Seismic Fragility Curves Research Articles

Effect of Ground Motion on Fragility and Vulnerability of Reinforced Concrete Bridge in Different Soil Conditions

Bridges are essential components of transportation networks, serving as lifelines for the movement of people and goods. The bridges' resilience to natural disasters, particularly earthquakes, is an issue of paramount significance. Yet, a critical knowledge gap exists when it comes to understanding how bridges perform in the challenging terrain of medium-density sandy and clay soils during seismic events. This study seeks to bridge that gap by developing fragility curves that quantitatively evaluate the likelihood of bridge damage or failure across varying levels of earthquake magnitudes. The CSI Bridge v25.0.0 was used to simulate earthquake ground motions specifically within medium-density sandy and stiff clayey soils. Nonlinear time history analyses of the bridge were performed by using 5 different earthquake events with PGA ranging from 0.25 – 1.5g. Fragility analysis is performed to develop seismic fragility curves for the critical part of the bridge for various peak ground acceleration (PGA) in two types of soil conditions. The findings revealed that the deck component is more susceptible to damage than the pier. The impact of seismic activity on medium-dense soil is more significant than on stiff clayey soils in relation to the critical components of the bridge. This result enhances the ability to design bridges that can withstand and recover from seismic events more effectively.

Analyzing the dependency of earthquake insurance premium on the intensity measure used in probabilistic seismic hazard analysis and fragility curves

Considering the uncertainties associated with earthquake, seismic response of structures, damage caused by the response and the repair costs, determining Earthquake Insurance Premium (EIP) is a challenging task that requires a framework for modeling all inherent risks. Most of the models proposed for EIP determination have two basic components. The first component indicates the probability of seismic hazard (generally obtained from the probabilistic seismic hazard analysis), and the second component indicates the probability of occurrence of a given damage to the structure with respect to different seismic hazard levels (generally obtained from the fragility curves). These two components are interconnected through an intermediate parameter, known as the Intensity Measure (IM), which is of great importance in calculations and has hardly been considered so far. In this study, the dependency of EIP on the IM used in the probabilistic seismic hazard analysis and the fragility curves is analyzed. To this end, the EIP was determined for five types of buildings at low, medium and high seismic hazard levels using two IMs (peak ground acceleration and first mode spectral acceleration). The results of this study warn that the EIP can be highly dependent on the IM, so that for all studied structures, the EIP determined based on peak ground acceleration at a high seismic hazard level is nearly three times as many as the one determined by the first mode spectral acceleration. A significant finding is that when the first mode spectral acceleration is used as the IM, the ratio of EIP at different seismic hazard levels closely matches the ratio of IM at the same levels. This result can be valuable in developing insurance codes and regulations. However, this is not the case for the currently used IM, i.e., peak ground acceleration.

Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls

Efficient seismic risk assessment aids decision-makers in formulating citywide risk mitigation plans, providing insights into building performance and retrofitting costs. The complexity of modeling, analysis, and post-processing of the results makes it hard to fast-track the seismic probabilities, and there is a need to optimize the computational time. This research addresses seismic probability and risk assessment of reinforced concrete shear walls (RCSWs) by introducing stacked machine learning (Stacked ML) models based on Bayesian optimization (BO), genetic algorithm (GA), particle swarm optimization (PSO), and gradient-based optimization (GBO) algorithms. The study investigates 4-, to 15-Story RCSWs assuming different bay lengths and soil types to build a comprehensive database based on the incremental dynamic analysis (IDA) subjected to 56 near-field pulse-like and no-pulse records. Having 227,200 and 63,384 data points for a median of IDA curve (MIDA) and seismic probability curve, respectively, the proposed Stacked ML models have shown good performance on curve fitting ability by accuracy of 99.1% and 99.4% for MIDA and seismic fragility curves, respectively. In addition, the proposed models can estimate the mean annual frequency, λ, which is a key parameter in seismic risk assessment of buildings. To provide the results of the study for general buildings, a user-friendly GUI is proposed that facilitates result utilization, offering insights into seismic performance levels, providing the estimated MIDA and seismic failure probability curves, and mean annual frequency calculations for specific performance levels and seismic hazard curves.

