Ground Motion Intensity Measures Research Articles

Enabling efficient regional seismic fragility assessment of multi‐component bridge portfolios through Gaussian process regression and active learning

AbstractRegional seismic fragility assessment of bridge portfolios must address the embedded uncertainties and variations stemming from both the earthquake hazard and bridge attributes (e.g., geometry, material, design detail). To achieve bridge‐specific fragility assessment, multivariate probabilistic seismic demand models (PSDM) have recently been developed that use both the ground motion intensity measure and bridge parameters as inputs. However, explicitly utilizing bridge parameters as inputs requires numerous nonlinear response history analyses (NRHAs). In this situation, the associated computational cost increases exponentially for high‐fidelity bridge models with complex component connectivity and sophisticated material constitutive laws. Moreover, it remains unclear how many analyses are sufficient for the response data and the resulting demand model to cover the entire solution space without overfitting. To deal with these issues, this study integrates Gaussian process regression (GPR) and active learning (AL) into a multistep workflow to achieve efficient regional seismic fragility assessment of bridge portfolios. The GPR relaxes the probability distribution assumptions made in typical cloud analysis‐based PSDMs to enable heteroskedastic nonparametric seismic demand modeling. The AL leverages the varying standard deviation to select the least but most representative bridge‐model‐ground‐motion sample pairs to conduct NRHA with much‐improved efficiency. Both independent and correlated multi‐output GPRs are proposed to deal with bridge portfolios with seismic demand correlations among multiple components (column, bearing, shear key, abutment, unseating, and joint seal). Considering a single benchmark highway bridge class in California as the case study, the AL‐GPR framework and the associated component‐level fragility results are investigated in terms of their efficiency, accuracy, and robustness. The fragility results show that 70 AL‐selected samples would enable the GPR to derive bridge‐specific fragility models comparable to the ones using the multiple stripes analysis approach with 1950 ground motions considered for each individual bridge. The AL‐GPR model also successfully captures the physics of how bridge span length, deck area, column slenderness, and steel reinforcement ratio would change the damage state exceedance probabilities of different bridge components. The efficiency of AL stems from the fact that, with the multi‐output independent GPR, a stable and reliable fragility model can be achieved using 50 AL‐selected samples compared to at least 270 randomly chosen samples. The proposed methodology advances the state of the art in enabling more efficient and reliable regional seismic fragility assessment of multi‐component bridge portfolios.

Influence of pulse-like motions and extreme environmental loads on the seismic foundation response of offshore wind turbines on layered liquefiable soils

Monopile foundations are known as the most common foundation solution for offshore wind turbines (OWTs). However, the state of practice for designing monopile foundations in high seismicity areas is still limited. In particular, the impact of soil liquefaction on the seismic soil-foundation-OWT interaction is not yet well understood. In this paper, three-dimensional (3D), fully-coupled, nonlinear finite-element analyses performed in the OpenSees numerical platform were used to evaluate the seismic performance of a series of hypothetical 5 MW OWTs on monopile foundations in layered, liquefiable sites. A suite of earthquake recordings with and without strong velocity pulses (i.e., near fault, pulse-like and ordinary motions, respectively) was used to investigate the impact of ground motion characteristics on the seismic response of the OWT system. Also, the influence of soil-structure interaction and earthquake shaking coupled with extreme environmental loading (i.e., wind and wave loads) on the seismic performance of soil-OWT systems was evaluated. The numerical results showed pile movements induced by extreme climate loading led to a bias in permanent settlement accumulation across the foundation area and accumulation of soil deformations in the proximity of the pile. Ground motion velocity pulses increased the cyclic stress demand in soil and, therefore, the potential for the occurrence of soil liquefaction. A subsequent, limited numerical sensitivity study showed that the foundation rotations of the OWT system were influenced by ground motion characteristics such as polarity and velocity pulses, and the presence of the wind and wave loads. The cumulative absolute velocity (CAV) was identified as the optimum ground motion intensity measure for permanent foundation settlement and tilt as well as for peak transient foundation tilt of the OWT system under extreme environmental loadings. The net outcome of these factors determined the magnitude and orientation of the foundation rotations at the end of shaking. This study highlights the importance of considering the effects of extreme loadings and pulse-like motions in the design and performance of OWT systems.

