Seismic Reliability Assessment Research Articles

Numerical Studies on Seismic Response of Structural Systems Using a Response-Spectra Based Intensity Measure

This paper proposes a novel intensity measure (IM) based on the geometric mean of acceleration response spectral ordinates to assess the probabilistic performance of structures subjected to seismic loading. Instead of relying solely on the fundamental period, the proposed IM is evaluated across a fixed period range for all structural systems, which allows consideration of higher mode effects and period changes due to nonlinearity. The proposed seismic IM is evaluated using two established indices sufficiency and efficiency. Sufficiency quantifies the independence of an engineering demand parameter at a specific intensity level with ground motion characteristics such as seismic magnitude [Formula: see text] and distance from site to fault plane [Formula: see text]. It is calculated by linear regression analysis, or using gradient-based relative sufficiency measures. On the other hand, efficiency is measured as dispersion across ground motions at a given intensity level for any physical response quantity. It helps to reduce computational demand for failure probability assessment by considering a smaller number of records compared to an inefficient IM for similar confidence levels. The effectiveness of the proposed IM along with 10 other IMs is demonstrated on single degree of freedom systems with various fundamental periods by performing nonlinear time history analysis using a far-field ground motion record set. The study is also extended to five degree of freedom lumped mass stick models, 2D models (4-, 8-, and 12-story archetype steel frames), and 3D reinforced concrete shear wall building model. The results indicate that the proposed IM limits dispersion to within 10% for long-time period structures, and demonstrates improved sufficiency across different structural systems. For example, gradient of the proposed IM with respect to magnitude [Formula: see text] and site-to-source distance [Formula: see text] for a 12-story steel frame is reduced by 42.9% and 94%, respectively, compared to spectral acceleration at fundamental time period. Potential application of this research lies in efficiently conducting seismic reliability assessment and design for structural systems.

Impact of epistemic and aleatory uncertainties on the seismic reliability assessment of existing structures

Seismic reliability assessment of structural systems is commonly done via the convolution of seismic hazard and structural fragility to get resulting failure rate, and later compute seismic reliability index. Within this frame, many uncertainty sources have to be handled by the risk analyst, who is asked to make some reasonable assumptions in order to carry out his/her seismic safety quantification. However, slight changes in the definition of input parameters and/or the use of different empirical or analytical models can strongly impact the final reliability outcomes. The present work aims therefore to investigate the impact of different sources of uncertainties by comparing together key parameters to be defined in both hazard and fragility analysis, considering a case-study represented by an existing multi-span steel–concrete composite bridge to better understand which of these sources is more impacting on the final estimates. Results show how uncertainties associated to hazard and fragility are as a whole of the same order of magnitude and thus how both uncertainty sources have to be properly considered. In addition, a detailed sensitivity analysis was carried out for ranking the uncertainty sources in relation to their impact on the overall reliability index dispersion. Results showed that the choice of a suitable Ground Motion Prediction Equation (GMPE) model, the non-linear modelling technique and the number of the accelerometric records adopted for the structural analysis seem to be the factors that mostly contribute to the seismic reliability index variability, representing the uncertainty related to these parameters the majority of the overall reliability index dispersion.

Structural Assessment of Historical Stone Bridges with the Finite Element Method under Dynamic Effects of Arch Shape: The Antik Iscehisar Bridge

In this study, the effect of the variation of the arch form in thickness and height on the bridge was investigated as a numerical analysis. For this purpose, the historic Antik Iscehisar Bridge located in the Iscehisar district of Afyonkarahisar in the Aegean Region was elected as a numerical application. The bridge was subjected to its own weight and moving load as a static analysis. For dynamic analysis, the effect of 10 different fault movements with historical character obtained from The Pacific Earthquake Engineering Research Center (PEER) on the bridge was investigated. The areas of principal stress and deformation resulting from the applied analyses were determined. Contour diagrams, tables, and charts were given in a comparative manner based on the results of the analysis applied to the bridge. At the end of the analysis, it was observed that the displacements decreased as the arch thickness increased under its own weight in the bridge. In addition, under the influence of live loads and earthquakes, it was observed that the displacements decrease as the arch thickness increases. A seismic reliability assessment was made using the performance criteria provided in this study. If the security level is below what it should be, reinforcement applications can be designed. Accordingly, future maintenance and monitoring planning can be made.