Hierarchical and mixed uncertainty quantification for simulation-based structural seismic fragility analysis

Seismic fragility assessments (SFA) encompass significant uncertainties, including ground motions, materials, geometry, boundaries, and modeling, which impart inherent uncertainties to the fragility curve. Quantifying these uncertainties is essential for informed decision-making in earthquake disaster mitigation. This paper presents a hierarchical uncertainty quantification (UQ) framework for simulation-based SFA, which can address mixed aleatory and epistemic uncertainties. The framework comprises two levels: At Level 1, ground motion uncertainties are accounted for through record-to-record variation, while structural parameter uncertainties are captured using well-defined probability distributions. The quantification of uncertainty in seismic fragility is accomplished using mean and quantile fragility curves, representing the average outcome and the dispersion of structural fragility, respectively. Fuzziness is also introduced to enhance the objectivity of failure determination. Level 2 addresses uncertainties in the distribution hyper-parameters of structural variables through evidence theory, facilitating an understanding of their influence on the seismic fragility curve and quantifying uncertainty in mean and quantile fragility curves. This UQ framework builds upon the multivariate seismic fragility analysis method, integrating Kriging regression for seismic demand prediction and logistic regression for generating multivariate seismic fragility functions. The proposed framework is validated numerically on an existing three-span reinforced concrete continuous girder bridge, affirming its practical utility and reliability.

Study on the Effect of Burial Depth on Selection of Optimal Intensity Measures for Advanced Fragility Analysis of Horseshoe-Shaped Tunnels in Soft Soil

Seismic intensity measures (IMs) can directly affect the seismic risk assessment and the response characteristics of underground structures, especially when considering the key variable of burial depth. This means that the optimal seismic IMs must be selected to match the underground structure under different buried depth conditions. In the field of seismic engineering design, peak ground acceleration (PGA) is widely recognized as the optimal IM, especially in the seismic design code for aboveground structures. However, for the seismic evaluation of underground structures, the applicability and effectiveness still face certain doubts and discussions. In addition, the adverse effects of earthquakes on tunnels in soft soil are particularly prominent. This study aims to determine the optimal IMs applicable to different burial depths for horseshoe-shaped tunnels in soft soil using a nonlinear dynamic time history analysis method, and based on this, establish the seismic fragility curves that can accurately predict the probability of tunnel damage. The nonlinear finite element analysis model for the soil–tunnel interaction system was established. The effects of different burial depths on damage to horseshoe-shaped tunnels in soft soil were systematically studied. By adopting the incremental dynamic analysis (IDA) method and assessing the correlation, efficiency, practicality, and proficiency of the potential IMs, the optimal IMs were determined. The analysis indicates that PGA emerges as the optimal IM for shallow tunnels, whereas peak ground velocity (PGV) stands as the optimal IM for medium-depth tunnels. Furthermore, for deep tunnels, velocity spectral intensity (VSI) emerges as the optimal IM. Finally, the seismic fragility curves for horseshoe-shaped tunnels in soft soil were built. The proposed fragility curves can provide a quantitative tool for evaluating seismic disaster risk, and are of great significance for improving the overall seismic resistance and disaster resilience of society.

