of this method. So, it needs significant computational effort to establish fragility curves especially for the complicated structures. The present paper proposed the Enhanced Cloud Method (E-Cloud) to enhance the efficiency but maintain the accuracy of the Cloud method. The basic concept of the proposed “E-Cloud” aims to utilize both maximum and additional seismic responses with corresponding intensity measures (IMs) from ground motions for the logarithmic linear regression of the Cloud method. Since the nonlinear time-history responses can be transferred to the Engineering Demand Parameter (EDP)-IM curve at the duration when the ground motion is intensifying, the additional seismic responses at different IM levels (i.e., potential Cloud points) can be selected from this EDP-IM curve. These potential Cloud points are combined with maximum seismic responses for the regression so as to reduce the required number of dynamic analyses in Cloud analysis. The proposed “E-Cloud” method is applied for the case study of a typical RC frame structure. By comparison of the obtained probabilistic seismic demand models and fragility curves from “E-Cloud” method to Cloud analysis, it is demonstrated that the E-Cloud method can significantly improve the computational efficiency of the Cloud analysis, which also leads to accurate and stable results for the seismic fragility assessment of structures.

scholarly journals Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1105 ◽  
Author(s):  
Davide Astolfi ◽  
Francesco Castellani ◽  
Andrea Lombardi ◽  
Ludovico Terzi
Keyword(s):  
Standard Deviation ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Curve Analysis ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Features Selection ◽  
Power Curve ◽  
Working Parameters ◽  
Extracted Power

Due to the stochastic nature of the source, wind turbines operate under non-stationary conditions and the extracted power depends non-trivially on ambient conditions and working parameters. It is therefore difficult to establish a normal behavior model for monitoring the performance of a wind turbine and the most employed approach is to be driven by data. The power curve of a wind turbine is the relation between the wind intensity and the extracted power and is widely employed for monitoring wind turbine performance. On the grounds of the above considerations, a recent trend regarding wind turbine power curve analysis consists of the incorporation of the main working parameters (as, for example, the rotor speed or the blade pitch) as input variables of a multivariate regression whose target is the power. In this study, a method for multivariate wind turbine power curve analysis is proposed: it is based on sequential features selection, which employs Support Vector Regression with Gaussian Kernel. One of the most innovative aspects of this study is that the set of possible covariates includes also minimum, maximum and standard deviation of the most important environmental and operational variables. Three test cases of practical interest are contemplated: a Senvion MM92, a Vestas V90 and a Vestas V117 wind turbines owned by the ENGIE Italia company. It is shown that the selection of the covariates depends remarkably on the wind turbine model and this aspect should therefore be taken in consideration in order to customize the data-driven monitoring of the power curve. The obtained error metrics are competitive and in general lower with respect to the state of the art in the literature. Furthermore, minimum, maximum and standard deviation of the main environmental and operation variables are abundantly selected by the feature selection algorithm: this result indicates that the richness of the measurement channels contained in wind turbine Supervisory Control And Data Acquisition (SCADA) data sets should be exploited for monitoring the performance as reliably as possible.

Influence of farm, parity, season and litter size on the lactation curve parameters of white British dairy goats

1993 ◽  
Vol 57 (1) ◽  
pp. 99-104 ◽  
Author(s):  
J. C. Williams
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Litter Size ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Dairy Goats ◽  
Lactation Curve ◽  
Non Linear ◽  
Research Institute

AbstractThe following goat lactation model was fitted (using non-linear regression) to 407 lactations from five commercial goat dairies and one Research Institute goat herd: y = A exp (B(l + n'/2)n' + Cn' 2 - 1·01/n) where y = daily yield in kg; n = day of lactation (post parturition); and n' = (n -150)1100.Influence of farm, parity and season on the parameter estimates for 376 individual lactations was studied, using multiple linear regression. The models adopted were of the form: A = 1·366 + 1·122 × parity - 0·137 × parity2; ln(-B) = - 1·711 + 0·107 × parity + 0·512 season one; C = 0·037, with a standard deviation for A of 0·658, for ln(-B) of 0·636 and for C of 0·127.Influence of litter size on parameters was investigated for the Research Institute herd. There was no evidence of an effect on any of the model parameters.

Breastfeeding Self-Efficacy and Its Relationship with the Perceived Stress and the Breastfeeding Performance in Iranian Mothers with Late Preterm Infants, 2019

2020 ◽  
Author(s):  
Akram Kahforoushan ◽  
Shirin Hasanpour ◽  
Mojgan Mirghafourvand
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Preterm Infants ◽  
Perceived Stress ◽  
Self Efficacy ◽  
Short Form ◽  
Late Preterm ◽  
Late Preterm Infants ◽  
The Mean

