Seismic Fragility Research Articles

Seismic response prediction and fragility assessment of high-speed railway bridges using machine learning technology

Nowadays, reliable and accurate prediction of the seismic response of high-speed railway bridges and then rapid assessment of their seismic fragility is crucial for the risk assessment of regional high-speed railway bridges. However, the traditional seismic fragility assessment based on nonlinear time history analysis (NTHA) has a characteristic of high computational cost. Therefore, this study explores different machine learning (ML) methods to develop a reliable rapid seismic fragility assessment framework for high-speed railway bridges. Taking a typical high-speed railway continuous (HRC) bridge as a case, a large number of numerical model and ground motion pairs considering various structural and ground motion features are established. Then, the two types of models including six popular ML methods are investigated, in which Lasso regression, artificial neural network (ANN) and support vector machine (SVM) are single models, and random forest (RF), extreme gradient boosting (XGBoost) and light gradient boosting machine (Light GBM) are ensemble models. A database containing a large number of seismic responses of different components is established through the NTHA to develop prediction models, and the optimal hyperparameters of different ML models are determined by ten-fold cross-validation and grid search. The study results show that the ensemble model can reliably predict the seismic response of the HRC bridge compared with the single model, and the XGBoost model is recommended for seismic fragility assessment. On this basis, the optimal ML model obtained by the performance evaluation is applied to develop seismic fragility curves of the HRC bridge, which are compared with the fragility curves based on NTHA. The results indicate that the XGBoost model can effectively replace the traditional NTHA for rapid seismic fragility assessment of the HRC bridge, which can achieve a sufficient assessment level while saving calculation costs.

Impact of choice of fragility approaches on seismic risk quantification of nuclear power plants

Development and evaluation of seismic fragility of structures and components is crucial in seismic probabilistic risk assessment of nuclear power plants. Simulation-based fragility approaches are a prevailing trend in the literature, while industry-recommended methodologies rely heavily on engineering judgment and deterministic analysis. In this paper, four critical components are selected, modeled and analyzed as a case study. Their fragilities are evaluated using both state-of-the-art fragility methods and code-recommended methods with approximate models. The impact of the choice of fragility approaches on the fragilities of the components and conditional core damage probability of the plant are assessed. The findings reveal that the recommended approaches employed with approximate models have limitations in estimating the median capacity of complex equipment. While there is a notable variance in the treatment of uncertainty among fragility approaches, its influence on core damage probability remains limited unless the component is the primary contributor to core damage.

Influence of Incidence Direction of Seismic Wave on the Probabilistic Seismic Fragility Assessment of Bridges

Influence of Incidence Direction of Seismic Wave on the Probabilistic Seismic Fragility Assessment of Bridges

Fragility Assessment of Single Concrete Pier and Pile-Soil Interaction Impact under Near and Far-Fault Earthquakes

Civil engineers face a significant challenge in assessing seismic vulnerability due to the complex of soil-pile-structureinteraction system. This research aims to evaluate the seismic vulnerability of individual piers under various seismicground motions. Factors like sand type, pile diameter, pier height, and mass placed were investigated for their impacton the seismic fragility of concrete piers. Using incremental dynamic analysis with a beam on a nonlinear Winklerfoundation model, the study compared the effects of near and far ground motions. Results from the dynamic analysisand fragility assessment demonstrated the effects of the parameters above-mentioned on the design of fragilitycurves, as well as its relationships to fundamental period of structural system.

Seismic fragility curves for concrete gravity retaining wall

Seismic fragility analysis plays a crucial role in performance-based seismic design (PBSD). The seismic fragility analyses for the concrete gravity retaining walls were performed in this study. Numerical models were developed for two concrete gravity retaining walls using OpenSees, considering two different backfill slope angles (0° and 12°). According to the probabilistic seismic demand models and comprehensive evaluation of intensity measures (IMs), the most suitable IM for concrete gravity retaining walls was determined. An approach for quantifying the limit state performance of concrete gravity retaining walls based on IDA was also proposed, and its reliability was verified based on observed damage during the 2008 Wenchuan earthquake. Seismic fragility curves for concrete gravity retaining walls were established under two backfill slope angles using three IMs. It is demonstrated to what extent a backfill slope angle can influence the damage probability under a specific seismic intensity level.

