New Insights Into the Relationship Between Seismic Intensity Measures and Nonlinear Structural Response

2021 ◽  
Author(s):  
Yeudy Felipe Vargas-Alzate ◽  
Jorge E. Hurtado ◽  
Lluis G. Pujades
Keyword(s):  
Seismic Risk ◽  
Regression Models ◽  
Fragility Curves ◽  
Large Data ◽  
Statistical Properties ◽  
Spectral Acceleration ◽  
Data Set ◽  
Intensity Measures ◽  
Fragility Function ◽  
Spectral Velocity

Abstract This paper focuses on the probabilistic analysis of Intensity Measures (IMs) and Engineering Demand Parameters (EDPs) for earthquake excitations. Several statistical properties, which are desirable in IMs when they are used to predict EDPs, have been analyzed. The main sources of uncertainty involved in the calculation of the seismic risk have been considered in the analysis. Efficiency, sufficiency and steadfastness have been quantified for a set of IMs with respect to two EDPs: the maximum inter-storey drift ratio, MIDR, and the maximum floor acceleration, MFA. Steadfastness is a new statistical property proposed in this article. It is related to the ability of IMs to forecast EDPs for big building suites. This also means that efficiency does not significantly vary when different types of buildings are included in the statistical analyses. This property allows reducing the number of calculations when performing seismic risk estimations at urban level since, for instance, a large variety of fragility curves of specific buildings can be grouped together within an only one, but more generic, fragility function. The nonlinear dynamic response of probabilistic multi-degree-of-freedom buildings’ models, subjected to a large data set of ground motion records, have been considered to perform the statistical analysis. Specifically, reinforced concrete buildings whose number of stories vary from 3 to 13 stories have been analysed. 18 spectrum-, energy- and direct-accelerogram-based IMs have been considered harein. From the statistical properties of the generated clouds of IM-EDP points, efficiency and sufficiency properties have been quantified. For MIDR, results show that IMs based on spectral velocity are more efficient and steadfast than the ones based on spectral acceleration; spectral velocity averaged in a range of periods, AvSv, has shown to be the most efficient and steadfast IM. The opposite happens for MFA, that is, spectral acceleration-based-IMs are more efficient than the velocity-based ones. A comparison on the use of linear vs quadratic regression models, and their implications on the derivation of fragility functions, is presented as well. Concerning sufficiency, most of the 18 basic IMs analyzed herein do not have this property. However, multi-regression models have been employed to address this lack of sufficiency allowing to obtain a so-called ‘ideal’ IM.

scholarly journals New insights into the relationship between seismic intensity measures and nonlinear structural response

2021 ◽  
Author(s):  
Yeudy F. Vargas-Alzate ◽  
Jorge E. Hurtado ◽  
Luis G. Pujades
Keyword(s):  
Seismic Risk ◽  
Regression Models ◽  
Fragility Curves ◽  
Large Data ◽  
Seismic Intensity ◽  
Dynamic Responses ◽  
Spectral Acceleration ◽  
Data Set ◽  
Intensity Measures ◽  
Spectral Velocity

AbstractThis paper focuses on the probabilistic analysis of Intensity Measures (IMs) and Engineering Demand Parameters (EDPs) in the context of earthquake-induced ground motions. Several statistical properties, which are desirable in IMs when they are used to predict EDPs, have been analysed. Specifically, efficiency, sufficiency and steadfastness have been quantified for a set of IMs with respect to two EDPs: the maximum inter-storey drift ratio, MIDR, and the maximum floor acceleration, MFA. Steadfastness is a new statistical property proposed in this article, which is related to the ability of IMs to forecast EDPs for large building suites. In other words, this means that efficiency does not significantly vary when different types of buildings are simultanously considered in the statistical analyses. This property allows reducing the number of calculations when performing seismic risk estimations at urban level since, for instance, a large variety of fragility curves, representing specific building typologies, can be grouped together within a more generic one. The main sources of uncertainty involved in the calculation of the seismic risk have been considered in the analysis. To do so, the nonlinear dynamic responses of probabilistic multi-degree-of-freedom building models, subjected to a large data set of ground motion records, have been calculated. These models have been generated to simulate the dynamic behavior of reinforced concrete buildings whose number of stories vary from 3 to 13. 18 spectrum-, energy- and direct-accelerogram-based IMs have been considered herein. Then, from clouds of IM-EDP points, efficiency, sufficiency and steadfastness have been quantified. For MIDR, results show that IMs based on spectral velocity are more efficient and steadfast than the ones based on spectral acceleration; spectral velocity averaged in a range of periods, AvSv, has shown to be the most efficient IM with an adequate level of steadfastness. For MFA, spectral acceleration-based-IMs are more efficient than velocity-based ones. A comparison is also presented on the use of linear vs quadratic regression models, and their implications on the derivation of fragility functions. Concerning sufficiency, most of the 18 IMs analysed do not have this property. Nonetheless, multi-regression models have been employed to address this lack of sufficiency allowing to obtain a so-called ‘ideal’ IM.