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

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

Seismic fragility curves for concrete gravity retaining wall

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

Seismic fragility analysis of prefabricated self-centering frame structure

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

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

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

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

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

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

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

Two-Scale Probabilistic Seismic Fragility Assessment for a Prestressed Concrete-Steel Hybrid Wind Turbine Tower with Incremental Dynamic and Multiple Stripe Analysis

ABSTRACT To evaluate the seismic performance of a prestressed concrete-steel hybrid (PCSH) wind turbine tower, the seismic fragility curves based on incremental dynamic analysis (IDA) and multiple stripe analysis (MSA) on a two-scale model are presented and compared. The peak ground acceleration (PGA) and peak lateral drift on the top of the tower are adopted as the intensity measure (IM) and engineering demand parameter (EDP) of analysis, respectively. Finally, the seismic performance of the PCSH tower subjected to seismic action at different soil is investigated. The fragility curves obtained by IDA and MSA methods are compared.

Seismic fragility analysis of human perception in high‐rise buildings subjected to far‐field long‐period earthquake ground motions: A case study for Shanghai tower

AbstractHuman perception in high‐rise buildings during far‐field long‐period earthquake ground motions often induces psychological discomfort, including fear and anxiety. A comprehensive understanding on human perception of seismic events is vital for city planning and emergency management, facilitating effective evacuation plans and minimizing stampede risks. This study addresses a gap by employing the seismic fragility analysis method to delineate how human perception in high‐rise buildings is affected during far‐field long‐period ground motions. The methodology involves several key steps. First, the far‐field long‐period earthquake ground motion was identified by detecting the later‐arriving surface waves, and a series of records were selected from the next generation attenuation (NGA) West‐2 ground motion database of the Pacific Earthquake Engineering Research center. Then, a high‐rise building in Shanghai, China was modeled using ETABS 20. Through the linear‐elastic time history analysis, the structural seismic response at each floor was computed. Furthermore, human perception thresholds to vibration were introduced to assess the degree of human perception at each floor, illustrating the difference of human perception to seismic tremor at different floors. Finally, a novel earthquake intensity measure (IM), namely average response spectrum intensity (ARSI) within the vibration period ranging from 0.1 s to 10 s was introduced to generate seismic fragility curves for human perception. According to the investigation, it was found that the corner frequency and the associated energy ratio are useful indicators to identify the far‐field long‐period earthquake ground motions. The ARSI is an effective parameter to assess human perception to seismic tremor compared to the spectral acceleration at a given fundamental period. The generated seismic fragility analysis can provide a complete and thorough understanding on the probability of people that should be evacuated under different earthquake intensity levels. The probability of human perception at different floors varies along the building height, demonstrating the difficulty to make crowd evacuation plan in practice. This insight is vital for understanding and mitigating seismic concerns in high‐rise buildings, particularly in low‐to‐moderate seismicity regions.

Confinement effects within the seismic design of reinforced concrete frames: reliability assessment and comparison

The current Italian code “NTC2018” and Eurocode “EC8” provide specific design requirements in terms of flexural overstrength factors for reinforced concrete (RC) buildings in seismic areas. This study proposes to improve the design methodology by including explicitly the confinement effects within the seismic design of a new regular RC frame due to their influence on the capacity design principles (at structural and member level). Specifically, the seismic performance of a RC frame designed according to codes is compared, in reliability terms, with the one of the same frame designed including the concrete confinement effects. Moreover, a comparison between three constitutive models for confined concrete is performed. Selecting L’Aquila (Italy) as reference site, the two design methodologies combined with the three confinement models are numerically implemented. Scaling 30 non-frequent natural ground motions to increasing seismic intensity measures, according to the site seismic hazard, non-linear incremental dynamic analyses (IDAs) have been developed. Assuming the interstory drift indices as engineering demand parameters, the peak responses have been fitted through log-normal distributions to define IDA curves. Subsequently, appropriate limit state thresholds have been adopted to define the seismic fragility curves. Finally, through the convolution integral and Poisson model, the seismic reliability curves have been derived showing the importance of the proposed design methodology. In fact, for the frame under investigation, the seismic performance strongly improves in terms of ductility respecting all the reliability objective levels if the confinement effects are explicitly considered in the seismic design. Furthermore, the different confinement models provide similar results when the proposed seismic design is adopted, implying a reduction of the model uncertainty.

