Multiple Intensity Measures Research Articles

Empirical correlations between ground motion intensity measures from the NGA‐West2 database and their use in vector generalized conditional intensity measures

AbstractThis paper examines the empirical correlations between 14 intensity measures (IMs) describing the frequency content, amplitude, cumulative effects, and duration aspects of ground motion based on the NGA‐West2 database. The correlation results in this paper are compared with the results of previous models based on the NGA‐West1 database, and the previous correlation coefficient models are updated and extended. The comparison results show that the trend of the correlation coefficients of the model established in this paper is essentially consistent with previous models based on the NGA‐West1 database, with most correlation coefficients observed in this paper being slightly lower than in previous studies. In addition, this paper extends the generalized conditional intensity measure (GCIM) ground motion selection method so that it can consider multiple conditional IMs (VGCIM), and gives full theoretical details. The differences between the theories of VGCIM and GCIM are discussed, and several possible application scenarios of VGCIM are illustrated. An example application of VGCIM is shown, and the results show that there are deviations between the target IMs conditional distribution constructed after considering multiple conditional IMs and considering one IM that is sufficient to make an impact in the ground motion selection. Finally, the effect of the correlation coefficient model on the IMs conditional distributions generated based on GCIM and VGCIM is discussed.

A direct analytical derivation of the multi-dimensional fragility spaces of structures under nonstationary mainshock-multi-aftershock sequences

Performance-based earthquake engineering (PBEE) is a popular direction in the earthquake community, and at this stage, risk-based PBEE has become mainstream. In the risk-based probabilistic framework, seismic fragility analysis constitutes the most important link, and corresponding research on the mainshock–aftershock sequence has received widespread attention in recent years. Since a mainshock is often accompanied by multiple aftershocks and there is great uncertainty in the vibration characteristics of aftershocks, a seismic fragility analysis of structures under a stochastic mainshock-multi-aftershock sequence is meaningful and necessary. The corresponding questions, such as how to derive the multi-dimensional analytical fragility form under a stochastic mainshock-multi-aftershock sequence and how to correlate multiple intensity measures with multiple demand parameters, still require further investigation. In this paper, a direct analytical derivation of the multi-dimensional seismic fragility spaces of structures under nonstationary stochastic mainshock-multi-aftershock sequences is introduced. The methodology framework, implementation steps, and application examples are also provided in detail. Moreover, two scenarios, the one-mainshock-one-aftershock and one-mainshock-two-aftershocks, are considered, and the obtained multi-dimensional analytical fragility spaces for both scenarios are validated. In general, the matching accuracy of the fragility results for both scenarios is proven to be high, and the direct analytical derivation of the multi-dimensional fragility spaces is validated to be ideally consistent, which further provides a reference for multi-dimensional risk analysis under nonstationary stochastic mainshock-multi-aftershock sequences in future work.

Prediction for underground seismic intensity measures using conditional generative adversarial networks

With the escalating development and utilization of subterranean spaces, the seismic hazards faced by underground structures are progressively increasing. However, owing to the challenges associated with acquiring underground seismic data and historical seismic design norms, pertinent regulations and research in this domain are scarce. This study focused on three crucial intensity measures in the seismic design process of underground structures peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD). The research leverages seismic data obtained from the California Strong Motion Instrumentation Program (CSMIP) to train and evaluate a conditional generative adversarial network (CGAN) model. This model was employed to establish a multivariate joint conditional probability distribution among the intensity measures at varying depths, facilitating the stochastic prediction of shallow intensity measures. In contrast to empirical formulas, the CGAN model eliminates the need for a predefined equation structure and enables the simultaneous prediction of multiple intensity measures. The performance of the model was evaluated by comparing the predictive accuracy of the CGAN model and empirical fitting formulas across diverse site conditions and depth intervals using metrics such as relative error coefficients. It can be concluded that the proposed CGAN model can accurately predict shallow seismic intensity measures, and the predictions conform to a specific conditional distribution while retaining the stochastic nature of seismic motion. Compared with empirical formula models, the CGAN model exhibited an enhanced predictive capability.