What Do We Know About Changing Economic Activity of Firms?

2019 ◽  
pp. 232102221886979
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Binomial Regression ◽  
History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

scholarly journals Investigating Efficiency of Vector-Valued Intensity Measures in Seismic Demand Assessment of Concrete Dams

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Mohammad Alembagheri
Keyword(s):  
Fragility Curves ◽  
Coupled System ◽  
Seismic Demand ◽  
Spectral Acceleration ◽  
Dam Reservoir ◽  
Natural Period ◽  
Intensity Measures ◽  
Structural Responses ◽  
Second Period ◽  
Vector Valued

The efficiency of vector-valued intensity measures for predicting the seismic demand in gravity dams is investigated. The Folsom gravity dam-reservoir coupled system is selected and numerically analyzed under a set of two-hundred actual ground motions. First, the well-defined scalar IMs are separately investigated, and then they are coupled to form two-parameter vector IMs. After that, IMs consisting of spectral acceleration at the first-mode natural period of the dam-reservoir system along with a measure of the spectral shape (the ratio of spectral acceleration at a second period to the first-mode spectral acceleration value) are considered. It is attempted to determine the optimal second period by categorizing the spectral acceleration at the first-mode period of vibration. The efficiency of the proposed vector IMs is compared with scalar ones considering various structural responses as EDPs. Finally, the probabilistic seismic behavior of the dam is investigated by calculating its fragility curves employing scalar and vector IMs considering the effect of zero response values.

scholarly journals Characterizing Ground Motions that Collapse Steel Special Moment-Resisting Frames or Make Them Unrepairable

2015 ◽  
Vol 31 (2) ◽  
pp. 813-840 ◽  
Author(s):  
Anna H. Olsen ◽  
Thomas H. Heaton ◽  
John F. Hall
Keyword(s):  
Regression Models ◽  
Ground Motions ◽  
Spectral Acceleration ◽  
Ground Displacement ◽  
Intensity Measures ◽  
Moment Resisting Frame ◽  
Frame Buildings ◽  
Moment Resisting ◽  
Equivalent Frame ◽  
Special Moment Resisting Frame

This work applies 64,765 simulated seismic ground motions to four models each of 6- or 20-story, steel special moment-resisting frame buildings. We consider two vector intensity measures and categorize the building response as “collapsed,” “unrepairable,” or “repairable.” We then propose regression models to predict the building responses from the intensity measures. The best models for “collapse” or “unrepairable” use peak ground displacement and velocity as intensity measures, and the best models predicting peak interstory drift ratio, given that the frame model is “repairable,” use spectral acceleration and epsilon ( ∊) as intensity measures. The more flexible frame is always more likely than the stiffer frame to “collapse” or be “unrepairable.” A frame with fracture-prone welds is substantially more susceptible to “collapse” or “unrepairable” damage than the equivalent frame with sound welds. The 20-story frames with fracture-prone welds are more vulnerable to P-delta instability and have a much higher probability of collapse than do any of the 6-story frames.

scholarly journals Risk Assessment of Aged Concrete Gravity Dam Subjected to Material Deterioration Under Seismic Excitation

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Tahmina Tasnim Nahar ◽  
Anh-Tuan Cao ◽  
Dookie Kim
Keyword(s):  
Mathematical Model ◽  
Risk Assessment ◽  
Seismic Risk ◽  
Fragility Curves ◽  
Seismic Excitation ◽  
Gravity Dam ◽  
Specific Time ◽  
Concrete Gravity Dam ◽  
Fragility Function ◽  
Cumulative Absolute Velocity

Abstract This paper proposes an approach to assess and predict the seismic risk of existing concrete gravity dams (CGDs) considering the ageing effect. The combination of fragility function and cumulative absolute velocity (CAV) depending on two failure states has been used in the analysis. It represents the time-variant degradation of the concrete structure and the conditional change of structural vulnerability in the case of the seismic excitation. Therefore, the seismic risk assessment captures here the nonlinear dynamic behavior of a concrete gravity dam through the fragility analysis. Incremental dynamic analysis for the fragility curves is adopted to state the performance of the dam in terms of different intensity measures. To assess the capacity of the aged concrete gravity dam, this research introduces a way to estimate the CAVlimit of CGDs with varying time. For a case study, an existing concrete gravity dam in Korea has been taken into consideration to apply this approach. The numerical finite element model is validated by optimizing the recorded field data. The proposed approach and its findings will be helpful to CGDs operators to ensure whether a dam needs to stop after a specific time using the extracted mathematical model. Furthermore, as this mathematical model is the function of time, the operator can get an idea about dam conditions at any specific time and can take necessary steps.

scholarly journals Investigating ANN architectures and training to estimate snow water equivalent from snow depth