Seismic reliability assessment of existing RC chimneys in Romania

Seismic reliability assessment of existing RC chimneys in Romania

An updated spectral representation method coupled with generalized probability density evolution method in assessing the seismic reliability of tunnels

The dimension reduction spectral representation method (SRM) is generally employed with the generalized probability density evolution method (GPDEM) to implement the seismic reliability assessment, where a noticeable non-zero value in the mean value of ground motion would be obtained. This study proposed an updated spectral representation method (U-SRM) to improve the simulated mean values, which coupled with GPDEM to investigate the nonlinear responses and seismic reliability assessment of a tunnel. Results obtained from the numerical study were compared between SRM and U-SRM. The horizontal displacement at the tunnel crown is more conservative using U-SRM than SRM in the reliability assessment of tunnels. Results indicated that U-SRM coupled with GPDEM is an efficient method to analyze the nonlinear responses and the seismic reliability of tunnels, and could serve as a reference for tunnel design in seismically active zones.

Seismic collaborative reliability evaluation of slopes using subset simulation via support vector machine

The computational effort places a limit on the low failure probability estimate of geotechnical engineering exposed to seismic stimulation. In this research, a novel method is proposed for the seismic reliability assessment of complicated geotechnical engineering slope structures with low failure probability. First, an improved subset simulation (SS) approach is provided for the low-failure events with high-dimensional indirect cross-correlation parameters. This method expands the usefulness of SS by using the support vector machine (SVM) method. Then, in order to reduce computing effort, the seismic collaborative reliability evaluation (SCRE) framework for seismic reliability assessment based on SS and the response conditioning method (RCM) is constructed. Finally, the accuracy of SCRE is verified by comparison with the standard Monte Carlo simulation based on 2D slope. The results show that SCRE's computational effort is approximately 2% that of Monte Carlo simulation. Meanwhile, SVM does a good job of preserving the significant indirect cross-correlation that occurs in the subspace samples that were split by SS. The time-saving benefit of SCRE is further enhanced when the aforementioned situations are expanded to 3D, and the findings demonstrate that the failure probability and sliding surface of the slope are different in 2D and 3D.

Improved estimation on seismic behavior of RC column members: A probabilistic method considering dynamic effect and structural parameter uncertainties

To acquire more accurate and reliable seismic behavior of reinforced concrete (RC) column members, a probabilistic estimation method for considering both the impacts of loading rate-sensitivity of materials and randomness in structural parameters is proposed in this paper. The previously developed dynamic modified coefficient (DMC) models are extended to include different sources of uncertainties by using the Monte-Carlo (MC) simulation with the Latin hypercube sampling (LHS) technique. The influences of variations in key structural parameters on the DMC estimates are investigated through sensitivity analysis. Moreover, the accuracy and effectiveness of the proposed method are verified by qualitatively and quantitatively comparing the probabilistic predictions with the available dynamic loading test results. Furthermore, the computation procedure of the dynamic skeleton model cluster (DSMC) is put forward and typical cases of skeleton line are also discussed. The impacts of randomness in structural parameters and skeleton line number on the DSMC predicted results are presented. This research shows that more reasonable estimate on the seismic behavior of RC column members can be obtained by employing the proposed probabilistic method, which can be further applied into the seismic reliability assessment of RC frame structures in future research works.

Seismic reliability assessment of bridge networks considering travel time and connectivity reliabilities

AbstractTransportation networks play a vital role in transporting goods and casualties immediately after an earthquake. Thus, identifying the most reliable and shortest path is critical for ensuring efficient emergency response and recovery after an earthquake; consequently, it is important to compute the reliability of transportation networks by simultaneously accounting for connectivity and travel time. In this regard, this paper proposes a novel seismic reliability indicator termed as the travel time and connectivity reliability (TTCR), which combines the travel time and connectivity reliabilities. Additionally, a novel framework for computing the TTCR of transportation networks is proposed. The proposed methodology is applied to a bridge network in California. Furthermore, a retrofit plan based on the TTCR is proposed to help risk managers make appropriate and timely decisions for reducing the seismic risk of transportation networks.

