scholarly journals Fragility Analysis of a Self-Anchored Suspension Bridge Based on Structural Health Monitoring Data

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Yuyao Cheng ◽  
Jian Zhang ◽  
Jiajia Wu
Keyword(s):  
Health Monitoring ◽  
Hazard Analysis ◽  
Fragility Curves ◽  
Ground Motions ◽  
Suspension Bridge ◽  
Monitoring Data ◽  
Fragility Analysis ◽  
Demand Model ◽  
Earthquake Scenarios ◽  
Ground Motion Selection

This paper presents an improved fragility analysis methodology to estimate structural vulnerability for probabilistic seismic risk assessment. Three main features distinguish this study from previous efforts. Firstly, the updated fragility curves generated are based on experimental measurements and possess higher accuracy than those produced using design information only. The updated fragility curves take into consideration both the geometry and material properties, as well as long-term health monitoring data, to reflect the current state of the structure appropriately. Secondly, to avoid arbitrariness when selecting ground motions, probabilistic seismic hazard analysis (PSHA) is adopted to provide suggestions for ground motion selection. By considering the uncertainty of the location and intensity of future earthquakes, the PSHA deaggregation result can help to determine the most probable earthquake scenarios for the specific site. Thus, the suggested ground motions are more realistic, and the seismic demand model is much closer to the actual results. Thirdly, this study focuses on the seismic performance evaluation of a typical self-anchored suspension bridge using the form of fragility curves, which has seldom been studied in the literature. The results show that bearing is the most vulnerable part of a self-anchored suspension bridge, while failure probabilities of concrete towers are relatively lower.

scholarly journals Seismic Fragility Analysis of Mid-Story Isolation Buildings

2021 ◽  
Keyword(s):  
Fragility Curves ◽  
Ground Motions ◽  
Near Field ◽  
Fragility Analysis ◽  
Seismic Fragility ◽  
Plastic State ◽  
Far Field ◽  
Analysis Method ◽  
Isolation System ◽  
Isolation Layer

Abstract. Seismic fragility analysis is essential for seismic risk assessment of structures. This study focuses on the damage probability assessment of the mid-story isolation buildings with different locations of the isolation system. To this end, the performance-based fragility analysis method of the mid-story isolation system is proposed, adopting the maximum story drifts of structures above and below the isolation layer and displacement of the isolation layer as performance indicators. Then, the entire process of the mid-story isolation system, from the initial elastic state to the elastic-plastic state, then to the limit state, is simulated on the basis of the incremental dynamic analysis method. Seismic fragility curves are obtained for mid-story isolation buildings with different locations of the isolation layer, each with fragility curves for near-field and far-field ground motions, respectively. The results indicate that the seismic fragility probability subjected to the near-field ground motions is much greater than those subjected to the far-field ground motions. In addition, with the increase of the location of the isolation layer, the dominant components for the failure of mid-story isolated structures change from superstructure and isolation system to substructure and isolation system.

scholarly journals Fragility Analyses of Bridge Structures Using the Logarithmic Piecewise Function-Based Probabilistic Seismic Demand Model

2021 ◽  
Vol 13 (14) ◽  
pp. 7814
Author(s):  
Yinghao Zhao ◽  
Hesong Hu ◽  
Lunhua Bai ◽  
Mengxiong Tang ◽  
Hang Chen ◽  
...  
Keyword(s):  
Fragility Curves ◽  
Statistical Significance ◽  
Seismic Intensity ◽  
Fragility Analysis ◽  
Seismic Demand ◽  
Demand Model ◽  
Intensity Measure ◽  
System Fragility ◽  
Probabilistic Seismic Demand ◽  
Seismic Demand Model