Updated Predictive Models for Permanent Seismic Displacement of Slopes for Greece and Their Effect on Probabilistic Landslide Hazard Assessment

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope’s yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed.

A comparison of ground motions predicted through one-dimensional site response analyses and three-dimensional wave propagation simulations at regional scales

One-dimensional (1D) site response analysis (SRA), which considers vertically propagating seismic waves from the bedrock to the surface, has been a common technique among geotechnical engineers to examine site-specific ground shaking. However, observations from past earthquakes and analytical studies indicate that idealizations ingrained in 1D SRA may be too severe to capture the ground truth, such as the omissions of spatial variability of soil properties, surface topography, and basin and directivity effects. Physics-based three-dimensional ground motion simulations (GMSs) can incorporate these factors and yield more reliable predictions. In this study, we utilize ground motions from 57 physics-based broadband (from 0 to 8–12 Hz) GMS for a region of Istanbul. A total of 2912 sites with experimentally measured soil profiles that are distributed over the 30 km-by-12.5 km area are also modeled as soil columns and analyzed through 1D SRA. The ground responses from 1D SRA and three-dimensional (3D) GMS are then compared for all 57 earthquake scenarios. These systematic comparisons are then used for examining model features that are correlated with variations in the ratios of various ground motion intensity measures (IMs) and for developing regression-based formulas that can be used for determining simple factors for the considered region to correctly scale (up or down) the site-specific ground motion intensities obtained from 1D SRA, including peak ground acceleration (PGA), peak ground velocity (PGV), and spectral acceleration ( Sa) values.

A damage-oriented ground motion intensity for degrading super high-rise buildings

To explicitly identify the influence of different seismic damages on the correlation between ground motion intensity measure (IM) and demand measure (DM), with a specific concern of super high-rise buildings, a novel damage-integrated spectral acceleration-based combination-type IM is developed for degrading systems. With the integration of seismic damage measure, inelastic displacement spectrum, and design acceleration spectrum, the prototype of the IM is theoretically derived for single-degree-of-freedom systems with a Q-type hysteretic model. The high-mode effect is reflected by an optimal number of lower modes combined with corresponding modal damage. To estimate the optimal number of modes while maximizing the case-independency of DM predictions, the dynamic properties of concerned damaged and undamaged structures are estimated with the collaboration of the modified flexure-shear model (FSM-MS), and further utilized to establish the regression expression with a large number of near-field and far-field ground motions. The performance of the proposed IM is systematically demonstrated via a case study on two super high-rise buildings, along with other selected IMs.

Utility of Stochastic Ground Motion Models in Conditional Spectrum-Based Selection Consistent with the Causal Earthquakes

ABSTRACT Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm’s potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures.

A ground motion intensity measure based on different spectral-shape types to predict nonlinear structural response in steel buildings

The selection of an appropriate intensity measure to assess the seismic performance in steel buildings is an important step to reduce uncertainty in the structural response. Hence, in this study, a scalar ground motion intensity measure able to increase the efficiency in the prediction of nonlinear behavior effects of steel structures subjected to earthquake ground motions named the generalized intensity measure INpg is analyzed. The intensity measure is based on a proxy of the spectral shape Npg, where it can be defined by using different types of spectral shapes, such as those obtained with pseudo-acceleration, velocity, displacement, input energy, inelastic parameters and so on. This work shows the efficiency of the generalized intensity measure named INpg when the spectral parameters of pseudo-acceleration and velocity are used. Therefore, to improve the performance of the analyzed intensity measure, two engineering demand parameters, maximum inter-story drift and horizontal peak floor acceleration, of steel frames with 5, 10, 15 and 20 stories subjected to several narrow-band ground motions are estimated as a function of the spectral acceleration at first mode of vibration of the structure Sa(T1), which is commonly used in earthquake engineering and seismology, and with the two particular cases under study of the recently developed parameter related to the structural response known as INpg. In general, the intensity measure here studied is able to efficiently predict nonlinear structural demands on steel buildings under earthquake ground motions. Further, the analyzed intensity measure must be considered to estimate maximum inter-story drift and horizontal peak floor acceleration demand of multi-story buildings.