Machine Learning–Based Seismic Reliability Assessment of Bridge Networks

Transportation networks are critical components of lifeline systems. They can experience disruptions due to seismic hazards that could lead to severe emergency response and recovery problems. Finding an efficient and effective method to evaluate the seismic reliability of bridge networks is crucial for risk managers. This study proposes a method that can compute the seismic reliability of bridge networks using machine learning techniques. The proposed method is computationally less expensive than existing methods and can be implemented easily in emergency risk management systems. Moreover, it includes information on ranking bridges and prioritizing retrofit plans.

Post-earthquake assessment model for highway bridge networks considering traffic congestion due to earthquake-induced bridge damage

Reliable post-earthquake modeling of a highway bridge network (HBN) is essential for seismic reliability, risk, and resilience assessment. This requires the accurate modeling of the effects of earthquake-induced bridge damage on traffic congestion. Accordingly, a novel post-earthquake HBN simulation method, wherein each bridge is defined as a link instead of as a part of a highway link, was proposed herein. In addition, the feasibility of implementing machine learning methods for fragility modeling of regular highway bridges was explored as well. The developed artificial neural network (ANN) fragility model can accurately capture the seismic damage of highway bridges at trivial computation costs compared to time-history analysis methods. Finally, the proposed methods were combined with probabilistic seismic hazard analysis, traffic demand assessment and distribution, etc., and applied to an HBN that connects several cities in Arizona and New Mexico to validate their efficacy and scalability. The results revealed that compared to the proposed HBN simulation method, conventional HBN modeling methods may underestimate the seismic resilience of HBNs.

Time-dependent seismic reliability of isolated bridges considering ageing deterioration of lead rubber bearings

Seismic isolation is an effective technology for mitigating the seismic damage to bridges. However, rupture and large cracks of laminated rubber bearings, including lead rubber bearings, have occurred at several bridges during recent earthquakes. Preventing the rupture of seismic isolators by considering uncertainties and variations in the mechanical properties due to ageing deterioration can improve the ductility capacity of the bridge systems when subjected to extreme ground motions. This study presents a time-dependent seismic reliability assessment methodology for isolated bridges that considers long-term ageing deterioration of lead rubber bearings. The effects of capacity hierarchy between the isolator and the column, as well as the ageing deterioration of isolators on the seismic reliability and the predominant ultimate failure mode were clarified. It was found that the effect of ageing deterioration of lead rubber bearings on seismic reliability is relatively smaller than that of the difference in seismic hazards at different sites. Furthermore, the seismic safety of isolated bridges can be significantly improved by setting an adequate capacity hierarchy between the isolator and the column, as it can prevent the rupture of the isolator under extreme ground motions considering the ageing deterioration of the isolator.

A sparse data-driven stochastic damage model for seismic reliability assessment of reinforced concrete structures

It is important to study the seismic reliability of concrete structures based on real measured data of the material properties. The data of material properties collected in practice is usually sparse in spatial distribution. When assessing the seismic performance of the structures based on data, the statistical inference is usually firstly conducted to evaluate the material properties of the whole structure from the sparse data, and the nonlinear seismic simulation of the structures can be performed. The coupling effect of uncertainty and nonlinearity has not been explained properly. In the present study, the Bayesian compressive sensing – Karhunen Loève expansion (BCS-KL) method is combined with the stochastic damage model (SDM) to build a sparse data-driven stochastic damage model. The model deals with nonlinear seismic stochastic analysis of concrete structures based on sparse data, in which the BCS-KL is applied for uncertainty quantification of the statistical inference and the SDM is used as a physical constitutive model of concrete. The simulation is performed with sophisticated modeling using stochastic finite element method, and the physical synthesis method is applied to assess the seismic reliability of the structure. Finally, a shake table test is conducted to verify the proposed simulation framework.

Seismic reliability assessment of a non-seismic reinforced concrete framed structure designed according to ABNT NBR 6118:2014

abstract: Due to the paucity of studies regarding the seismic assessment of buildings in Brazil, this study aims to present and discuss a seismic reliability assessment of a reinforced concrete framed structure designed according to the Brazilian standard ABNT NBR 6118:2014 without the consideration of the seismic design requirements of ABNT NBR 15421:2006. Herein, fragility functions are generated through probabilistic seismic demand analysis, and integrated with hazard curves for Northeastern Brazil to generate regional failure probability maps for three limit-states: Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP). The results indicated that the building performance is adequate for IO; however, for LS and CP, an unacceptable performance is observed.

