scholarly journals Safety Evaluation of a Water-Immersed Bridge Against Multiple Hazards via Machine Learning

2019 ◽  
Vol 9 (15) ◽  
pp. 3116
Author(s):  
Kuo-Wei Liao ◽  
Fu-Sheng Chien ◽  
Rong-Jing Ju
Keyword(s):  
Machine Learning ◽  
Fragility Curves ◽  
Plastic Hinge ◽  
Pushover Analysis ◽  
Safety Evaluation ◽  
Time History ◽  
Seismic Intensity ◽  
Support Vector ◽  
Stream Velocity ◽  
Analysis Model

A safety analysis process for an immersed bridge is proposed, in which material nonlinear properties, added mass, soil spring, plastic hinge length and property, nonlinear pushover analysis, and time history analysis are included. Outcomes of interest in a bridge analysis, such as displacement ductility, is not an easy task if the authentic model is adopted. The least-squares support-vector machine (LSSVM) is therefore proposed for replacing the analysis model. Water-immersed bridges under varied water depths, scouring depth, and seismic intensity are investigated. The feasibility of using machine learning as a surrogate model is discussed. Results indicate that the trends of fragility curves derived from LSSVM are very similar to those of the authentic model. Therefore, LSSVM is a suitable tool to reduce the computational burden. Analyses also show that stream velocity and pier length are two important factors for the safety of an immersed bridge, and the immersed effect should not be ignored if pier length is long.

Seismic Fragility Analysis of Mid-Story Isolation and Reduction Structures Based Two Directions

2013 ◽  
Vol 739 ◽  
pp. 309-313 ◽  
Author(s):  
Pei Ju Chang
Keyword(s):  
Fragility Curves ◽  
Response Spectrum ◽  
Time History ◽  
Longitudinal Axis ◽  
Seismic Intensity ◽  
Seismic Fragility ◽  
Infill Wall ◽  
Intensity Index ◽  
History Analysis ◽  
Masonry Infill Wall

This study focus on derivation of such fragility curves using classic mid-story isolation and reduction structures (MIRS) in China metropolis. This study focus on derivation of such fragility curves using conventional industrial frames with masonry infill wall. A set of stochastic earthquake waves compatible with the response spectrum of China seismic code selected to represent the variability in ground motion. Dynamic inelastic time history analysis was used to analyze the random sample of structures. MIRS seismic capability of longitudinal and transversal orientation is different. Stochastic damage scatter diagrams based different seismic intensity index are obtained. Seismic fragility of longitudinal axis (Y axis) is larger than transversal axis (X axis) of frames under major earthquake obviously.

scholarly journals A Machine Learning Framework for Assessing Seismic Hazard Safety of Reinforced Concrete Buildings

2020 ◽  
Vol 10 (20) ◽  
pp. 7153 ◽  
Author(s):  
Ehsan Harirchian ◽  
Vandana Kumari ◽  
Kirti Jadhav ◽  
Rohan Raj Das ◽  
Shahla Rasulzade ◽  
...  
Keyword(s):  
Machine Learning ◽  
Input Data ◽  
Safety Evaluation ◽  
Support Vector ◽  
Optimum Number ◽  
Damage Classification ◽  
Damage Prediction ◽  
Rapid Visual Screening ◽  
Damage Data ◽  
Screening Platform

Although averting a seismic disturbance and its physical, social, and economic disruption is practically impossible, using the advancements in computational science and numerical modeling shall equip humanity to predict its severity, understand the outcomes, and equip for post-disaster management. Many buildings exist amidst the developed metropolitan areas, which are senile and still in service. These buildings were also designed before establishing national seismic codes or without the introduction of construction regulations. In that case, risk reduction is significant for developing alternatives and designing suitable models to enhance the existing structure’s performance. Such models will be able to classify risks and casualties related to possible earthquakes through emergency preparation. Thus, it is crucial to recognize structures that are susceptible to earthquake vibrations and need to be prioritized for retrofitting. However, each building’s behavior under seismic actions cannot be studied through performing structural analysis, as it might be unrealistic because of the rigorous computations, long period, and substantial expenditure. Therefore, it calls for a simple, reliable, and accurate process known as Rapid Visual Screening (RVS), which serves as a primary screening platform, including an optimum number of seismic parameters and predetermined performance damage conditions for structures. In this study, the damage classification technique was studied, and the efficacy of the Machine Learning (ML) method in damage prediction via a Support Vector Machine (SVM) model was explored. The ML model is trained and tested separately on damage data from four different earthquakes, namely Ecuador, Haiti, Nepal, and South Korea. Each dataset consists of varying numbers of input data and eight performance modifiers. Based on the study and the results, the ML model using SVM classifies the given input data into the belonging classes and accomplishes the performance on hazard safety evaluation of buildings.

scholarly journals Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3340 ◽  
Author(s):  
Ehsan Harirchian ◽  
Tom Lahmer ◽  
Vandana Kumari ◽  
Kirti Jadhav
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Risk Mitigation ◽  
Safety Evaluation ◽  
Supervised Machine Learning ◽  
Computational Time ◽  
Economic Losses ◽  
Support Vector ◽  
Optimum Number ◽  
Damage Classification

The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning.