Ground-Motion Intensity Measures for the Seismic Response of the Roof-Isolated Large-Span Structure

Ground-motion intensity measures (IMs), which quantify and describe the characteristics of earthquake ground motion, are of utmost importance in the assessment of seismic risk and the design of resilient structures with large spans. The appropriate selection of a ground-motion IM is crucial in establishing a reliable and robust correlation between seismic hazards and structural demands. The current study presents a novel ground-motion IM that incorporates the influence of multiple vibration modes and period elongation resulting from isolation based on the velocity spectrum. A comprehensive study has been conducted to examine the efficiency of 37 different ground-motion IMs on a roof-isolated large-span structure with engineering demand parameters (EDPs), using far-field ground-motion data. The initial examination of the proposed intensity measure involves a planar lumped-mass model. Subsequently, a numerical model of a large-span roof-isolated structure, specifically the Beijing Workers’ Stadium, is constructed and examined. The results suggest that the proposed intensity measure (IM) demonstrates satisfactory adequacy and achieves optimal efficiency when considering three different engineering demand parameters among 37 other ground-motion intensity measures.

Seismic risk assessment of highway bridges in western Canada under crustal, subcrustal, and subduction earthquakes

This study conducts seismic risk assessment of highway bridges in western Canada. The performance-based earthquake engineering (PBEE) framework is enhanced to assess the expected annual repair cost ratio (ARCR) and annual restoration time (ART) of a benchmark bridge class under the region’s three types of earthquakes - shallow crustal earthquakes (CEs), deep subcrustal earthquakes (SCEs), and megathrust Cascadia subduction earthquakes (CSEs). First, event-specific seismic hazard models are considered, whereas event-consistent ground motions are selected for non-linear time history analyses. Compared with those from CEs and SCEs, CSE ground motions feature a much longer duration. This long-duration effect is captured by validating the numerical model of the bridge column against (1) a cyclic pushover test under standard versus long-duration loading protocols and (2) a shaking table test excited by six consecutive ground motions. Besides, the Park and Ang damage index is utilized as the column’s engineering demand parameter (EDP) and updated as a demand-capacity ratio model when reaching four different damage states. A comprehensive list of ground motion intensity measures (IMs) is considered where the spectra acceleration at one second, Sa(1.0), is chosen as the most suitable IM based on its performance in proficiency, efficiency, practicality, and EDP-IM correlation across all three earthquake events. Subsequently, component- and system-level fragility models are derived under each earthquake type using the cloud analysis that convolves the seismic demands with capacity models for multiple bridge components. To further quantify and propagate the epistemic uncertainty associated with the development of probabilistic seismic demand models (PSDMs), the bootstrap resampling technique is utilized to generate numerous seismic demand datasets and develop a stochastic set of seismic fragility curves. Finally, the bootstrapped, event-dependent fragility models are combined with the respective hazard models and probabilistic loss functions to assess the expected ARCR and ART for the benchmark bridge class. This study underscores the significantly higher seismic risk of highway bridges when facing CSEs, followed by CEs and SCEs.

Investigation of Ambient Seismic Noise in Different Region of Georgia

Georgia, like the whole South Caucasus, is a tectonically and structurally complex region. It is one of the active segments of the Alpine-Himalayan belt, therefore it is important to assess the seismic hazard for Georgia. At the regional scale, this assessment is evaluated by applying probabilistic seismic hazard analysis that identifies the annual probability of exceedance of various ground motion levels defined in terms of selected ground motion intensity measures, such as PGA or spectral accelerations (SA) corresponding to various return periods related to possible future earthquake scenarios for a site represented by soil classes A according to EC8, Euro-code 8-EN 1998-1 (1998). At the local scale, seismic hazard assessment is made by analyzing the geological, geomorphological, geotechnical and geophysical characteristics of the site, as it is well established that the incoming seismic motion can change in amplitude, frequency, and duration due the site-specific local characteristics. That is the subject of micro-zonation investigation. Site – specific local characteristics are presented by the following parameters: Dominant frequency, Vs,30 (average shear-wave velocity to a depth of 30 meters) and amplification factor. In this work, we presented results of geophysical survey assessing local site conditions by dominant frequency that allows identification of similar seismic response areas. For this purpose, seismic noise records have been used first time in Georgia.