A Practice-Oriented Procedure for Seismic Reliability Assessment of RC Structures Affected by Carbonation-Induced Degradation

Existing reinforced concrete (RC) buildings in Europe have generally been designed without proper consideration of seismic actions and capacity design principles, and thus they tend to be vulnerable to earthquakes. Moreover, since a significant proportion of the aforementioned buildings were developed during the 1950s and 1960s, they are currently close to the end of their service life. Therefore, seismic assessment of existing RC building is a major issue in structural engineering and construction management, and the related seismic analyses should take into account the effect of material ageing and degradation. This paper proposes a practice-oriented procedure for quantifying seismic reliability, taking into account the main effects of carbonation-induced degradation phenomena. It summarizes the main aspects of the most up-to-date models for the seismic degradation of concrete and RC members and shows how nonlinear static (pushover) analyses can be utilized (in lieu of the most time-consuming non-linear time history analyses) in quantifying seismic reliability with respect to the performance levels of relevance in seismic engineering. A relevant case study is finally considered with the aim to showing how some parameters, such as exposure class and cover thickness, affect the resulting seismic reliability of existing RC buildings.

Variation of reliability-based seismic analysis of an electrical cabinet in different NPP location for Korean Peninsula

The area of this study will cover the location-wise seismic response variation of an electrical cabinet in nuclear power point (NPP) based on classical reliability analysis. The location-based seismic ground motion (GM) selection is carried out with the help of probabilistic seismic hazard analysis using PSHRisk-tool, where the variation of reliability analysis can be understood from the relation between the reliability index and intensity measure. Two different approaches such as the first-order second moment method (FOSM) and Monte Carlo Simulation (MCS) are helped to evaluate and compare the reliability assessment of the cabinet. The cabinet is modeled with material uncertainty utilizing Steel01 as the material model and the fiber section modeling approach is considered to characterize the section's nonlinear reaction behavior. To verify the modal frequency, this study compares the FEM result with recorded data using Least-Squares Complex Exponential (LSCE) method from the impact hammer test. In spite of a few investigations, the main novelty of this study is to introduce the reader to check and compare the seismic reliability assessment variation in different seismic locations and for different earthquake levels. Alongside, the betterment can be found by comparing the result between two considered reliability estimation methods.

Seismic reliability assessment of base-isolated bridges in Quebec

The aim of this work is to estimate the seismic reliability of a simple typical two-span lifeline base-isolated bridge designed to behave essentially elastic or as per the Canadian Highway Bridge Design Code, for seven localities in Quebec. Two limit states are considered for possible failure due to unacceptable damage: flexure at the pier-base and displacement within the seismic isolation systems (SIS). The main problem random variables considered and modeled are seismic hazard, temperature, pier base dimensions, and material mechanical properties. The Monte Carlo method was used to evaluate the probability of failure and the reliability of each limit state. Preliminary results reveal that notwithstanding the large temperature and seismic hazard variabilities between the seven sites in Quebec, the global bridge reliability indices are almost uniform, approximately 3.45 ± 0.02. Furthermore, the security factor (i.e., 1.25) on the SIS displacement capacity results in reliability indices for SIS displacement that are not levelled with the flexural reliability indices and requires further examination and consideration.

Multi‐scale seismic reliability assessment of networks by centrality‐based selective recursive decomposition algorithm

AbstractAs lifeline networks such as transportation or electricity networks in modern societies are intricately interlocked, a small number of components damaged by natural or man‐made disasters can have a great impact on network performance. For this reason, it is essential to assure the capability of rapid assessment of network reliability to make prompt follow‐up measures. Despite the rapid development of various algorithms and computing power, the capability is still limited due to computational cost for analyzing the connectivity of a single origin and destination (O/D) node pair in large‐scale networks. Therefore, this paper introduces a new algorithm utilizing network centrality, termed “centrality‐based selective recursive decomposition algorithm” (CS‐RDA). By preferentially decomposing the node which is most likely to belong to the min‐cut identified based on the betweenness centrality, the convergence of the bounds on the O/D connectivity can be expedited significantly. This paper also introduces a new multi‐scale analysis approach termed “edge‐betweenness algorithm.” The algorithm groups components such that its modularity is maximized, by sequentially removing edges that have the highest level of betweenness centrality. As a result, the reliability of large‐scale networks can be accurately evaluated in a short time owing to the reduced complexity of the simplified network. The proposed methods are successfully demonstrated by a hypothetical network example, the highway bridge networks in San Jose and San Diego in California, USA.