A New Spatial Variation Model for Ground-Motion Intensities Combined with Correlation and Coherency

Abstract Regional seismic risk or loss assessments generally require simulation of spatially distributed ground motions using multiple intensity measures. Hence, in this study, ground-motion model estimation is performed with a spatial correlation. Previously, many researchers have analyzed spatial correlations and developed empirical models using ground-motion recordings. In this study, ground motions occurring in California between 2019 and 2023 were used to analyze spatial correlations using semivariograms for the peak ground acceleration and pseudospectral acceleration in various spectral periods. Based on the analysis results, two aspects need to be revised in the conventional correlation model: (1) the empirical exponential model cannot reasonably reflect the target spatial correlation at a separation distance <10 km, and (2) the variation in the spatial correlation ground-motion intensity cannot be described at a small separation distance <1 km. Owing to these limitations, we revised the fitting model of the semivariogram to better characterize the spatial correlation. In the model, another function called coherency, replaced the spatial correlation to characterize the variation in the Fourier phase rather than the intensity within a separation distance <1 km. This research shows that the spatial variation in any region can be analyzed by combining the coherence and correlation functions for practical seismic-risk or loss assessment problems.

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).

Toward multivariate fragility functions for seismic damage and loss estimation of high‐rise buildings

AbstractData‐driven models for seismic damage and loss assessment of buildings have become more common in recent years due to the availability of large repositories of recorded and synthetic ground motions coupled with structural response simulation data. This paper explores the benefits of using bivariate and multivariate fragility functions to estimate earthquake‐induced damage and economic loss in high‐rise buildings. The dataset used in this study encompasses 15,000 simulations of modern high‐rise reinforced concrete shear wall buildings ranging from eight to 24 stories which are subjected to ground motion records at five different intensity levels. The proposed functions are conditioned on average spectral accelerations and ground motion significant duration. The results indicate that bivariate fragility functions improve damage state prediction success (Brier score) by 16%, and multivariate fragility functions by 24% relative to conventional univariate functions (standard of practice). To develop multivariate functions, nominal and ordinal probit regression models are fit to the dataset. While both models yield satisfactory predictive performance, ordinal functions can lead to a 15% reduction in misclassified collapse instances, that is, the minority class. Univariate functions tend to overestimate seismic losses at lower intensity levels while underestimating them at higher intensities. These loss estimates are significantly improved when bivariate or multivariate building fragility functions are used. Given the increase in the use of physics‐based ground motion simulations and/or multi‐variate ground motion models, from which multiple intensity measures can be extracted, a shift toward a more complex representation of fragility functions, for example, multivariate curves, is necessary and inevitable. The proposed functions are used to evaluate the performance of a portfolio of modern high‐rise reinforced concrete shear wall buildings at four sites across the Seattle, Washington metropolitan area under a potential magnitude‐9 Cascadia subduction zone earthquake scenario. The results indicate that the proposed functions can be beneficial in enhancing damage state predictions and loss estimates at a regional scale.

Physically Based and Empirical Ground Motion Prediction Equations for Multiple Intensity Measures (PGA, PGV, Ia, FIV3, CII, and Maximum Fourier Acceleration Spectra) on Sakhalin Island

In the present study, empirical attenuation relations for multiple ground motion intensity measures (PGA, PGV, Ia, FIV3, CII, and MFAS) were developed for Sakhalin Island (in the far east of Russia). A recorded strong motion dataset was used, making GMPEs applicable in active crustal regions with an earthquake magnitude range of 4–6 and a distance range of up to 150 km. The hypocentral distance was used as a basic distance metric. For the first time in the research, an analytical representation of Arias intensity (Ia) was obtained in the framework of a multi-asperity source model. Asperities are considered as sub-sources of high-frequency incoherent radiation. The physical representation of the attenuation model in our study was based on a stress drop on the asperities and the ratio of the total rupture area to the combined area of asperities. The average stress drop on asperities for the examined earthquakes was approximately 13.4 MPa, and the ratio of the total rupture area to the asperity area was 0.22, which is generally close to similar estimates for crustal earthquakes. The coefficients and statistical scattering of the attenuation models were also analyzed. Moreover, a magnitude scale based on a modified Arias intensity is proposed in the present study. The new magnitude scale has an explicit physical meaning and is characterized by its simplicity of measurement. It is associated with the acceleration source spectrum level and can be successfully used in early warning systems.