Development of seismic fragility curves for hospital buildings using empirical damage observations

Fragility curves are widely recognized as an essential tool for assessing and projecting asset damage, vulnerability, and seismic risk. The technical literature provides different fragility curves for residential buildings that exhibit unique characteristics for different categories of buildings, however, comparatively fewer curves are available for hospital buildings. The specific design criteria for hospital buildings necessitate specialized fragility curves for them. In the current study, a set of empirical fragility curves for hospitals have been developed utilizing the data of 341 damaged hospital buildings in previous earthquakes. The peak ground acceleration of the occurred earthquake events in the location of the considered hospitals is utilized as earthquake intensity measure. Fragility curves are developed for the three categories of low-rise concrete moment frame, medium-rise concrete moment frame, and masonry hospital buildings. Due to the lack of appropriate available data, the fragility curves of masonry hospitals are developed by merging the collected data with the fragility curves acquired from the existing literature. The creation of fragility curves for non-structural components of hospitals is another contribution of this study. Finally, a comparison of the fragility curves derived in the current study with those documented in existing research papers is provided.

Influence of frequency characteristics of seismic ground motion on the fragility of an RC frame structure

Ground-motion frequency characteristics have a significant impact on structural damage. A quantitative description of the impact of ground-motion spectral features on structures is an urgent topic that requires solutions. In this study, the ratio of peak displacement to peak acceleration (R d2a ) is used as a representative index of the ground-motion spectral characteristics. The seismic records collected from the Kik-net strong-motion array in Japan are grouped according to their R d2a values. By adopting the incremental dynamic analysis method, the seismic fragility curves of a six-story RC frame structure loaded by inputting seismic records with different R d2a values are calculated using the OpenSees platform. The influence of R d2a values on the structural fragility is analyzed. Ground-motion records of liquefied sites are collected, and the peak ratios of seismic records at the liquefied sites are analyzed to reveal the differences in the fragility of the six-story RC structures at liquefied and non-liquefied sites. The results of this study are expected to serve as a reference for the evaluation of structural fragility and to understand the effect of ground-motion frequency characteristics on the fragility of RC frame structures.

Selection of optimal intensity measures for seismic performance evaluation of underground utility tunnel and internal pipeline system

Seismic intensity measures (IMs), which are closely related to both earthquake hazards and structural responses, play a critical role in the performance-based earthquake engineering (PBEE) framework. This paper aims to investigate and define the most representative IMs for probabilistic seismic demand model (PSDM) for the longitudinal utility tunnel and internal pipeline system. Similar to the requirements by the Chinese Code for seismic design of urban rail transit structures, the utility tunnel in this study is assumed being buried in four typical engineering site classes for the analyses. Soil-tunnel-pipeline interactions are simulated using a double-beam system resting on a nonlinear foundation. Soil-tunnel interaction, tunnel-pipeline interaction and joint connections are modeled as nonlinear springs with different mechanical properties. A series of 17 pairs of outcrop ground-motion records and 27 commonly used IMs are selected in the analyses. Engineering demand parameter (EDP) is defined based on the maximum joint opening and the maximum strain of the utility tunnel and internal pipeline, respectively. The selected IMs for predicting the seismic responses of tunnel-pipeline systems are tested using different criteria, i.e., correction, efficiency, practicality, and proficiency. The final decision-making procedure employs a fuzzy multi-criteria decision method. The numerical results indicate that: (1) The velocity-related IMs are the most representative IMs based on the four criteria for structures buried in four sites of different conditions; (2) The optimal seismic IMs vary with different criteria, site classes and structure types. According to the fuzzy multi-criteria decision approach, optimal IMs for the tunnel-pipeline system are PGV, PGV, EPV and HI for site classes I – IV, respectively. The proposed optimal IMs can be utilized to construct seismic fragility curves of utility tunnel, internal pipeline and tunnel-pipeline systems in four different site classes.

