Engineering Demand Parameters Research Articles

Seismic Fragility Estimation Based on Machine Learning and Particle Swarm Optimization

In seismic performance assessment, the development of building fragility curves is critical for performance-based engineering. Traditional methods for time history analysis, reliant on detailed ground motion (GM) inputs, often suffer from inefficiency and a lack of automation. This study proposes an accurate fragility assessment methodology, which is assisted by machine learning (ML) and particle swarm optimization (PSO), adept at handling scenarios with both scarce and sufficient fragility data. Under scenarios of scarce data, the integrated algorithms of PSO and ML are utilized, focusing on selecting GMs that may induce maximum inter-story drifts. When the dataset is sufficient, an ML fusion model is utilized to predict engineering demand parameters (EDPs), facilitating the generation of more accurate fragility curves. The effectiveness of this method is demonstrated through a case study on a high-rise reinforced concrete (RC) building, revealing a marked improvement in the precision of GM selection and the estimated range of fragility curves over traditional approaches. The proposed methodology aids in advancing structural optimization and the development of early-warning systems for seismic events, thus holding the potential to enhance current seismic risk mitigation strategies.

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.

Investigating seismic performance of SMRF buildings under subshear and supershear rupture conditions: Assessment of storey-level internal damage

The source effect of earthquake ruptures causes spatial-temporal variability in ground motion characteristics in the vicinity of a fault. This research employs a dynamic rupture-based method with SPECFEM3D to simulate two rupture types: subshear and supershear. While the former depends on the rupture length of the fault, the latter depends on the rupture velocity. Synthetic earthquake ground motion data are generated across the entire domain, encompassing both near-fault and far-fault locations. Ground motions exhibit multidimensionality, with subshear effects evident in the fault normal component and supershear effects distinctly reflected in the fault parallel component. These ground motions have been employed to excite low (S1L), mid (S1M), and high-rise (S1H) steel special moment resisting frame (SMRF) buildings modelled using the modified Ibarra-Medina-Krawinkler (IMK) analogy. Understanding the far-field effects of supershear earthquakes and the near-field effects of directivity in subshear earthquakes on the structural response of SMRF buildings has been aided by several engineering demand parameters (EDPs), including floor acceleration, interstorey-drift ratio, and roof drift ratio.

Modification of wavelet-based downsampling method to reduce computational error in nonlinear dynamic analysis

One of the significant obstacles in conducting linear and non-linear time history analysis is its time-consuming nature. In this article, a new downsamlping method based on discrete wavelet transform (DWT) and smoothing is proposed to overcome this problem. In order to assess the precision of this approach, 50,000 linear and non-linear dynamic analyses of single degree of freedom (SDOF) systems and 300 nonlinear dynamic analyses of frame structures have been performed. One hundred Fema440 records were utilized to generate approximate waves up to the third level and the outcomes of this method were then contrasted with those of DWT. It has been demonstrated that the third-level approximate wave produced by DWT, previously considered dependable in other research, generates significant errors in results and the average error (absolute error percentage of the acceleration spectrum) of its third-level approximate wave is approximately 17.5%. On the other hand, the proposed method generated approximate waves with an average error of less than 4.5% across all behavior coefficients and periods and the error rate decreases as the period and behavior coefficient increase. Analysis of steel moment-resisting frames indicated that the lowest error in both methods is achieved for the base shear and across different engineering demand parameters, the average error rate for the proposed method was below 7.5%. Furthermore, caution must be exercised when employing the proposed method for structures with periods shorter than 0.5 s.

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.

A Computationally Efficient Algorithm for Constructing Effective Vector-Valued Seismic Intensity Measures for Engineering Structures

ABSTRACT Seismic intensity measures (IMs) quantify the severity of ground motions and their impacts on structures. They play a vital role in many aspects of earthquake engineering. This paper proposes a novel method, namely the express iteration method (EIM), for constructing effective vector-valued IMs based on dozens of existing scalar ones given a specific engineering structure or a class of them. Taking advantage of the sophisticated while efficient mapping between scalar IMs and engineering demand parameters (EDPs) via a machine learning model, EIM iteratively eliminates less important scalar IMs from a pool of candidates to find the most effective combinations for a vector-valued IM and achieves superior computational efficiency by avoiding updating the nonlinear mapping during the process. Taking a base-isolated structure and its non-isolated counterpart for a demonstrating case study, the performance of the vector-valued IMs determined by EIM is compared with those by other existing methods in the literature for the task of selecting the most unfavorable ground motions. The results show that EIM prioritizes records with the largest peak inter-story drift PIDs and thus leads to the smallest subset that imposes most severe structural damage, while its computational cost was two orders of magnitude smaller as compared to the existing methods of similar effectiveness. Such superior performance can also be expected in all tasks that involve vector-valued IMs, including but not limited to multi-dimensional fragility analysis, incremental dynamic analysis, and real-time seismic damage prediction.