EFFECT OF SITE AMPLIFICATION ON SEISMIC SAFETY EVALUATION OF FLYOVER PIER

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Mohammad Raihan Mukhlis ◽  
Md. Abdur Rahman Bhuiyan
Keyword(s):  
Failure Modes ◽  
Pushover Analysis ◽  
Safety Evaluation ◽  
Time History ◽  
Site Amplification ◽  
Shear Capacity ◽  
Main Concern ◽  
Seismic Safety ◽  
Displacement Ductility ◽  
Seismic Safety Evaluation

Bangladesh is a developing country in which a lot of multi-span simply/continuous supported flyovers are being constructed in its major cities. Being situated in a seismically active region, seismic safety evaluation of flyovers is essential for seismic risk reduction. Effects of site amplification on seismic safety evaluation of flyover piers are the main concern of this study. In this regard, failure mode, lateral strength and displacement ductility of piers of a typical multi-span simply supported flyover have been evaluated by Japan Road Association (JRA) recommended guidelines, with and without considering site amplification. Ultimate flexural strengths of piers have been computed using the pushover analysis results. Shear capacity of piers have been calculated using the guidelines of JRA. Lateral strengths have been determined depending on the failure modes of the piers. Displacement ductility of piers has been computed using yield and ultimate displacements of the piers obtained from the pushover analysis results. Selected earthquake time history is used in seismic safety evaluation of the flyover piers. Finally, the ductility design method is used to conduct the seismic safety evaluation of the piers with and without considering site amplification. From the numerical results, it has been revealed that the effects of site amplification on seismic safety evaluation of bridge structures should be carefully taken into account.

A Drift Pushover Analysis Procedure for Estimating the Seismic Demands of Buildings

2014 ◽  
Vol 30 (4) ◽  
pp. 1601-1618 ◽  
Author(s):  
Arash Sahraei ◽  
Farhad Behnamfar
Keyword(s):  
Lateral Displacement ◽  
Plastic Hinge ◽  
Pushover Analysis ◽  
Time History ◽  
Relative Displacement ◽  
Analysis Procedure ◽  
New Method ◽  
Nonlinear Time History Analysis ◽  
Displacement Based ◽  
Nonlinear Time History

Relative displacement is a parameter that has a very high correlation with damage. The objective of this article is to develop an analysis procedure founded on the displacement-based seismic design methodology. Generalized interstory drift spectrum is applied as an essential tool in this new method called drift pushover analysis. In order to evaluate the behavior of structures, three demand parameters—lateral displacement, story shear, and plastic hinge rotation—are computed with conventional pushover analysis (CPA), modal pushover analysis (MPA), and drift pushover analysis (DPA), and are compared with those of the nonlinear time history analysis (NTA). It is observed that the new method, DPA, predicts the peak response measures more precisely and with less effort than the other nonlinear pushover procedures investigated in this study.

scholarly journals Fragility Curve Analysis of Medium Cable Stayed Bridge

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Masrilayanti Masrilayanti ◽  
Ade Prayoga Nasution ◽  
Ruddy Kurniawan ◽  
Jafril Tanjung ◽  
Sarmayenti Sarmayenti
Keyword(s):  
Fragility Curves ◽  
Pushover Analysis ◽  
Time History ◽  
Fragility Curve ◽  
Seismic Load ◽  
Fragility Function ◽  
Cable Stayed Bridge ◽  
Damage States ◽  
Probabilistic Values ◽  
Nonlinear Time History

Abstract The seismic performance of a bridge can be shown by analyzing the vulnerability of the structure in resisting an earthquake motion and then developing into fragility curves. This study presents a convenient method to establish the fragility curve for the cable-stayed bridge. For this purpose, three spans cable-stayed bridge is assessed using a series of seismic loads in different intensities to ensure that the structure was experiencing damage in several conditions. The fragility curve was obtained by analyzing the structure using Nonlinear Time History (NTHA) and Pushover Analysis. The ground motions of the earthquake were subjected to the bridge in different intensities, which were scaled from the initial ground motion. Hereafter, the structure’s ductilities were developed into the fragility curves as the responses of the bridge. HAZUS standard is used for classifying the damages of the bridge, which are grouped into; slight, moderate, extensive, and complete due to the seismic load. The values of the damage states were generated to the fragility curves using the probabilistic values of the damage states. To ensure the validity of the data statistically, Kolmogorov-Smirnov test was conducted to the fragility function. The result revealed that the fragility curve was qualified as the lognormal distribution.

scholarly journals Probabilistic pushover analysis of reinforced concrete frame structures using dropout neural network

2021 ◽  
Vol 15 (1) ◽  
pp. 30-40
Author(s):  
Dang Viet Hung ◽  
Nguyen Truong Thang ◽  
Pham Xuan Dat
Keyword(s):  
Machine Learning ◽  
Reinforced Concrete ◽  
Lateral Displacement ◽  
Plastic Hinge ◽  
Pushover Analysis ◽  
Training Data ◽  
Data Driven ◽  
Frame Structures ◽  
Nonlinear Phenomena ◽  
Base Shear

