Search Results (34)

A significant challenge in probabilistic seismic demand analysis lies in selecting appropriate intensity measures and investigating their relationships with demand parameters to ensure accurate seismic fragility predictions. A single ground motion intensity measure is insufficient to capture the complex characteristics of ground motion, leading researchers to focus on compound intensity measures. It is essential to investigate the selection of ground motion features and the number of features included in the construction of compound intensity measures, as these measures cannot comprise an unlimited set of ground motion features. This study focused on machine learning feature selection methods to select ground motion features for compound intensity measures, utilizing mutual information for feature selection. Considering the symmetry and asymmetry requirements of this process, optimized features were selected. Based on the selected features, the compound ground motion intensity measure was constructed to evaluate structural seismic fragility. The compound ground motion intensity measure was evaluated against scalar intensity measure in terms of correlation, efficiency, practicality, proficiency, and sufficiency. A comprehensive comparative analysis demonstrates the applicability of the compound intensity measure. The study’s findings support fragility analysis and performance evaluation using compound intensity measures. The corresponding results can be applied in the risk analysis aspect of performance-based earthquake engineering. Full article

Seismic risk assessment is pivotal for ensuring the reliability of prefabricated subway stations, where selecting optimal intensity measures (IMs) critically enhances probabilistic seismic demand models and fragility analysis. While peak ground acceleration (PGA) is widely adopted for above-ground structures, its suitability for underground systems remains debated due to distinct dynamic behaviors. This study identifies the most appropriate IMs for soft soil-embedded prefabricated subway stations at varying depths through nonlinear finite element modeling and develops corresponding fragility curves. A soil–structure interaction model was developed to systematically compare seismic responses of shallow-buried, medium-buried, and deep-buried stations under diverse intensities. Incremental dynamic analysis was employed to construct probabilistic demand models, while candidate IMs (PGA, PGV, and v_rms) were evaluated using a multi-criteria framework assessing correlation, efficiency, practicality, and proficiency. The results demonstrate that burial depth significantly influences IM selection: PGA performs optimally for shallow depths, peak ground velocity (PGV) excels for medium depths, and root mean square velocity (v_rms) proves most effective for deep-buried stations. Based on these optimized IMs, seismic fragility curves were generated, quantifying damage probability characteristics across burial conditions. The study provides a transferable IM selection methodology, advancing seismic risk assessment accuracy for prefabricated underground infrastructure. Through a systematic investigation of the correlation between IM applicability and burial depth, coupled with the development of fragility relationships, this study establishes a robust technical framework for enhancing the seismic performance of subway stations, and provides valuable insights for seismic risk assessment methodologies in underground infrastructure systems. Full article

The multi-strip method is often used to establish a demand model for fragility analysis. Using the multi-strip method to scale the ground motion record may cause uncertainty and bias in structural response calculation and fragility assessment. It is necessary to analyze the effect of differences in the amplitude scaling range in different strips on structural seismic response calculation and seismic fragility assessment. In this paper, the multi-strip method was used to analyze the seismic demand bias based on four multi-story reinforced concrete frame structures subjected to eight ground motion record sets. The bias, variance, and coefficient of variation in different strips in each group of ground motion records were obtained. The effect of different strips on the demand bias was investigated by analysis of variance (ANOVA). Uncertainty quantification of structural demand and fragility curves was carried out using the bootstrap sampling method. The results for structures in different ground motion record sets verify that the differences between the demand bias for different strips by amplitude scaling are statistically insignificant for a 95% confidence level. These findings will contribute to the use of scaling methods for ground motion record sets in a probabilistic seismic demand assessment, allowing for a more reliable prediction of structural seismic fragility. Full article

The increasing demand in structural engineering now extends beyond collapse prevention to encompass business continuity planning (BCP). In response, energy dissipation devices have garnered significant attention for building response control. Among these, buckling-restrained braces (BRBs) are particularly favored due to their stable hysteretic behavior and well-established design provisions. However, BCP also necessitates the prevention of furniture overturning—an area that remains quantitatively underexplored in the context of buckling-restrained braced frames (BRBFs). Addressing this gap, this research designs BRBFs using various design criteria and performs incremental dynamic analysis (IDA) with artificially generated seismic waves. The results are compared with previously developed fragility curves for furniture overturning under different BRB design conditions. The findings demonstrate that the fragility of furniture overturning can be mitigated by a natural frequency shift, which alters the threshold of critical peak floor acceleration. These results, combined with hazard curves obtained from various locations across Japan, quantify the mean annual frequency of furniture overturning. The study reveals that increased floor acceleration in stiffer BRBFs can lead to a 3.8-fold higher risk of furniture overturning compared to frames without BRBs. This heightened risk also arises from the greater hazards at shorter natural periods due to stricter response reduction demands. The probabilistic risk analysis, which integrates fragility and hazard assessments, provides deeper insights into the evaluation of BCP. Full article