A Practical Approach of Probabilistic Seismic Hazard Analysis for Vector IMs Regarding Mainshock with Potentially Largest Aftershock

ABSTRACT Seismic hazard and risk may be underestimated if aftershocks are ignored within the traditional probabilistic seismic hazard analysis (PSHA). In this study, a practical approach is proposed to calculate the mean annual rate of the joint exceedance of different ground motion vector intensity measures (IMs) for mainshocks and their potentially largest aftershock. The proposed approach is built based on an indirect vector-valued PSHA framework, which was originally used to compute the joint hazard of multiple IMs for mainshocks. Within the proposed approach, the overall hazard caused by mainshock-aftershock scenarios is calculated by including two key components, namely: 1) the occurrence rate of a given hazard level of mainshock as calculated by the standard PSHA; and 2) the occurrence probability of a given hazard level of aftershock conditioned on the mainshock, which is calculated by establishing the joint distribution of the IMs for both mainshocks and aftershocks using the Copula theory. Based on 662 groups of mainshock-aftershock ground motions from 13 earthquake events in the NGA-West2 database, the empirical joint probability distribution model between the peak ground accelerations of mainshocks and aftershocks (i.e. PGA MS and PGA AS) is developed. The Clayton Copula function is calibrated as the optimal choice and the marginal distributions of PGA MS or PGA AS could be properly modelled using the generalized extreme value distributions. The proposed approach is implemented to construct the joint hazard surface regarding both PGA MS and PGA AS for a hypothetical site. The results indicate that using different types of copula functions and marginal distributions of PGA MS and PGA AS has a noticeable impact on the final joint hazard level. Moreover, a coarse assumption that PGA MS and PGA AS follow a joint Gaussian distribution in the logarithm scale would lead to considerable bias in the mainshock-aftershock joint hazard especially at low mean annual rates of exceedance.

Machine learning-driven probabilistic seismic demand model with multiple intensity measures and applicability in seismic fragility analysis for nuclear power plants

A novel machine learning (ML) framework is applied in mapping intensity measures (IMs) to seismic engineering demand parameter (EDP). It is an efficient and accurate approach to establishing the probabilistic seismic demand model (PSDM) with multiple IMs for nuclear power plant (NPP). The optimal IM set on the performance of the prediction is further discussed herein. A total of 33 IMs are used to represent the characteristics of ground motions (GMs) comprehensively and significant IMs are identified by recursive random forest (RRF). 14 algorithms are used for determining the most optimal ML model. The generalization ability of the proposed ML model is empirically tested against traditional models based on scalar and vector IMs in a new dataset. ML is found to have absolute superiority in the construction of PSDM with inelastic correlation. Moreover, a promising methodology for deriving the fragility curve is presented based on multiple stripe analysis (MSA) driven by ML. By training the model, the workload of nonlinear time-history analysis (NLTHA) can be replaced, and the large inelastic behavior can be captured considering the stiffness degradation of NPP when it is subjected to high-intensity effects. It is meaningful for the fragility assessment of NPP beyond a design-basis event.

Post-earthquake regional structural damage evaluation based on artificial neural networks considering variant structural properties

The application of artificial neural networks (ANN) to regional seismic damage evaluation is still challenging due to variant structural properties and high computing requirements. This study aims to develop an ANN-based approach of regional seismic damage evaluation and investigate the ANN performance when structural properties like the fundamental period T and ductility factor μ change. By utilizing the Single-Degree-of-Freedom (SDOF) structures, ANN seismic classifiers, whose inputs are multiple intensity measures (IMs) and outputs are damage states, are trained for a structure portfolio with T from 0.3 to 3 s and μ from 1.5 to 6. The importance ranking of the multiple input IMs for classification indicates that the most important IMs for the correct classification of damage states vary when the structural properties change. Acceleration-related IMs play the most important role for short-period structures (e.g., less than 0.5 s), velocity-related IMs are most important for intermediate structures (e.g., 0.5 to 2.0 s), and displacement-related IMs are most important for long-period structures (e.g., larger than 2.0 s). This importance variation of IMs validates the necessity and advantage of ANN seismic classifiers with multiple IMs as input for the regional seismic damage evaluation. Further comparison of two different damage indices shows that the ANN performance will not be compromised by changing the damage index. Overall, the classification accuracy higher than 92% on the structure portfolio shows that the ANN-based regional seismic damage evaluation could be a robust and accurate approach with some limitations to be addressed in future studies.