Seismic capacity models for earth dams and their use in developing fragility curves

This article develops seismic capacity models for earth dams and demonstrates how the proposed seismic capacity models may be used to develop seismic fragility curves that predict the probability of damage as a function of ground shaking intensity. Developing earth dam fragility curves requires two major components: (1) a seismic capacity model that predicts the probability of a damage state given the relative settlement (RS) and (2) an engineering demand model that predicts the RS as a function of ground motion intensity. While seismic demand models for earth dams have been studied by various researchers, there is a scarcity of seismic capacity models. This article focuses first on developing seismic capacity relationships using a dataset of earthquake case histories of dam performance. Different from previously developed relationships, only the damage description for each dam is used when assigning the damage state, which results in statistical variability in the capacity relationship between the damage state and RS. The fragility curve development is demonstrated by combining the seismic capacity relationships with a seismic demand model for RS derived from nonlinear, dynamic finite element analyses for a 20-m generic dam geometry subjected to a suite of earthquake motions from the NGA-West2 database.

Study on seismic fragility of prefabricated columns connected by grouted sleeves based on IDA method

PurposePrefabricated columns connected by grouted sleeves are increasingly used in practical projects. However, seismic fragility analyses of such structures are rarely conducted. Seismic fragility analysis has an important role in seismic hazard evaluation. In this paper, the seismic fragility of sleeve connected prefabricated column is analyzed.Design/methodology/approachA model for predicting the seismic demand on sleeve connected prefabricated columns has been created by incorporating engineering demand parameters (EDP) and probabilities of seismic failure. The incremental dynamics analysis (IDA) curve clusters of this type of column were obtained using finite element analysis. The seismic fragility curve is obtained by regression of Exponential and Logical Function Model.FindingsThe IDA curve cluster gradually increased the dispersion after a peak ground acceleration (PGA) of 0.3 g was reached. For both columns, the relative displacement of the top of the column significantly changed after reaching 50 mm. The seismic fragility of the prefabricated column with the sleeve placed in the cap (SPCA) was inadequate.Originality/valueThe sleeve was placed in the column to overcome the seismic fragility of prefabricated columns effectively. In practical engineering, it is advisable to utilize these columns in regions susceptible to earthquakes and characterized by high seismic intensity levels in order to mitigate the risk of structural damage resulting from ground motion.

Multi-metric evaluation of the optimal intensity measure for mainshock-aftershock fragility analysis of transmission towers

Identifying the optimal intensity measure (IM) contributes to reducing uncertainties in probabilistic seismic demand models (PSDM) and enhancing confidence of their results. Although many studies have discussed appropriate IMs for mainshocks, relatively less attention is paid to the potential aftershock effect. In particularly, the optimal IMs for developing PSDM of transmission towers subjected to sequential earthquakes has not been well studied previously. Therefore, this paper conducts a multi-metric evaluation to identify the optimal IM in the context of mainshock-aftershock (MSAS) fragility analysis of transmission towers. This evaluation process includes collecting a set of ground motions recorded with varying magnitudes and fault distances to construct MSAS sequences, followed by comprehensive discussions on their seismic characteristics by comparing thirty-six potential IMs candidates. Additionally, a finite element model of a transmission tower is established to numerically investigate different engineering demand parameters (EDP) of the tower under the selected MSAS sequences. The optimal PSDM of the transmission tower is determined by evaluating each couple of IM−EDP on a logarithmic space using multiple evaluation metrics. Through this analysis, the first-mode spectral acceleration Sa(T1,ξ) is identified as the optimal IM as it exhibits excellent correlation, efficiency, practicality and proficiency with the EDPs of the transmission tower, especially with the inter-segment displacement ratio. Consequently, seismic fragility curves of the transmission tower are developed using the optimal IM. The research outcome can contribute to improving the understanding of the probabilistic assessment of transmission towers in the MSAS scenario.