Assessing the fragility of intake towers using a single damage index does not allow for accurate evaluation of the potential for structural damage under seismic conditions. In this study, based on the probabilistic seismic demand analysis method, the effects of ground motion intensity on maximum displacement, local damage index, and global damage index are considered, and the seismic fragility of an intake tower structure is analyzed. First, 10 natural ground motion records were selected from the ground motion database (PEER) and 2 artificial seismic waves were synthesized. These seismic waves were amplitude-modulated for incremental dynamic analysis (IDA). The trends of the IDA curves were analyzed to divide the performance levels of the intake tower structure. Furthermore, a two-dimensional fragility curve for the intake tower structure was plotted in this study. The maximum displacement in the direction of parallel flow and the damage index were taken into account in the two-dimensional fragility curve. The results show that, under the designed seismic acceleration, the two-dimensional fragility curve for the intake tower structure was lower than the one-dimensional curve. This indicates that the seismic design based on the one-dimensional performance index was unstable. This provides a theoretical reference for seismic optimization design and the strengthening of intake towers. Therefore, it is recommended to use multidimensional fragility analysis to study the seismic performance of intake tower structures in seismic design. Full article

Based on the incremental dynamic analysis (IDA) method, this paper conducts seismic fragility analysis of a CFST plane frame, a CFST spatial frame under 1D (one-dimensional) ground motions, and a CFST spatial frame under 2D (two-dimensional) ground motions, with different attacking angles. Firstly, nine-story, three-span CFST frame structures (including the plane frame and spatial frame) were modeled in OpenSees, based on the accurate simulation of the hysteresis performance of the test CFST frames. Then, twenty-five groups of ground motions were employed to analyze the seismic response. Lastly, the IDA curve clusters, probabilistic demand models, and seismic fragility curves of frame structures were researched, respectively. The analytical results showed that the exceeding probability of the spatial frame under 2D ground motions was successively greater than that under 1D ground motions, and greater than the plane frame, and the maximum difference at each performance level was up to 6% and 16%, respectively. The fragility analysis result of the spatial frame was sensitive to the attacking angle of ground motion, and the exceeding probability of the 135°, 150°, and 165° fragility curves was larger than that of the 0° (original attacking angle) fragility curve at each performance level. The research results provide a reference for seismic fragility analysis of CFST frame structures employing the IDA method. Full article

Machine learning (ML) approaches, widely used in civil engineering, have the potential to reduce computing costs and enhance predictive capabilities. However, many ML methods have yet to be applied to develop models that accurately analyze the nonlinear dynamic response of cross-fault hydraulic tunnels (CFHTs). To predict CFHT models and fragility curves effectively, we identify the most effective ML techniques and improve prediction capacity and accuracy by initially creating an integrated multivariate earthquake intensity measure (IM) from nine univariate earthquake IMs using principal component analysis. Structural reactions are then performed using incremental dynamic analysis by a multimedium-coupled interaction system. Four techniques are used to test ML–principal component analysis (PCA) feasibility. Meanwhile, mathematical statistical parameters are compared to standard probabilistic seismic demand models of expected and computed values using ML-PCA. Eventually, multiple stripe analysis–maximum likelihood estimation (MSA-MLE) is applied to assess the seismic performance of CFHTs. This study highlights that the Gaussian process regression and integrated IM can improve reliable probability and reduce uncertainties in evaluating the structural response. Thorough numerical analysis, using the suggested methodology, one can efficiently assess the seismic fragilities of the tunnel by the predicted model. ML-PCA techniques can be viewed as an alternate strategy for seismic design and CFHT performance enhancement in real-world engineering. Full article