Seismic fragility analysis of girder bridges under mainshock‐aftershock sequences based on input‐output hidden Markov model

AbstractCurrent seismic design codes for bridge structures do not account for the influence of aftershock sequences, which, to some extent, overestimate the seismic performance for bridges subjected to mainshock‐aftershock (MS‐AS) scenarios. To address the great need for ground motion sequences tailored to specific research sites for fragility analysis, this study proposes a method for generating artificial MS‐AS ground motion sequences based on the evolutional bimodal Kanai–Tajimi model and the Epidemic–Type Aftershock Sequence model. We establish a framework for MS‐AS fragility analysis using an input–output Hidden Markov Model (IOHMM), where the damage states (DS) of bridge piers are considered unobservable and are inferred statistically through damage indices in an unsupervised manner. Model parameters are trained using intensity measure (IM) sequences and damage index (DI) sequences. Fragility curves for both the mainshock and state‐dependent aftershocks considering multiple aftershocks are formulated based on the initial state probability and state transition probabilities of the proposed IOHMM. The fragility analysis results reveal that as the initial seismic damage level increases, the probability of aftershocks causing higher damage levels in the structure also increases, highlighting the significant impact of aftershocks on structural damage increments. Furthermore, we extend the proposed model to a bivariate seismic intensity measure and develop fragility surfaces. The proposed framework provides a novel approach and insights for tackling seismic fragility under multiple aftershocks.

Seismic fragility analysis of prefabricated self-centering frame structure

The seismic fragility of prefabricated self-centering frame structures composed of self-centering joints is investigated for reference in their seismic design and application. In this paper, a new design of the concrete-filled double steel tubular column-RC beam joint with self-centering capability and friction energy dissipation was proposed. The joint was modeled with ABAQUS and OpenSees, and hysteretic simulation was carried out to verify the reasonableness and accuracy of the OpenSees phenomenological model. Based on the OpenSees platform, a one-bay, six-story prefabricated self-centering frame structure was modeled, and seismic fragility analysis based on the incremental dynamic analysis was performed, in which seismic fragility curves were obtained according to the probabilistic demand analysis model. The results indicated a high coincidence in hysteretic curves between the ABAQUS model and the OpenSees phenomenological model, which showed favorable self-centering and energy dissipation performance of the joint proposed in this paper. Based on FEMA 356, three kinds of performance points were defined, i.e., “Immediate Occupancy” (IO), “Life Safety” (LS), and “Collapse Prevention” (CP). The prefabricated self-centering frame in this paper tended to exceed the IO, where the stiffness and strength of the structure was susceptible to damage. However, the failure probability at the LS performance level remained low, with only 5% and 37% failure probability under moderate and rare earthquakes, respectively. The structure achieved a collapse margin ratio of 4.28, which indicated a high collapse resistance in it.

Probabilistic-based seismic fragility analysis of a ground-bridge structure system considering site liquefaction

Bridge structures are vulnerable to severe damage in previous earthquake events. Meanwhile, the earthquake-induced site liquefaction can significantly affect the seismic performance of bridge structures. Seismic fragility analysis is considered as an effective approach for evaluating seismic performance of bridge structural systems exposed to seismic hazards. This study aims to assess seismic fragility of a ground-bridge structure system considering site liquefaction. Based on the probabilistic seismic demand model (PSDM) and joint probabilistic seismic demand model (JPSDM), a holistic framework for fragility analysis considering site liquefaction is proposed to derive the liquefaction fragility as well as the structural system fragility. The system fragility surfaces obtained by the proposed method can reflect the influence of site properties on the damage exceeding probability of bridge structures. The results demonstrate an increasing probability of undergoing high-level damage states for the whole system containing highly loose sand under strong earthquakes, which may provide significant basis for probabilistic-based aseismic design.

Seismic fragility analysis of RC frame structures based on IDA analysis and machine learning

This study introduces a machine learning-based methodology designed to rapidly predict the seismic responses of reinforced concrete (RC) frame structures. The research focuses on three types of RC frame structures: low-rise, multi-story, and small high-rise buildings. Ground shaking records are selected according to the conditional mean spectrum (CMS). A sample database, constructed via Incremental Dynamic Analysis (IDA), facilitates the prediction of structural responses using ground shaking intensity and structural details as inputs. Concurrently, the study performs a feature importance analysis of the model. Machine learning algorithms, including integrated learning and neural networks, are utilized to predict the seismic responses of the RC frame structures. This methodology also assists in evaluating the seismic fragility of these structures. The results show that the discrepancy between the neural network-based seismic fragility assessments and the IDA results is minimal, indicating a high degree of accuracy in the proposed methodology. Among the characteristic parameters, Average Spectral Acceleration (AvgSa) is identified as the most significant. This methodology serves as a valuable tool for the rapid prediction of seismic responses in RC frame structures, demonstrating substantial practical value.