When taking into consideration nonlinear phenomena such as material plasticity, plastic hinge, and P-Delta effect, the pushover analysis can provide more realistic structures’ nonlinear responses. However, this method is not widely used in practice as it is more complex and requires more expertise than elastic approaches. On the other hand, the data-driven method emerges as an increasingly appealing alternative since it requires only input parameters, then directly yields results in conditions that enough training data are provided, as well as an appropriate machine learning model is devised. Thus, this study develops a probabilistic data-driven approach using the Multiple Layer Perceptron network coupled with the Dropout mechanism to perform the pushover analysis of reinforced concrete (RC) frame structures, predicting base shear, lateral displacement, as well as their relationship between the two formers. Moreover, corresponding confidence intervals of predicted values are also available owing to the probabilistic nature of the method, thus helping engineers design conservative solutions. Keywords: pushover analysis; reinforced concrete; structure; probabilistic analysis; machine learning; dropout mechanism; OpenSees.

Stochastic Vibration-Based Fragility Analysis of Middle-Story RC Frames

2011 ◽  
Vol 204-210 ◽  
pp. 1235-1238
Author(s):  
Jian Zhu ◽  
Ping Tan
Keyword(s):  
Fragility Curves ◽  
Response Spectrum ◽  
Time History ◽  
Seismic Intensity ◽  
Western China ◽  
Fragility Analysis ◽  
Design Practice ◽  
Rc Frames ◽  
History Analysis ◽  
Set Up

A lot of reinforced concrete (RC) frames have being collapsed during Si Chuan Earthquake (SCE) in western China May 12th.2008, at the same time others have being injured on several levers. In recent years how to evaluate reliability and fragility of the buildings and search reasonable design practice of seismic strengthening of these buildings is urgent mission. One goal of fragility analysis is set up relation between vulnerability and seismic intensity. A set of stochastic earthquake waves compatible with the response spectrum of China seismic code selected to represent the variability in ground motion. Dynamic inelastic time history analysis was used to analyze the random sample of structures. In the end structural weak position also be pointed as valuable consultation for diagnose these buildings and fragility curves of typical middle-storey RC frames of China was obtained finally.

Development of Fragility Curves for Multi-Span RC Bridges using Generalized Pushover Analysis

2019 ◽  
Author(s):  
Camilo Perdomo ◽  
Ricardo Monteiro ◽  
Halûk Sucuoğlu
Keyword(s):  
Seismic Vulnerability ◽  
Fragility Curves ◽  
Pushover Analysis ◽  
Time History ◽  
Nonlinear Time History Analysis ◽  
Engineering Practice ◽  
Nonlinear Analyses ◽  
History Analysis ◽  
Nonlinear Time History ◽  
Existing Bridges

<p>Over the past few decades, fragility curves became a powerful tool for the seismic vulnerability assessment of structures. There are several available analytical procedures for calculating fragility curves, using both static and dynamic nonlinear analyses. In this study, a nonlinear static method, based on Generalized Pushover Analysis (GPA), is implemented for the development of analytical fragility curves of reinforced concrete (RC) bridges. The relative accuracy of the GPA algorithm, when applied to a large number of existing bridges, is evaluated through the comparison with the results from Nonlinear Time History Analysis (NTHA). Results indicate that GPA provides a good estimation of the fragility curves with respect to NTHA. The added computational demand of the GPA algorithm in terms of the number of analyses pays off in terms of accuracy while keeping the simplicity of a non-adaptive conventional pushover algorithm, which is desirable in engineering practice.</p>

Response of irregular lightly reinforced concrete frame structures in low seismic zones

2016 ◽  
Vol 20 (4) ◽  
pp. 519-533
Author(s):  
Arash Rezavandi ◽  
Chung C Fu
Keyword(s):  
Reinforced Concrete ◽  
Plastic Hinge ◽  
Time History ◽  
Seismic Intensity ◽  
Time History Analysis ◽  
Nonlinear Time History Analysis ◽  
Reinforced Concrete Frame ◽  
Seismic Zones ◽  
History Analysis ◽  
Lightly Reinforced Concrete

This article evaluates the performance of lightly reinforced concrete frames in low seismic zones. The frames under evaluation contain vertical and/or plane irregularities and are designed for gravity loads only. Nonlinear time history analysis using scaled ground motions and pushover procedure as a supplemental method is performed in this study. With the adoption of plastic hinge method, damage levels are addressed according to FEMA 356 definitions. The pivot model is considered for hysteresis behavior. The damage stage and number of formed hinges are classified for the beams and columns. A comparison between models demonstrates while the first story height may suffer minor to moderate damage levels even under low seismic intensity, the severity of damage to the asymmetric plan models can be noticeable. The pushover method results are close to that of time history analysis only for the vertical irregular frames without plane irregularity.

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