Seismic fragility analysis is an efficient method to evaluate the structural failure probability during earthquake events. Among the existing fragility analysis methods, the probabilistic seismic demand model (PSDM) and the joint probabilistic seismic demand model (JPSDM) are generally used to compute the component and system fragility, respectively. However, the statistical significance behind the parameters related to the current PSDM and JPSDM are not comparable. Aside from that, when calculating the system fragility, the Monte Carlo sampling (MCS) method is time-consuming. To solve the two flaws, in this paper, the logarithm piecewise functions were used to generate the PSDM and the JPSDM, and the MCS was replaced by the univariate conditioning approximation (UCA) method. The concepts and application procedures of the proposed fragility analysis methods were elaborated first. Then, the UCA method was illustrated in detail. Finally, fragility curves of a steel arch truss case study bridge were generated by the proposed method. The research results indicate the following: (1) the proposed methods unify the data sources and statistical significance of the parameters used in the PSDM and the JPSDM; (2) the logarithmic piecewise function-based PSDM sensitively reflects the changing trend of the component’s demand with the fluctuation of the seismic intensity measure; (3) under transverse seismic waves, major injuries happen on the side bearings of the bridge, while slight damage may occur on each pier, and as the seismic intensity measure increases, the side bearings are more likely to be damaged; (4) for the severe damage and the absolute damage of the studied bridge, the system fragility curves are closer to the upper failure bounds; and (5) compared with the MSC method, the accuracy of the UCA method can be guaranteed with less calculation time.

Ground Motion Selection in the Near-Fault Region considering Directivity-Induced Pulse Effects

2019 ◽  
Vol 35 (2) ◽  
pp. 759-786 ◽  
Author(s):  
Karim Tarbali ◽  
Brendon A. Bradley ◽  
Jack W. Baker
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Ground Motions ◽  
Seismic Hazard Analysis ◽  
Response Analysis ◽  
Seismic Demand ◽  
Ground Motion Selection ◽  
Near Fault ◽  
Fault Region

This paper focuses on the selection of ground motions for seismic response analysis in the near-fault region, where directivity effects are significant. An approach is presented to consider forward directivity velocity pulse effects in seismic hazard analysis without separate hazard calculations for ‘pulse-like’ and ‘non-pulse-like’ ground motions, resulting in a single target hazard (at the site of interest) for ground motion selection. The ability of ground motion selection methods to appropriately select records that exhibit pulse-like ground motions in the near-fault region is then examined. Applications for scenario and probabilistic seismic hazard analysis cases are examined through the computation of conditional seismic demand distributions and the seismic demand hazard. It is shown that ground motion selection based on an appropriate set of intensity measures (IMs) will lead to ground motion ensembles with an appropriate representation of the directivity-included target hazard in terms of IMs, which are themselves affected by directivity pulse effects. This alleviates the need to specify the proportion of pulse-like motions and their pulse periods a priori as strict criteria for ground motion selection.

scholarly journals Fragility Analysis of Gantry Crane Subjected to Near-Field Ground Motions

2020 ◽  
Vol 10 (12) ◽  
pp. 4219
Author(s):  
Qihui Peng ◽  
Wenming Cheng ◽  
Hongyu Jia ◽  
Peng Guo
Keyword(s):  
Failure Probability ◽  
Structural Damage ◽  
Fragility Curves ◽  
Damage Control ◽  
Ground Motions ◽  
Near Field ◽  
Vertical Component ◽  
Gantry Crane ◽  
Fe Model ◽  
Demand Model