Seismic fragility of unbraced industrial steel pallet racks

Past Italian earthquakes revealed the high seismic vulnerability of steel pallet racks designed for gravity loads only, which are still the most widespread industrial storage system. This study aims to derive the seismic fragility of these structures to enable more refined estimates of enterprise risk and the definition of effective retrofit solutions. For this purpose, 3D non-linear models of 27 unbraced pallet racks, representative of the Italian context, were analysed in Time-History under 268 bidirectional events, representative of Italian seismicity. Multiple fragility models were then derived, based on various engineering demand parameters and seismic intensity measures, through a cloud approach.

Seismic fragility assessment of an in-service piled bridge abutment subjected to a liquefaction-induced lateral spreading and mitigation strategy

This study assesses the seismic fragility curves of in-service piled bridge abutments on liquefaction-prone soils and evaluates an optimal countermeasure within the vulnerability framework. Seismic fragility curves, accounting for varying ground motion intensities, assess the seismic risk and describe abutment damage through settlement measurements. The ageing abutment performance is estimated by integrating a corrosion model into fragility curves. The impact of different sheet pile positions on the seismic performance of in-service piled bridge abutments is analysed, and the optimal pile position is discussed. The developed fragility curves provide a rapid and effective risk assessment tool for the seismic performance of in-service abutments and guide liquefaction remedial measures.

A maximum entropy-driven support vector classification model for seismic collapse fragility curves estimation of reinforced concrete frame structures

A novel method called maximum entropy-driven support vector classification (MED-SVC) for estimating the seismic collapse fragility curves of reinforced concrete (RC) frames is proposed in this paper. In comparison to kernel-based learning (KBL) methods, a subset selection process based on the maximum Renyi entropy criterion is embedded into the proposed MED-SVC, which can actively select an informative small-scale subset from a large-scale training dataset. The selected subset can accurately reflect the data distribution of the large-scale training dataset, which can be further used to explicitly establish a high-dimensional feature variable. Due to this, the computational complexity of training the proposed MED-SVC model only depends on the dimension of the feature variable, which can effectively solve the problem related to the low efficacy of training the KBL model in the context of a large-scale training dataset. Meanwhile, the proposed MED-SVC model can also reduce the negative effect of randomness in the subset selection process on predictive performance. The robustness, accuracy, and efficiency of the proposed MED-SVC model are thoroughly validated by comparison with random sampling driven SVC (RSD-SVC), KBL methods, random forest, neural network, and naive bayes based on 33,000 damage data of RC frames under earthquakes. Then, the proposed MED-SVC model is applied to estimate the seismic collapse fragility curve of an RC frame structure outside the aforementioned dataset. The results show that the proposed MED-SVC model can generate seismic collapse fragility curves that agree well with those produced by the finite element method (FEM) and performs more robustly than RSD-SVC.

Seismic collapse fragility analysis of steel moment-resisting frames (MRFs) with stiffness and strength deterioration considering the spectral shape of ground motion records

In this paper, the seismic collapse fragility analysis of special steel moment-resisting frame (MRF) buildings with strength and stiffness deterioration is comprehensively evaluated, taking into account the effect of record selection based on the spectral shape parameters. For this purpose, three special steel moment-resisting frame buildings, including 3, 10, and 20-story structures as representatives of low-rise, mid-rise, and high-rise buildings, are investigated. The modified bilinear Ibarra-Medina-Krawinkler (IMK) relation is used for modeling the concentrated plasticity and considering cyclic degradation in beams and columns under earthquake effects in Incremental Dynamic Analysis (IDA). The ground motion records are selected and categorized based on the three parameters, including the epsilon, SaRatio, and Np from a large database consisting of 2000 earthquake records. The records are divided into 7 groups using the epsilon parameter. The SaRatio and Np parameters further divide the records into 5 categories. Additionally, a randomly selected set of 200 earthquake records is used. The effects of hysteretic deterioration and spectral shape parameters of earthquake records on the median IDA curves, collapse fragility curves, and collapse capacity of the steel MRF structures are investigated. In addition to the hysteretic deterioration of steel structural members based on the results of the previous experiments, different values of the deterioration parameter (λ) are used in the nonlinear time history analyses to parametrically study the effect of deterioration on the collapse capacity. Finally, the effect of various parameters on the seismic collapse capacity of the structures in the form of Tornado diagrams is illustrated. The results demonstrate that the randomly selected records yield results close to the records sets with Np = 1, SaRatio = 1, and ε = 0. Furthermore, for a constant deterioration level, increasing epsilon and SaRatio, and decreasing Np lead to an increase in the collapse capacity of the structures. The results also show that the effect of deterioration on the collapse capacity of the buildings, especially the low-rise 3-story building, is significant.