A new ground-motion simulation procedure based on feature extraction matching multiple intensity measures

A new ground-motion simulation procedure based on feature extraction matching multiple intensity measures is proposed. The new simulation procedure is developed with the aim of generating a suite of acceleration time series at a single site that captures the multiple characteristics of ground motions. Building a regional ground-motion database in the data-driven simulation procedure is the first step of this work. Then, the principal component analysis algorithm is used to extract the main features of the regional database. Moreover, after dimension reduction, the regional eigenquakes database is generated. Multiple intensity measures (i.e., peak ground acceleration, spectral acceleration, significant duration and Arias intensity) are selected to construct a target vector-IM. A suite of target multiple IMs is obtained by sampling from the multivariate normal distribution of the target vector-IM, which is used as constraint conditions to control the goodness-of-fit. Finally, the superposition coefficients of the eigenquakes are determined using the multi-objective genetic algorithm, and the simulated records are generated by superposition in the time domain. Taking Kanto, Japan, as the target region, the proposed simulation procedure is specifically applied. These results indicate that the simulation procedure is able to achieve the coordinated matching of the amplitude, spectrum, duration and cumulative energy of ground motions. This new simulation procedure can provide reasonable input for structural dynamic analysis.

Compound intensity measures for improved seismic performance assessment in cross-fault hydraulic tunnels using partial least-squares methodology

Seismic performance assessment of cross-fault hydraulic tunnels is typically expressed in terms of the probability of reaching or exceeding specific damage states related to earthquake intensity measures (IMs) and engineering demand parameters (EDPs). Obviously, this would require determining the appropriate earthquake IMs to reflect the seismological characteristics of ground motions and improving the predictive ability for the EDPs of cross-fault hydraulic tunnels. It is worth emphasising that an unreasonable IM may increase the ground motion uncertainty, which in turn introduces additional uncertainty to the EDP estimates of cross-fault hydraulic tunnels. Hence, this study presents a compound IM, integrating multiple earthquake IMs, to improve the reliability of the seismic fragility analysis of a cross-fault hydraulic tunnel based on the partial least squares methodology. To this end, seven scalar IMs are first determined from the 20 examined earthquake IMs as the fundamental element of the compound IM using four test criteria. Subsequently, a 1080 complete nonlinear dynamic time history analysis of the cross-fault hydraulic tunnel using the incremental dynamic analysis method is conducted to generate a large amount of data. The findings of this study highlight that the compound IM-based probabilistic seismic demand models of the cross-fault hydraulic tunnel incorporate various sources of uncertainties, which enhances the correlation, efficiency, practicality, and proficiency of probabilistic seismic demand analysis and eventually leads to a more reasonable estimation of probabilistic failure. Moreover, the potential of nonlinear polynomial regression of compound IM can further improve the correlation and efficiency of probabilistic seismic demand analysis. Relative to the classical fragility curves utilising a single optimum-IM peak ground velocity, the probability of damage to the cross-fault hydraulic tunnel under a particular damage state related to the compound IM is significantly larger than the corresponding single optimum-IM peak ground velocity. Similarly, the probability of damage to the cross-fault hydraulic tunnel under the Wenchuan seismic event is underestimated, derived from a single optimum-IM peak ground velocity. The abovementioned phenomena demonstrate that a compound IM based on the partial least squares methodology can be suggested for use in estimating the probability of structural damage in performance-based earthquake engineering.

Empirical Correlations between the Spectral Input Energy and Spectral Acceleration

ABSTRACT Correlating different intensity measures (IMs) enables the joint consideration of multiple IMs in various performance-based earthquake engineering applications, such as ground motion selection. The correlations between the spectral input energy and spectral accelerations have not been previously examined. This study develops a correlation model quantifying the uncertainties due to GMPE selection and a finite sample size. The results show a strong correlation between the spectral amplitudes at the same period, and the correlation decreases as the period difference increases. With the proposed model, ground motion characteristics reflected by the input energy can be explicitly considered in performance-based earthquake engineering applications.