2021 ◽  
Vol 25 (6) ◽  
pp. 3017-3040
Author(s):  
Konstantin F. F. Ntokas ◽  
Jean Odry ◽  
Marie-Amélie Boucher ◽  
Camille Garnaud
Keyword(s):  
Snow Depth ◽  
Regression Models ◽  
Snow Water Equivalent ◽  
Structural Characteristics ◽  
Large Data ◽  
Multilayer Perceptrons ◽  
Snow Density ◽  
General Applicability ◽  
Data Set ◽  
Snow Water

Abstract. Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Measurements of SWE are, however, expensive and not continuously accessible in real time. This motivates a search for alternative ways of estimating SWE from measurements that are more widely available and continuous over time. SWE can be calculated by multiplying snow depth by the bulk density of the snowpack. Regression models proposed in the literature first estimate snow density and then calculate SWE. More recently, a novel approach to this problem has been developed and is based on an ensemble of multilayer perceptrons (MLPs). Although this approach compared favorably with existing regression models, snow density values at the lower and higher ends of the range remained inaccurate. Here, we improve upon this recent method for determining SWE from snow depth. We show the general applicability of the method through the use of a large data set of 234 779 snow depth–density–SWE records from 2878 nonuniformly distributed sites across Canada. These data cover almost 4 decades of snowfall. First, it is shown that the direct estimation of SWE produces better results than the estimation of snow density, followed by the calculation of SWE. Second, testing several artificial neural network (ANN) structural characteristics improves estimates of SWE. Optimizing MLP parameters separately for each snow climate class gives a greater representation of the geophysical diversity of snow. Furthermore, the uncertainty of snow depth measurements is included for a more realistic estimation. A comparison with commonly used regression models reveals that the ensemble of MLPs proposed here leads to noticeably more accurate estimates of SWE. This study thus shows that delving deeper into ANN theory helps improve SWE estimation.

What Do We Know About Changing Economic Activity of Firms?

2020 ◽  
Vol 8 (2) ◽  
pp. 149-169
Author(s):  
Radhika Pandey ◽  
Amey Sapre ◽  
Pramod Sinha
Keyword(s):  
Economic Activity ◽  
Regression Models ◽  
Statistical Approach ◽  
Negative Binomial ◽  
Large Data ◽  
Poisson Model ◽  
Negative Binomial Regression ◽  
Data Set ◽  
Binomial Regression ◽  
History Of

Identification of primary economic activity of firms is a prerequisite for compiling several macro aggregates. In this paper, we take a statistical approach to understand the extent of changes in primary economic activity of firms over time and across different industries. We use the history of economic activity of over 46,000 firms spread over 25 years from CMIE Prowess to identify the number of times firms change the nature of their business. Using the count of changes, we estimate Poisson and Negative Binomial regression models to gain predictability over changing economic activity across industry groups. We show that a Poisson model accurately characterizes the distribution of count of changes across industries and that firms with a long history are more likely to have changed their primary economic activity over the years. Findings show that classification can be a crucial problem in a large data set like the MCA21 and can even lead to distortions in value addition estimates at the industry level. JEL Classifications: D22, E00, E01

scholarly journals Using an ensemble of artificial neural networks to convert snow depth to snow water equivalent over Canada

2020 ◽  
Author(s):  
Konstantin Franz Fotios Ntokas ◽  
Jean Odry ◽  
Marie-Amélie Boucher ◽  
Camille Garnaud
Keyword(s):  
Snow Depth ◽  
Regression Models ◽  
Snow Water Equivalent ◽  
Large Data ◽  
Multilayer Perceptrons ◽  
Snow Density ◽  
General Applicability ◽  
Data Set ◽  
Snow Water ◽  
Artificial Neural

Abstract. Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Measurements of SWE are, however, expensive and not continuously accessible in real time. This motivates a search for alternative ways of estimating SWE from measurements that are more widely available and continuous over time. SWE can be calculated by multiplying snow depth with the bulk density of the snowpack. Regression models proposed in the literature first estimate snow density and then calculate SWE. More recently, a novel approach to this problem has been developed and is based on an ensemble of multilayer perceptrons (MLPs). Although this approach compared favourably with existing regression models, snow density values at the lower and higher ends of the range remained inaccurate. Here, we improve upon this recent method for determining SWE from snow depth. We show the general applicability of the method through the use of a large data set of 234 779 snow depth-density-SWE records from 2878 non-uniformly distributed sites across Canada. These data cover almost four decades of snowfall. First, it is shown that the direct estimation of SWE produces better results than the estimation of snow density followed by the calculation of SWE. Second, optimizing MLP parameters separately for each snow climate class further improves the accuracy of SWE estimates. A comparison with commonly used regression models reveals that the ensemble of MLPs proposed here leads to noticeably more accurate estimates of SWE. This study thus shows that delving deeper into artificial neural network theory helps improve SWE estimation and that using a greater number of MLP parameters could lead to even further improvements.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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