Abstract BackgroundLate preterm infants suffer from many short-term and long-term problems after birth. The key factor in fighting these problems is effective breastfeeding. The present study aimedto determine the breastfeeding self-efficacy and its relationship with the perceived stress and breastfeeding performance in mothers with late preterm infants. MethodsIn this prospective study, 171 nursing mothers with late preterm infants born in Alzahra Medical Center of Tabriz, Iran, who met the conditions of this study were selected through convenience sampling. The Breastfeeding Self-Efficacy Scale-Short Form (BSES- SF) was employed to measure breastfeeding self-efficacy and 14-item Perceived Stress Scale (PSS14) was used to measure the perceived stress during 24 hours after giving birth and when the child was 4 months old the breastfeeding performance was measured by the standard breastfeeding performance questionnaire. The data were analyzed by Pearson and Spearman’s correlation tests, independent t-test, one-way ANOVA, and Multiple Linear Regression.ResultsThe mean (standard deviation) of breastfeeding self-efficacy equaled 50.0 (7.8) from the scores ranging between13-65 and the mean (standard deviation) of the perceived stress equaled to 26.5 (8.8) from the scores ranging between 0-56. The median (25-75 percentiles) of breastfeeding performance score in the mothers equaled 2.0 (1.0 to 3.0) from the scores ranging between 0-6. On the basis of multiple linear regression and through adjusting the personal-social characteristic, by increasing the score of the breastfeeding self-efficacy, the perceived stress was decreased to a statistically significant amount (B=-0.1, 95%CI=-0.3 to 0.0), however, there was no statistically significant relationship between breastfeeding self-efficacy and breastfeeding performance (p=0.418). ConclusionDue to the modifiable variability of breastfeeding self-efficacy and its role in perceived maternal stress, the development of appropriate strategies to further increase breastfeeding self-efficacy and provide more support to these mothers and infants is of particular importance.

Descriptive and Predictive Analytical Methods for Big Data

Web Services ◽  
2019 ◽  
pp. 314-331 ◽  
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

scholarly journals Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

Heredity ◽  
2020 ◽  
Vol 126 (1) ◽  
pp. 92-106 ◽  
Author(s):  
Germano Costa-Neto ◽  
Roberto Fritsche-Neto ◽  
José Crossa
Keyword(s):  
Kernel Methods ◽  
Large Scale ◽  
Additive Model ◽  
Computation Time ◽  
Reaction Norm ◽  
Model Complexity ◽  
Gaussian Kernel ◽  
Environment Interaction ◽  
Complex Structures ◽  
Processing Times

AbstractModern whole-genome prediction (WGP) frameworks that focus on multi-environment trials (MET) integrate large-scale genomics, phenomics, and envirotyping data. However, the more complex the statistical model, the longer the computational processing times, which do not always result in accuracy gains. We investigated the use of new kernel methods and modeling structures involving genomics and nongenomic sources of variation in two MET maize data sets. Five WGP models were considered, advancing in complexity from a main-effect additive model (A) to more complex structures, including dominance deviations (D), genotype × environment interaction (AE and DE), and the reaction-norm model using environmental covariables (W) and their interaction with A and D (AW + DW). A combination of those models built with three different kernel methods, Gaussian kernel (GK), Deep kernel (DK), and the benchmark genomic best linear-unbiased predictor (GBLUP/GB), was tested under three prediction scenarios: newly developed hybrids (CV1), sparse MET conditions (CV2), and new environments (CV0). GK and DK outperformed GB in prediction accuracy and reduction of computation time (~up to 20%) under all model–kernel scenarios. GK was more efficient in capturing the variation due to A + AE and D + DE effects and translated it into accuracy gains (~up to 85% compared with GB). DK provided more consistent predictions, even for more complex structures such as W + AW + DW. Our results suggest that DK and GK are more efficient in translating model complexity into accuracy, and more suitable for including dominance and reaction-norm effects in a biologically accurate and faster way.

Shake Table Testing of a Low Damage Steel Building with 2-4 Displacement Dependent (D3) Viscous Damper

2018 ◽  
Vol 763 ◽  
pp. 331-338
Author(s):  
Nikoo K. Hazaveh ◽  
Ali A. Rad ◽  
Geoffrey W. Rodgers ◽  
J. Geoffrey Chase ◽  
Stefano Pampanin ◽  
...  
Keyword(s):  
Viscous Fluid ◽  
Residual Deformation ◽  
Structural Response ◽  
Steel Structure ◽  
Experimental Tests ◽  
Base Shear ◽  
Moment Frame ◽  
Fluid Damper ◽  
Resistive Forces ◽  
Viscous Fluid Damper

To improve seismic structural performance, supplemental damping devices can be incorporated to absorb seismic response energy. The viscous fluid damper is a well-known solution. However, while they reduce displacement demand, they can increase overall base shear demand in nonlinear structures as they provide resistive forces in all four quadrants of force-displacement response. In contrast, Direction and Displacement Dependent (D3) viscous fluid dampers offer the opportunity to simultaneously reduce structural displacements and the total base-shear force as they only produce resistive forces in the second and fourth quadrants of a structural hysteresis plot. The research experimentally examines the response of a half-scale, 2-storey moment frame steel structure fitted with a 2-4 configuration D3 viscous fluid damper. The structure is also tested with conventional viscous dampers to establish a baseline response and enable comparison of results. Dynamic experimental tests are used to assesses the base shear, maximum drift and residual deformation under 5 different earthquakes (Northridge, Kobe, Christchurch (CCCC), Christchurch (CHHC), and Bam ground motion). Response metrics including base shear, the maximum structural displacement, and peak structural accelerations are used to quantify performance and to assess the response reductions achieved through the addition of dampers. It is concluded that only the 2-4 device is capable of providing concurrent reductions in all three of these structural response metrics.

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