Seismic Reliability Assessment of Water Supply Systems Considering Critical Paths

In general, studies of the seismic reliability of water supply systems through hydraulic analysis models fall into three categories: (1) model development through hydraulic analysis, (2) establishment of post-earthquake recovery strategies, and (3) robust design in preparation for earthquakes and the development of improvement techniques. In this study, with regard to the design of earthquake resistance techniques, which is the third category, a seismic reliability assessment was conducted that considers the water supply system, and a plan to maximize the seismic reliability was determined as a result of the analysis. The degree of improvement in seismic reliability was quantified for water supply systems with various sources and water supply routes via modeling of seismic protection for each water supply route. Korea’s A industrial water system, which has a history of large-scale earthquakes, was divided into nine paths, and the reliability of the water supply system and the cost of earthquake resistance were calculated and compared between the different paths. A water supply path consists of a combination of pipes of various sizes and lengths, and the amount of water to be supplied differs between paths. Quantifying the effect of the water supply system and the seismic reliability of a route is intended to provide a basic methodology for identifying the critical path that should be the target of seismic protection when money and resources available for construction are limited. This methodology was used to confirm that the reliability of the water supply, the demand for water, and the cost of earthquake resistance should be considered together to establish an earthquake resistance strategy for the critical path in a water supply system. We expect that the results of this study will be used to establish detailed strategies for preliminary reinforcement against earthquake damage based on critical paths rather than all or individual pipelines in a water supply network.

Shannon Information Entropy for Seismic Reliability Assessment of Structures Using Self-Organizing Neural Networks

Localization and quantification of structural damages and find a failure probability is the key important in reliability assessment of structures. In this study, a Self-Organizing Neural Network (SONN) with Shannon Information Entropy simulation is used to reduce the computational effort required for reliability analysis and damage detection. To this end, one demonstrative structure is modeled and then several damage scenarios are defined. These scenarios are considered as training datasets for establishing a Self-Organizing Neural Network model. In this regard, the relation between structural responses (input) and structural stiffness (output) is established using Self-Organizing Neural Network models. The established SONN is more economical and achieves reasonable accuracy in detection of structural damages under ground motion. Furthermore, in order to assess the reliability of structure, five random variables are considered. Namely, columns’ area of first, second and third floor, elasticity modulus and gravity loads. The SONN is trained by Shannon Information Entropy simulation technique. Finally, the trained neural network specifies the failure probability of purposed structure. Although MCS can predict the failure probability for a given structure, the SONN model helps simulation techniques to receive an acceptable accuracy and reduce computational effort.

Efficient seismic reliability analysis of non-linear structures under non-stationary ground motions

The seismic reliability assessment (SRA) of complex nonlinear structures which possess structural and seismic uncertainty is an open challenge for academics and practical engineers. This paper proposes an improved maximum entropy method (IMEM) for the SRA of nonlinear structures. Non-stationary ground motions are first generated via a random-function-based spectral representation method. High-dimensional random variables in the ground motion simulation are then converted into two basic random variables as the high-dimensional probability space is reduced to a low-dimensional probability space. A two-step IMEM constrained with fractional moments is deployed to estimate the extreme value distribution (EVD) of the nonlinear structural dynamic system. An improved Latin hypercube sampling approach is also established to calculate the fractional moments of the complex nonlinear dynamic system. The first-passage probability of the structural nonlinear seismic response can be computed conveniently by a numerical integration using the EVD. Two numerical examples, 1) a three-story nonlinear shear frame structure and 2) a real-world high-pier continuous rigid frame bridge model, both of which exhibit strong nonlinearity under seismic action, are analyzed to show that the proposed method is efficient and accurate.