A demand-based three-stage seismic resilience assessment and multi-objective optimization method of community water distribution networks

This paper proposes a three-stage resilience concept that takes into account reliability, vulnerability and recovery resilience of water distribution networks (WDNs) from three phases, namely, pre-disaster preparation (PDP) phase, disaster response (DR) phase and the post-disaster recovery (PDR) phase. Based on the three-stage resilience concept, the initial performance, robustness and multi-objective optimization recovery method are proposed. In PDP phase, based on the reliability concept and the user demand, the initial performance of WDNs is defined, and the method is proposed based on Monte Carlo simulation method combined with seismic fragility of pipelines. In DR phase, a comprehensive quantitative index is proposed to quantify the robustness of WDNs by integrating the structural attributes, topological attributes and operational characteristics of WDNs. Finally, on the basis of the previous two stages, the disaster recovery process of failed pipelines of WDNs is further analyzed, and a multi-objective recovery optimization model of failed pipelines with consideration of users’ demands under the resources constraints is proposed to achieve the resilience enhancement. Through a case, the three-stage resilience assessment optimization enhancement of WDNs is implemented using the improved NSGA-II. The proposed method can be used as a basis for disaster prevention planning and seismic measures.

Seismic fragility and resilience assessment of steel frame with replaceable T-stub connection components

Based on a rational design, steel beam-column joints with replaceable T-stub connection components (SBCJ-RTCCs) possess superior structural fuse performance and seismic resilience performance. To further evaluate the benefits of steel frames using RTCCs in terms of dynamic response, structural and non-structural components damage as well as seismic resilience, a 12-story steel frame equipped with RTCCs (SF-RTCCs) and a steel moment resisting frame (SMRF) were designed and established. On the basis of validating the simulation results with the experimental findings, the nonlinear dynamic analyses and incremental dynamic analysis (IDA) based seismic fragility analysis was performed. The results confirm that the introducing RTCCs into steel frames can significantly mitigate base shear and floor acceleration as well as produce a more uniform inter-story deformation and a more rational yielding mechanism. Meanwhile, the SF-RTCCs can obtain comparable collapse-resistant performance compared to SMRF, and both frames possess sufficient redundancy to resist structural collapse. Thanks to the lower floor acceleration responses, the SF-RTCCSs performs much better than SMRF in controlling non-structural components damage. After DBE excitations, the probabilities of RTCCs can be replaced by 1st class and 2nd class was 17.2 % and 84.9 %, respectively. After MCE excitations, RTCCs has about 29.3 % and 67.2 % probability that it can be replaced by 2nd class and 3rd class, respectively. Considering the lower economic losses, short repair time and fewer casualties, the rating of seismic resilience of SF-RTCCs was enhanced from one-star of SMRF to two-star.

Performance-based assessment of the novel prefabricated CFST bridge pier by numerical approach

This study aims to profoundly investigate the seismic performance and application prospect of the typical continuous beam bridge with the novel prefabricated concrete-filled steel tubular (CFST) piers, with advantages in simple on-site assembly, convenient transportation of prefabricated components, good seismic performance, and low construction cost. According to two prototype continuous beam bridges respectively equipped with low and tall typical reinforced concrete (RC) piers, 7 bridge cases with prefabricated CFST piers featuring various configurations of the tie beams are designed. Three-dimensional numerical models of these bridge cases are established and verified with the previous experimental studies, and numerical simulations of all the bridge cases are conducted to assess and compare the seismic responses and fragility curves. The results show that the bridges with prefabricated CFST piers exhibit a reduction in top pier transversal displacement and maximum curvature along the pier height, which are 43 % and 138 % lower than those of the prototype RC piers, respectively. Besides, the bridges with prefabricated CFST piers generally present lower probabilities of being damaged compared to those of the prototype bridges with RC piers.