Ensemble Region-Specific GMMs for Subduction Earthquakes

Abstract This study develops data-driven global and region-specific ground-motion models (GMMs) for subduction earthquakes using a weighted average ensemble model to combine four different nonparametric supervised machine-learning (ML) algorithms, including an artificial neural network, a kernel ridge regressor, a random forest regressor, and a support vector regressor. To achieve this goal, we train individual models using a subset of the Next Generation Attenuation-Subduction (NGA-Sub) data set, including 9559 recordings out of 153 interface and intraslab earthquakes recorded at 3202 different stations. A grid search is used to find each model’s best hyperparameters. Then, we use an equally weighted average ensemble approach to combine these four models. Ensemble modeling is a technique that combines the strengths of multiple ML algorithms to mitigate their weaknesses. The ensemble model considers moment magnitude (M), rupture distance (Rrup), time-averaged shear-wave velocity in the upper 30 m (VS30), and depth to the top of the rupture plane (Ztor), as well as tectonic and region as input parameters, and predicts various median orientation-independent horizontal component ground-motion intensity measures such as peak ground displacement, peak ground velocity, peak ground acceleration, and 5%-damped pseudospectral acceleration values at spectral periods of 0.01–10 s in log scale. Although no functional form is defined, the response spectra and the distance and magnitude scaling trends of the weighted average ensemble model are consistent and comparable with the NGA-Sub GMMs, with slightly lower standard deviations. A mixed effects regression analysis is used to partition the total aleatory variability into between-event, between-station, and event-site-corrected components. The derived global GMMs are applicable to interface earthquakes with M 4.9–9.12, 14≤Rrup≤1000 km, and Ztor≤47 km for sites having VS30values between 95 and 2230 m/s. For intraslab events, the derived global GMMs are applicable to M 4.0–8.0, 28≤Rrup≤1000 km, and 30≤Ztor≤200 km for sites having VS30 values between 95 and 2100 m/s.

Feature selection of ground motion intensity measures for data‐driven surrogate modeling of structures

AbstractIn the probabilistic seismic performance assessment of structures, intensity measures (IMs) represent seismic characteristics and variations. Traditional fragility analysis method based on the assumption of linear regression requires selecting an optimal IM as input variable. By introducing machine learning (ML) techniques, nonparametric fragility analysis theoretically allows for considering all potential IMs as inputs. Nevertheless, to reduce input dimensionality and improve training efficiency, the feature selection of IMs remains imperative. This paper proposes a method to select optimal ground motion IMs for data‐driven surrogate modeling of structures. Specifically, the elastic net algorithm is employed to select the optimal multiple IMs based on the coefficient of determination and regression coefficient, differing from the efficiency and practicality emphasized in the traditional method. Using the optimal multiple IMs as input variables, several ML techniques are employed to construct surrogate models for seismic damage assessment of structures, thereby developing fragility functions, that is, the conditional probability of exceeding a damaged state given seismic intensity. A 3‐span, 6‐storey, reinforced concrete frame is utilized to illustrate the proposed methodology. The predictive performance of all ML models with the optimal multiple IMs outperforms that of the models with the commonly used IM (e.g., peak ground acceleration, PGA) as sole input and all candidate IMs as inputs. Additionally, the surrogate models with the optimal multiple IMs enable a more comprehensive seismic fragility modeling of structures under two or more IMs simultaneously, such as the fragility surface under spectral acceleration at 1.0s (Sa‐1.0s) and velocity spectrum intensity (VSI).

Deformation behavior of rectangular underground structures in liquefiable deposits correlated to ground motion intensity measures

Rectangular underground structures in liquefiable deposits are susceptible to larger deformation during earthquake events. The intensity of earthquake motions, as one of the critical factors that would induce large deformation of underground structures in liquefiable deposits, however, was not received enough recognitions. In this paper, nonlinear dynamic analyses with a probabilistic seismic demand model (PSDM) were conducted for a two-dimensional liquefied soil-structure system, considering a variable thickness of liquefiable layer below the structure. The nonlinear soil behavior was simulated by the nonlinear constitutive model PDMY02. The constitutive parameters of saturated sand were calibrated by a slope model in the centrifuge test from LEAP-2017. Interstory drift ratio (IDR) and uplift ratio (UR) of underground structures were selected as engineering demand parameters (EDP). There were totally 18 recorded earthquake motions and 8 intensity measures (IMs) included for the PSDM. Numerical results show that the uplift behavior of underground structures is more sensitive to the variable thickness of liquefiable deposits compared to the horizontal drift of the structure. According to the performance of correlation, efficiency, practicality and proficiency, the velocity-related IMs are more appropriate than the acceleration-related IMs for the evaluation of seismic deformation behavior of underground structures. The deformation response as well as the adaptability of velocity-related IMs are further discussed and evaluated by considering more general cases including different buried depths and height-width ratios for rectangular underground structures.