For medium- and small-span bridges, the weight of the superstructure in steel–concrete composite girder bridges is lighter than that of a reinforced concrete girder bridge. However, it is still uncertain whether steel–concrete composite girder bridges exhibit superior seismic performance compared to reinforced concrete girder bridges. This study quantitatively compared the seismic performance of the two types of bridges. Using the theory of probabilistic seismic demand analysis, the seismic vulnerability curves of bridges were derived. To conduct seismic demand analysis for probabilistic analysis on the OpenSEES platform, bridge samples were generated using the Latin hypercube stratified sampling method, which considers the uncertainties associated with the two types of bridges. The vulnerability curves of the piers, bearings, abutments, and the system of the two bridges were established using probabilistic analysis of the time history analyses. The results showed that the seismic vulnerabilities of components and the overall system of the steel–concrete composite girder bridge were both lower than those of the reinforced concrete girder bridge. When the peak ground acceleration (PGA) of the ground motion was 0.3 g, the moderate and serious damage probabilities of the piers in the steel–concrete composite bridge were only 54.61% and 60.89%, respectively, of those of the reinforced concrete bridge. Consequently, replacing the upper reinforced concrete girders with steel–concrete composite girders can significantly improve the seismic performance of a large number of existing bridges. Full article

In this paper, a comprehensive probabilistic framework is proposed and adopted to perform seismic reliability and risk analysis of existing link slab (LS) bridges, representing a widely diffused structural typology within the infrastructural networks of many countries worldwide. Unlike classic risk analysis methods, innovative fragility functions are used in this work to retrieve more specific and detailed information on the possible failure modes, without limiting the analysis to the global failure conditions but also considering several intermediate damage scenarios (including one or more damage mechanisms), and providing insights on the numerosity of elements involved within a given damage scenario. Reliability analyses are performed on a set of LS bridges with different geometries (total lengths and pier heights) designed according to the Italian codes enforced in the 1970s. Accurate numerical models are developed in OpenSees and Multiple-Stripe nonlinear time–history analyses are carried out to build proper demand models, from which fragility functions are determined according to two limit states: damage onset and near-collapse. Mean annual rates of exceeding are thus estimated through the convolution between the hazard and the fragility. The results shed light on the main failure mechanisms characterizing this bridge typology, highlighting how different levels of risk (hence safety margins) can be associated with failure scenarios that differ in terms of elements/mechanisms involved and damage extension. Such a higher level of detail in the risk analysis may be useful to better quantify post-earthquake consequences (e.g., costs and losses) and define more tailored retrofit interventions. A comparison of the reliability levels associated with bridges of the same class with different geometries is finally presented. Full article

Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the application of deep learning methods to develop prediction models that efficiently assess the nonlinear dynamic responses of cross-fault hydraulic tunnels (CFHTs) is lacking. Thus, the objective of this study is to construct a rational artificial neural network (ANN) prediction model to generate the mass data and fragility curves of CFHTs. Firstly, an analysis of 1080 complete nonlinear dynamic time histories via incremental dynamic analysis (IDA) is conducted to obtain the mass data of the drift ratio of the CFHT. Then, the hyper-parameters of the ANN model are discussed to determine the optimal parameters based on four examined approaches to improve the prediction capacity and accuracy. Meanwhile, the traditional probabilistic seismic demand models of the predicted values obtained by the ANN model and the numerical results are compared with the statistical parameters. Eventually, the maximum likelihood estimation couping IDA method is applied to assess the seismic safety of CFHTs under different damage states. The results show that two hidden layers, ten neurons, and the ReLU activation function for the ANN model with Bayesian optimization can improve the reliability and decrease the uncertainty in evaluating the structural performance. Moreover, the amplitude of the seismology features can be used as the neurons to build the input layers of the ANN model. It is found through vulnerability analysis that the traditional seismic fragility analysis method may overestimate the earthquake resistance capacity of CFHTs compared with maximum likelihood estimation. In practical engineering, ANN methods can be regarded as an alternative approach for the seismic design and performance improvement of CFHTs. Full article

This paper investigates the seismic damage behavior of steel pipe pile wharves after pitting corrosion. The seismic intensity is treated as random, and a probabilistic strength model for randomly pitting corroded steel is utilized to assess the seismic response of a typical steel pipe pile wharf. By analyzing the internal force response of each pile and the deformation response of the deck and soil slope, the process of seismic failure in steel pipe pile wharves with different pitting corrosion ratios is investigated. The results demonstrate that pitting corrosion amplifies the internal force within the steel pipe piles, leading to more severe seismic damage. Additionally, probabilistic seismic demand functions are established for the most vulnerable row of piles affected by random pitting corrosion, and the seismic fragility of the pipe pile wharves considering different pitting corrosion ratios is evaluated. These findings provide valuable insights for the design and strengthening of steel pipe pile wharves. Full article