Seismic fragility analysis of buried pipelines under Kahramanmaraş ground motions

This paper aims to evaluate the failure probability of buried pipelines under pulse-like ground motions based on the seismic records detected during the Kahramanmaraş earthquake in Turkey. The incremental dynamic analysis (IDA) method was used to evaluate the vulnerability of continuous and segmented steel pipes, taking into account the corrosion effect in alkaline and near-neutral soil environments.The results show that pulse-like ground motion can significantly impact the fragility of buried pipelines, particularly segment steel pipelines. Moreover, the failure probability of buried pipelines tends to increase with service age, although the rate of failure gradually decreases. Empirical models notably underestimate the failure probability of buried pipelines under pulse-like ground motion, so they should be used cautiously in engineering application. These insights contribute to advancing our understanding of the seismic fragility of buried pipelines.

Vine-copula-based multi-dimensional fragility analysis of nuclear power plant under sequential earthquakes

Seismic resilience of critical infrastructure, such as nuclear power plants, is paramount in ensuring nuclear safety. This study presents a comprehensive analysis of the seismic fragility of nuclear power plants under sequential earthquakes, employing the innovative vine-copula theory. The methodology integrates advanced modeling techniques, including layered shell elements and plastic damage softening constitutive modeling, to capture the intricate behavior of nuclear power plants under seismic loading. The seismic sequence is derived from the Wenchuan earthquake data, considering both mainshocks and aftershocks. A set of random seismic peak ground accelerations (PGAs) is generated based on the distribution of giant earthquake PGAs. Utilizing seismic attenuation theory, corresponding random aftershock PGAs are generated. The resulting mainshock-aftershock sequence, modulated within the real seismic sequence, serves as the input for numerical simulations. The vine-copula theory is employed for multi-dimensional fragility analysis, providing a flexible framework to model the complex nonlinear dependencies among structural response parameters. The vine-copula model is applied to fit seismic response data, allowing the construction of fragility surfaces under sequential earthquakes. This approach, rooted in performance-based earthquake engineering (PBEE), enables a more realistic representation of the seismic risk profile. The findings demonstrate that seismic fragility trends for nuclear power plants increase with higher mainshock and aftershock intensity measures (IMs). The impact of aftershocks on the structural performance, often overlooked in traditional studies, is elucidated through the proposed methodology. The study contributes valuable insights into nuclear safety assessments by quantifying the influence of sequential earthquakes on the fragility of nuclear power plants.

A data-driven approach for structural integrity assessment of prestressed concrete containment vessel considering the effect of vertical ground motions

As a final barrier against the release of radioactive material, the prestressed concrete containment vessel (PCCV) plays an important role in the nuclear power plant (NPP). When subjected to seismic loads, its structural performance requires high structural integrity. During an earthquake, the PCCV experiences ground motions (GMs) consisting of horizontal and vertical components in a global coordinate system, and its structural integrity is probably overestimated if only the horizontal component is considered in numerical analyses. In this study, a novel cumulative damage index is first employed to quantitatively assess the structural integrity of PCCVs. Accordingly, the failure probability of structural integrity of the PCCV is investigated by incremental dynamic analysis (IDA) and further seismic fragility analysis considering the effect of vertical ground motions (VGMs). The results show that the effect of VGMs on structural integrity of PCCVs is gradually non-negligible with the increase of vertical intensity. Moreover, an accurate cumulative damage prediction model for the PCCV is established based on eight mainstream machine learning (ML) algorithms, and the necessity of incorporating vertical intensity measures (IMs) in ML models is investigated. The results show that the ML model considering the vertical IMs is superior in capturing the cumulative damage of the PCCVs. The best estimates are obtained by GB with R2 of 0.912, MAE of 1.301, MSE of 6.186, and RMSE value of 2.487. Finally, the SHapley Additive exPlanation (SHAP) method is adopted for interpreting the prediction results of the optimal ML model to assess the effect of each IM and its interactions on the generalization performance of the model. The proposed ML-based cumulative damage prediction model for PCCVs considering the effect of VGMs enables an accurate quantitative assessment on structural integrity of PCCVs. This provides an important reference for evaluating the potential environmental hazard due to the leaking of radioactive materials.

Impact of failure mode uncertainty on seismic fragility and collapse risk of buildings

Impact of failure mode uncertainty on seismic fragility and collapse risk of buildings