Optimal Generation of Multivariate Seismic Intensity Maps Using Hazard Quantization

This paper introduces a method for the optimal generation of multivariate intensity measure (IM) maps representing various aspects of the intensity of an earthquake over a region that can be used as input for regional hazard and loss-estimation analyses. The proposed method is an extension of the single-variate hazard quantization (HQ) methodology for the effective sampling of IM maps with a single intensity measure. The use of multiple intensity measures to describe an earthquake enables a more comprehensive representation of the event, with richer information. For instance, to conduct accurate regional seismic loss analyses of a portfolio of structures with different fundamental periods, maps of spectral accelerations at different periods (multivariate IM maps) are necessary. With this in view, the multivariate HQ method is proposed for the effective generation of a relatively small set of multivariate IM maps that can capture the seismicity of a region. A case study of the seismic region of Charleston, South Carolina, is presented. The sample space is represented by a large set of pairs of IM maps representing the spectral acceleration at two different periods, with each pair representing a specific earthquake event. The simulated pairs of IM maps carry the stochastic characteristics of the associated seismic events, such as the spatial correlation of the ground motions and the cross correlation between different intensities at different sites in the region. The ability of the proposed method to provide an accurate estimation of the hazard curve for both intensity measures and to correctly capture the spatial autocorrelations and the spatial cross correlation among the two intensity measures was investigated quantitatively. Experiments were conducted to demonstrate the robustness of the method and the effect of the sample size on the performance of the method.

A Seismic Demand Model for Bridges Based on IM and EDP Parameter Matching and Bidirectional Optimization

The accuracy of seismic demand models in seismic vulnerability analysis of structures or components mainly depends on the seismic intensity measures (IMs) and engineering demand parameters (EDPs). This paper proposes a novel method to obtain the optimal seismic demand model for the seismic vulnerability analysis of bridges. The method obtains the IM-EDP combination by matching all IMs and EDPs within a wide range one by one, considering the contribution of multiple IM parameters to the seismic response of the structure and avoiding the blindness of EDP selection. The IM is determined by calculating Pearson correlation coefficient and partial correlation coefficient, controlling the correlation between EDP and IM (or IMs) to a minimum to reduce the multicollinearity within the vector IMs and avoid ill-conditioned models. The optimal seismic demand model is obtained by inspecting the scatter plot and residual plot of suboptimal seismic demand models determined from all combinations by guaranteeing efficiency and sufficiency. The efficiency of seismic demand models is guaranteed by controlling the root mean square error (RMSE) and the coefficient of determination (R2). The sufficiency of models is guaranteed by controlling the slope of fitted line. A continuous rigid frame bridge with double thin-walled piers is used as a case study and a dynamic time-history analysis is performed to obtain the seismic vulnerability of bridge with the proposed method. The results show that the proposed method is feasible and ideally suited for optimizing seismic demand model.

Real-Time Seismic Damage Prediction and Comparison of Various Ground Motion Intensity Measures Based on Machine Learning

ABSTRACT After earthquakes, an accurate and efficient seismic-damage prediction is indispensable for emergency response. Existing methods face the dilemma between accuracy and efficiency. A real-time and accurate seismic-damage prediction method based on machine-learning algorithms and multiple intensity measures (IMs) is proposed here. 48 IMs are used for representing the ground-motion characteristics comprehensively, and the workload of the nonlinear time-history analysis (NLTHA) method is replaced by model training in the non-urgent stage to promote efficiency. Case studies with various buildings prove the accuracy and efficiency of the proposed method, and corresponding key IMs are identified by iterative optimization.

Modeling spatial cross-correlation of multiple ground motion intensity measures (SAs, PGA, PGV, Ia, CAV, and significant durations) based on principal component and geostatistical analyses

Ground motion intensity measures (IMs) were observed to be spatially correlated during past earthquakes. In this article, a new spatial cross-correlation model for a vector-IM, which consists of spectral acceleration (SA) ordinates at 17 periods and six non-SA IMs (e.g. peak ground velocity, Arias intensity, cumulative absolute velocity, and significant durations), is proposed using principal component analysis (PCA) and geostatistical analysis. A total of 3797 ground motion records are selected from the NGA-West2 database for such analyses. PCA is used to transform the spatially correlated within-event residuals into uncorrelated principal components; a permissible function is then proposed to fit the empirical semivariograms calculated by the principal components. It is evident that the proposed model performs well in capturing the spatial variability characteristics of the multiple ground motion IMs. A simple example is presented to illustrate the use of the proposed model in realizing spatially correlated ground motion residuals of multiple IMs. The model developed enables one to simulate spatially cross-correlated IMs over a large area in a rapid way.