Ground-motion effects on liquefaction response

This article evaluates the influence of ground-motion characteristics on the liquefaction response of free-field level-ground deposits. Ground motions imposed on liquefiable soils have historically been characterized by the peak ground acceleration (PGA). In simplified procedures for liquefaction evaluation, the use of the ground surface PGA can be justified by the fact that it is closely related to the peak shear stresses developing in the critical zone for liquefaction manifestation, at shallow depths. In nonlinear dynamic analysis (NDA), however, such simplified characterization of the input motion using the bedrock-level PGA can result in large record-to-record variability in the computed response. Record-to-record variability can be substantially reduced by employing more appropriate ground-motion intensity measures (IMs) for specific aspects of the liquefaction response observed in NDA. Among 25 IMs examined in this study, the Modified Acceleration Spectrum Intensity (MASI), defined as the integral of the acceleration spectrum over periods 0.1–1.5 s, is found to be the most efficient IM for the prediction of the pore-pressure response of liquefiable deposits, whereas Velocity Spectrum Intensity (VSI) ranks among the best-performing IMs for response parameters related to ground deformations. Importantly, such “multiple-period spectrum IMs” are shown to perform consistently better than several highly-regarded time-integrated IMs (e.g. Arias intensity, CAV). Conclusions and recommendations are drawn on the basis of a comprehensive series of NDAs involving 180 input motions and 13 deposit models based on characteristic soil profiles from Christchurch.

Response to discussion of “Seismic damage analysis due to near‐fault multipulse ground motion”

AbstractThe authors thank the discusser for the attention and interests on our previous work, entitled “Seismic damage analysis due to near‐fault multipulse ground motion” (referred to as the original paper/work in the following text). To demonstrate the increased seismic demands required by multipulse ground motions compared to non‐ and single‐pulse ground motions, three cases were illustrated in the original work, including frame structures, a soil slope and a concrete dam. The discusser, Dr M.Amin Hariri‐Ardebili, raised concerns on the seismic response of the dam, together with the optimal intensity measure of ground motions. Specifically, four subissues are involved, including effectiveness of the numerical model of dam, the damage index for dam, the selection strategy for input ground motions, and the ground motion intensity measures. Detailed responses to these issues are provided. In short, the main conclusion in the original paper that the multipulse ground motions potentially cause more severe damage compared to non‐ and single‐pulse ground motions is reliable.

Construction of a Ground-Motion Logic Tree through Host-to-Target Region Adjustments Applied to an Adaptable Ground-Motion Prediction Model: An Addendum

ABSTRACT Boore et al. (2022; hereafter, Bea22) described adjustments to a host-region ground-motion prediction model (GMPM) for use in hazard calculations in a target region, using Chiou and Youngs (2014; hereafter, CY14) as the host-region model. This article contains two modifications to the Bea22 procedures for the host-to-target adjustments, one for the source and one for the anelastic attenuation function. The first modification is to compute logic-tree branches for the source adjustment variable ΔcM given in Bea22 assuming that the host- and target-region stress parameters are uncorrelated, instead of the implicit assumption in Bea22 that they are perfectly correlated. The assumption of uncorrelated stress parameters makes little difference for the example in Bea22 because the standard deviation of the host-region stress parameter is much less than that of the target-region stress parameter. However, this might not be the case in some future applications. The second modification is to the host-to-target anelastic attenuation path adjustment. The adjustment in Bea22 involves a distance-independent change in the γ variable that controls the rate of anelastic attenuation in the CY14 GMPM. This article proposes a method to account for a distance dependence in the adjustment. Such a dependence is needed for short-period ground-motion intensity measures (GMIMs) at distances greater than 100 km, with the importance increasing with distance. For the example in Bea22, the ratio of GMIMs computed with the revised and the previous adjustment to γ is less than about a factor of 1.05 at distances within about 100 km, but it can exceed a factor of 2 at 300 km for short-period GMIMs.