The aim of this paper is to present a performance-based method to estimate uniform risk spectra (URS) for the seismic design and assessment of structures. These spectra, computed with the proposed methodology, provide the lateral capacity (in terms of spectral acceleration) that should be given to a structure, characterized by a reference single degree of freedom system, to achieve a predetermined exceedance rate of economic loss. This procedure involves the seismic hazard assessment necessary to define a seismic design level consistent with the accepted loss value, using a large enough number of synthetic seismic records of several magnitudes, which were obtained by means of an improved empirical Green function method. The statistics of the expected losses of a reference single degree of freedom system are obtained using Monte Carlo simulation, considering the seismic demand and the lateral strength of the structure as random variables. The method is divided into two main stages: (1) definition of the seismic hazard at the site of interest and (2) the probabilistic analysis of the seismic performance in terms of an economical loss ratio of nonlinear SDOF. To illustrate the proposed methodology and, subsequently, to validate it, a URS was computed for a site located in the Mexico City lake-bed zone, and its use in the design of three reinforced concrete frames is shown. The results show that the proposed spectra provide a sufficient approximation between the seismic risk level considered in the seismic design and that of the designed structure. It is concluded that the proposed procedure is a significant improvement over others considered in the literature and a useful research tool for the further development of risk-based earthquake engineering. Full article

The effect of an intensity measure’s (IM’s) sufficiency property on the probabilistic assessment of reinforced concrete (RC) structures due to floor-to-floor structural pounding conditions is examined. In the first part of this investigation, efficiency and sufficiency properties of 23 scalar IMs are verified. Then, the magnitude M_w and the distance R_rup are examined as elements in a vector with an efficient scalar IM to evaluate whether they have any significant effect on the structural response. Subsequently, probabilistic seismic demand models (PSDMs) are developed using linear regression analyses based on a scalar IM and a vector-valued IM. Fragility curves are developed based on these PSDMs, and the influence of M_w and R_rup on the evaluation of the minimum required separation gap distance d_g,min due to the pounding effect is examined. More than two hundred nonlinear time history analyses are performed based on the Cloud Analysis method. Seismic displacement demands that control of the global state of the structure, as well as the probability of structural pounding, are examined. The results of this research indicate that once M_w or R_rup is increased, fragility curves are shifted to greater values of IM, and the probability of the exceedance of a certain performance level is reduced. Also, the predictive power of R_rup seems to be greater than the one of M_w. On the other hand, it is revealed that M_w and R_rup induce variabilities in the demand solutions for adequate separation gap distance between the adjacent structures. Therefore, variation in M_w or R_rup may lead, in some cases, to unacceptable evaluations of the pounding effect in the capacity levels of structures. Full article

In order to address the difficulty in determining the seismic damage probability of continuous girder bridges under construction, the seismic vulnerability analysis method of the construction state is proposed in this study. Firstly, taking a long-span prestressed concrete composite box girder bridge with corrugated steel webs (OSW) as an example, the finite element models (FEMs) of dynamic calculation in different phases of cantilever construction are simulated by OpenSEES. Secondly, by selecting reasonable seismic waves and seismic intensity measures, the non-linear time-history analysis is carried out, followed by the demand parameters and damage indexes suitable for the construction state proposed. Finally, the probabilistic seismic demand model (PSDA) of the continuous box girder bridge during the construction stage is constructed by using the “cloud method”, and the seismic vulnerability curves of the piers and temporary bearings are established to evaluate the seismic performance during the construction stage. The results indicate that the damage probability of piers and temporary bearings increases with the progress of construction. The initial formation of the cantilever structure and the sudden change in the size of the construction segmental girder correspond to a high probability of damage, and seismic protection measures should be strengthened during this construction state. Moreover, significantly higher damage probability of the components under construction compared to the completed bridge after it is built. Full article

Curved bridges are commonly used for logistics and emergencies in urban areas such as highway interchange bridges. These types of bridges have complicated dynamic behaviors and also are vulnerable to earthquakes, so their functionality is a critical parameter for decision makers. For this purpose, this study aims to evaluate the bridge seismic resilience under the effects of changes in deck radius (50, 100, 150 m, and infinity), pier height irregularity (Regular and Irregular), and incident seismic wave angle (0°, 45°, and 90°) under short- and long-period records. In the first step, fragility curves are calculated based on the incremental dynamic analysis and probabilistic seismic demand models. Finally, seismic resilience curves/surfaces are constructed and their interpolated values of the log-normal distribution function presented for assessing system resilience. It is found that when long-period records are applied in one given direction, the angle of incidence has the most significant effect on seismic resilience, and bridges are most vulnerable when the angle of incidence tends to 0°. The effect of deck radius on seismic resilience became more remarkable as the angle of incidence increased. Additionally, results indicate that the bridge vulnerability in long-period records is more significant than that under short-period records. Full article