Conditioning criteria based on multiple intensity measures for selecting hazard-consistent aftershock ground motion records

A mainshock earthquake is typically followed by a sequence of aftershocks, normally with a higher probability of occurrence within the first few days after the mainshock event. Quantifying the subsequent damage due to an aftershock event for a given structure that is already at some state of damage due to the mainshock event is an important task in seismic hazard and loss assessment. This task requires a framework to quantify the aftershock hazard in addition to the mainshock hazard that is already well defined. The present study aims to develop such a framework that facilitates the quantification of aftershock hazard given two sequential fault rupture scenarios—an approach that ultimately serves the basis for a hazard-consistent aftershock ground motion selection. To this end, conditioning criteria based on a single, two, and average-of-multiple intensity measures are utilized in order to select hazard-consistent aftershock records from databases comprising both mainshock and aftershock events. The primary outcome of this study is, thus, a stochastic framework that defines the aftershock seismicity of a given site and enables the aftershock ground motion selection for performance-based seismic assessment and design of structures.

Investigation of anisotropic spatial correlations of intra-event residuals of multiple earthquake intensity measures using latent dimensions method

SUMMARYConsidering spatial correlation of multiple earthquake intensity measures (IMs) is of particular importance in loss assessment of spatially distributed assets. This subject has been investigated in previous studies under the assumption of isotropy. Considering the fact that the assumption of isotropy is not valid in general, the present study employs a non-separable covariance model based on latent dimensions method to investigate anisotropic properties of spatial correlations and cross-correlations of intra-event residuals of multiple earthquake IMs. This method leads to the generation of valid covariance matrix in order to model anisotropic spatially distributed multivariate random fields. Two sets of IMs are considered in this study; the first set consists of peak ground intensity values (acceleration, velocity, and displacement), and the second set consists of spectral accelerations at three different periods. Data of 10 earthquake events in California and Japan are utilized in this study to estimate parameters of marginal and cross-covariance models. Moreover, parameters of covariance model of regional site condition, which is considered as average shear wave velocity of top 30 m of soil profile (Vs30), are obtained in order to investigate the effect of local sited conditions on spatial correlations of IMs. It is shown that maximum range and anisotropy ratio of covariance models of intra-event residuals of IMs are correlated with those of Vs30 values. Also, it is observed that the anisotropy direction of residuals of IMs is consistent with anisotropy direction of Vs30 values. Finally, predictive models are proposed to obtain marginal and cross-covariance functions for different earthquake IMs considering anisotropy.

Accounting for spectral shape in simplified fragility analysis of case-study reinforced concrete frames

This paper deals with the selection of optimal intensity measures (IMs) for displacement-based seismic demand assessment and fragility derivation of case-study mid-rise reinforced concrete (RC) frames. The considered frames represent distinct RC vulnerability classes in the Mediterranean region. Optimal IM selection is performed by means of probabilistic seismic demand models considering multiple IMs and various engineering demand parameters (EDPs). Based on findings from previous and concurrent studies, a small subset of potential IMs is investigated here, including conventional peak IMs and two advanced scalar IMs accounting for spectral shape over a range of periods. Probabilistic seismic demand models are built on data obtained from analysis of the case-study frames subjected to over nine hundred ground motions by employing an innovative capacity spectrum method using inelastic response spectra derived from actual earthquake accelerograms to estimate seismic demand and derive fragility curves. This approach has the advantage of simplicity and rapidity over other methods as nonlinear dynamic analysis.This study concludes that advanced IMs, and particularly the ones accounting for period elongation (due to the nonlinear structural behavior) and structure-specific parameters, can effectively satisfy all the selection criteria, including the hazard computability criterion.