A gantry crane located in a near-field earthquake-prone area is selected in this paper as an example, and the nonlinear finite element (FE) model is used considering the material nonlinearity including plastic hinges and the second order (P − Δ ) effect with a comprehensive consideration of the components including sill beams, support beams, legs, and trolley girders. The local displacement ratio (LDR) and deflection ratio (DR) are proposed as demand measures (DMs) of the gantry crane, which are utilized to construct a probabilistic seismic demand model (PSDM). Then, the capacity limit states for the gantry crane are defined in this study by performing pushover analysis (POA), known as serviceability, damage control, and collapse prevention, respectively. Moreover, the operating capacity of the crane during an earthquake is further investigated and quantified by operating seismic peak ground acceleration, which is defined as the maximum acceleration when the failure probability is 50%. Finally, the fragility curves and the failure probability of the gantry crane are derived by the above definitions, all of which are pioneering in the seismic design of gantry cranes subjected to near-field ground motions. Some major conclusions are drawn that the horizontal component of an earthquake has a more notable effect on the structural damage of the gantry crane compared to the vertical component, and incremental dynamic analysis can take seismic uncertainty into account and quantify the deformation of gantry crane in more detail.

scholarly journals Representative ground-motion ensembles for several major earthquake scenarios in New Zealand

2014 ◽  
Vol 47 (4) ◽  
pp. 231-252 ◽  
Author(s):  
Karim Tarbali ◽  
Brendon A. Bradley
Keyword(s):  
New Zealand ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Ground Motions ◽  
Conditional Intensity ◽  
Intensity Measure ◽  
Earthquake Scenarios ◽  
Major Earthquake ◽  
The Mean ◽  
Representative Ground

In this paper, representative ground motion ensembles for several major earthquake scenarios in New Zealand are developed. Cases considered include representative ground motions for the occurrence of Alpine, Hope and Porters Pass earthquakes in Christchurch city, and the occurrence of Wellington, Wairarapa and Ohariu fault ruptures in Wellington city. For each considered scenario rupture, ensembles of 20 and 7 ground motions are selected using the generalized conditional intensity measure (GCIM) approach, ensuring that the ground motion ensembles represent both the mean and distribution of ground motion intensity which such scenarios could impose. These scenario-based ground motion sets can be used to complement ground motions which are often selected in conjunction with probabilistic seismic hazard analysis, in order to understand the performance of structures for the question “what if this fault ruptures?”

scholarly journals Vector-Valued Fragility Analysis Using PGA and PGV Simultaneously as Ground-Motion Intensity Measures

2010 ◽  
Vol 5 (4) ◽  
pp. 407-416
Author(s):  
Sei’ichiro Fukushima ◽  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Hazard Analysis ◽  
Fragility Curves ◽  
Fragility Analysis ◽  
Peak Ground Velocity ◽  
Seismic Risk Analysis ◽  
Ground Acceleration ◽  
Intensity Measures ◽  
Vector Valued

Seismic risk analysis usually expresses ground-motion intensity using a single index such as peak ground acceleration (PGA), spectral acceleration for a specified period, or peak ground velocity (PGV). Limiting the number of indices, however, adds greater uncertainty when estimating annual failure probability given by convolving seismic hazard and fragility curves. This is because information other than ground-motion intensity is missing. Author proposed seismic hazard analysis using PGA and PGV simultaneously as groundmotion input measures. After analyzing the correlation coefficient between PGA and PGV using K-NET and KiK-net databases, probabilistic seismic hazard for seven sites in Kanto district in Japan was evaluated. In this study, seismic fragility analysis using PGA and PGV is conducted followed by advantage of vector-valued fragility analysis.

Delay Prediction of Aircrafts Based on Health Monitoring Data

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
B. A. Dattaram ◽  
N. Madhusudanan
Keyword(s):  
Machine Learning ◽  
Health Monitoring ◽  
Monitoring Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Flight Delay ◽  
Delay Prediction ◽  
The Relationship

Flight delay is a major issue faced by airline companies. Delay in the aircraft take off can lead to penalty and extra payment to airport authorities leading to revenue loss. The causes for delays can be weather, traffic queues or component issues. In this paper, we focus on the problem of delays due to component issues in the aircraft. In particular, this paper explores the analysis of aircraft delays based on health monitoring data from the aircraft. This paper analyzes and establishes the relationship between health monitoring data and the delay of the aircrafts using exploratory analytics, stochastic approaches and machine learning techniques.

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