Optimal intensity measures in probabilistic seismic demand model of subway station

Ground motion intensity measure (IM), which is a key link between the seismic hazard analysis and structural seismic response analysis, plays an important role in the performance-based earthquake engineering framework. The objective of this paper is to evaluate the most commonly used IMs with respect to their capability to predict the seismic response of underground subway station structures. Because the properties of the surrounding soil has significant influence on the seismic response of subway station structure, four typical classes of engineering sites ranging from Class I to IV according to Chinese code for seismic design of urban rail transit structures are selected in this study. Dynamic time history analyses considering nonlinear soil-structure interaction (SSI) are performed on four two-dimensional finite element models consisting of two-storey three-span subway stations embedded in four different engineering sites in this paper. Both the near-fault without velocity pulse and far-fault ground motions are used as the seismic inputs for the nonlinear SSI models in this study. Ground motion excitation is converted into equivalent seismic load at the artificial boundary for the discretized finite element model to realize the seismic load input. The applicability of 20 ground motion IMs commonly used in earthquake engineering research are investigated in this study. Statistical regression analysis is carried out for those IMs and engineering demand parameters (EDPs) of the subway station structure measured by maximum inter-storey displacement ratio obtained from nonlinear time history analyses. Based on the criteria of efficiency, practicality, proficiency and sufficiency, the optimal IMs in probabilistic seismic demand model that can best estimate the seismic response of the subway station structure are identified among those commonly used IMs. It is found that for sites of Class I and Class II, the Arias intensity followed by the velocity spectrum intensity are the optimal candidates, and for sites of Class III and Class IV, the Arias intensity is the optimal IM.

Rapid damage state classification for underground box tunnels using machine learning

This study develops and compares the performance of eight machine learning (ML) models to rapidly predict the seismic damage state of underground box tunnels. Nonlinear time history analyses of 24 soil-tunnel configurations subject to 85 ground motions were performed to generate the dataset for the ML models. The aspect ratio, buried depth, flexibility ratio, and 23 ground motion intensity measures (IMs) are employed as input variables of ML models. The output variables are four damage states, namely ‘none’, ‘minor’, ‘moderate’, and ‘extensive’. Among the eight ML models, LightGBM is found to yield the most favorable prediction of the damage states, resulting in an accuracy of 91%. The effects of earthquake IMs were also examined. Results show that the spectral acceleration () and spectral displacement () at the fundamental period of the site (T 1) have the strongest correlation with the damage prediction. Finally, the effect of reducing the input variables to two groups (i.e. combinations of soil-tunnel configuration parameters with top five and top ten ranked IMs) on the model prediction capability was investigated. Accordingly, ), acceleration spectrum intensity, spectral velocity, and velocity spectrum intensity were identified as the key parameters representing the ground-motion characteristics needed for the predictive model.

Seismic fragility analysis of clay-pile-pier systems considering the optimization of ground motion intensity measures

Seismic fragility analysis of clay-pile-pier systems considering the optimization of ground motion intensity measures

Fragility curves accounting for uncertainties in material parameters and ground motion characteristics using a data driven surrogate model

This study uses a statistical surrogate model to develop fragility curves for an infilled reinforced concrete frame building, considering uncertainties in both material properties and ground motion parameters. The focal point of this study is a school building in Nepal damaged during the 2015 Gorkha earthquake. The school was instrumented, and its seismic response was simulated using a nonlinear numerical model. The model, developed following a recently proposed framework and extensively validated with the field data, is used in a parametric study conducted to identify the most influential material parameters (MPs). The model is then used in incremental dynamic analyses conducted to provide data for the calibration of a surrogate model. The three-staged least square statistical modeling approach is adopted to relate the influential MPs and ground motion intensity measures with important response quantities related to the peak and residual first-story drift ratios. The surrogate model is employed to generate fragility curves accounting for the two sources of uncertainty. The results indicate that accounting for uncertainties associated with the MPs can alter the fragility curves, causing a shift in the prediction of the median and dispersion of intensity measures.