Seismic fragility analysis of slopes based on large-scale shaking table model tests

Seismic fragility analysis is one probabilistic method that represents the conditional probabilities of exceeding different predetermined damage states for various given earthquake hazard levels, which is the essential part of probabilistic seismic risk assessment. It has gained much popularity recently because much more comprehensive and richer results than traditional probabilistic methods can be provided. The classical seismic fragility analysis of slopes is always performed through analytical approaches. In the present study, a novel seismic fragility analysis framework combined with shaking table model test results is proposed for probabilistic seismic performance and risk assessment of slopes. The shaking table model tests of a rock slope reinforced by pile-anchor structures were firstly performed subjected to a suite of real earthquake records with different intensity levels. Subsequently, the seismic responses from shaking table model tests were then gathered for the optimal analysis to determine the optimal engineering demand parameter (EDP) and earthquake intensity measure (IM) for seismic fragility analysis of slopes. At last, the identified most appropriate IM and EDP were selected to construct the scalar-valued fragility curves, vector-valued fragility surfaces and fragility surfaces considering acceleration elevation amplification effect by the proposed framework. The results indicate that using single IM to construct scalar-valued fragility functions may result in different failure probabilities depending on the chosen earthquake IM. The vector-valued fragility surfaces provide more information than ordinary fragility curves, and lead to more proper and realistic evaluations of seismic hazard of slopes. The methods and results presented in this paper also provide the basis for the probabilistic seismic risk and loss assessment of earthquake-induced slope geological disaster.

Stochastic emulation with enhanced partial‐ and no‐replication strategies for seismic response distribution estimation

AbstractModern performance earthquake engineering practices frequently require a large number of time‐consuming non‐linear time‐history simulations to appropriately address excitation and structural uncertainties when estimating engineering demand parameter (EDP) distributions. Surrogate modeling techniques have emerged as an attractive tool for alleviating such high computational burden in similar engineering problems. A key challenge for the application of surrogate models in earthquake engineering context relates to the aleatoric variability associated with the seismic hazard. This variability is typically expressed as high‐dimensional or non‐parametric uncertainty, and so cannot be easily incorporated within standard surrogate modeling frameworks. Rather, a surrogate modeling approach that can directly approximate the full distribution of the response output is warranted for this application. This approach needs to additionally address the fact that the response variability may change as input parameter changes, yielding a heteroscedastic behavior. Stochastic emulation techniques have emerged as a viable solution to accurately capture aleatoric uncertainties in similar contexts, and recent work by the second author has established a framework to accommodate this for earthquake engineering applications, using Gaussian Process (GP) regression to predict the EDP response distribution. The established formulation requires for a portion of the training samples the replication of simulations for different descriptions of the aleatoric uncertainty. In particular, the replicated samples are used to build a secondary GP model to predict the heteroscedastic characteristics, and these predictions are then used to formulate the primary GP that produces the full EDP distribution. This practice, however, has two downsides: it always requires minimum replications when training the secondary GP, and the information from the non‐replicated samples is utilized only for the primary GP. This research adopts an alternative stochastic GP formulation that can address both limitations. To this end, the secondary GP is trained by measuring the square of sample deviations from the mean instead of the crude sample variances. To establish the primitive mean estimates, another auxiliary GP is introduced. This way, information from all replicated and non‐replicated samples is fully leveraged for estimating both the EDP distribution and the underlying heteroscedastic behavior, while formulation accommodates an implementation using no replications. The case study examples using three different stochastic ground motion models demonstrate that the proposed approach can address both aforementioned challenges.

Dynamic increase factors for progressive collapse analysis of steel structures considering column buckling

Progressive or disproportionate collapse of structures may have severe socio-economic consequences. Aiming at buildings that can withstand such events, one solution is to prevent or minimize the propagation of damage that may lead to progressive collapse by means of robust design strategies. As a typical approach to model progressive collapse and assess robustness, the Alternate Path Method (APM) allows for static analyses, in which the dynamic effects induced by a sudden column loss are taken into account by amplifying loads through a Dynamic Increase Factor. Current recommendations for Dynamic Increase Factors to be used within non-linear static analyses have mainly considered beam-type collapse, overlooking other failure mechanisms, e.g., column buckling. The present paper investigates the dynamic effects of steel structures subjected to progressive collapse when buckling of columns is relevant. Five low- to high-rise case study building structures are considered together with three different column loss scenarios. A numerical procedure is introduced to evaluate the Dynamic Increase Factors considering two different Engineering Demand Parameters (EDPs), suited for describing beam- and column-type mechanisms, respectively. As Dynamic Increase Factors are typically assessed by increasing the loads on all the spans (DIF), a procedure was proposed for deriving factors that apply only on the spans above the removal (DIF*), consistently with UFC guidelines. The obtained DIF and DIF* are compared with the current literature and with values recommended in the UFC guidelines, highlighting the limits of current recommendations. Relevant considerations on the derived Dynamic Increase Factors and failure mechanisms involved are provided.

Seismic fragility analysis of high‐strength concrete frame structures reinforced with high‐strength steel bars

SummaryTo investigate the influence of using high‐strength steel bars in columns on the seismic resistance capacity and seismic resilience of frame structures, seismic fragility evaluation of three 8‐story reinforced concrete (RC) frame structures was conducted based on the incremental dynamic analysis (IDA) using 11 ground motion records. The main parameter is the longitudinal reinforcement configuration in the frame columns, where the first structure is reinforced with HRB 600 grade steel bars in the columns, the second structure is replaced with equal area ultra‐high‐strength (UHS) steel bars (i.e., with a yield strength of approximately 1425 MPa), and the third structure is replaced with equal strength UHS steel bars. A numerical model of the RC frame structure was developed and then validated using previous experimental results. The exceeding probabilities at various performance limit states were calculated based on two typical engineering demand parameters (EDPs) of maximum interstory drift and residual interstory drift. The results showed that using UHS longitudinal steel bars instead of HRB 600 grade steel bars in frame columns could reduce the energy dissipation capacity of the structure, inevitably leading to an increase in the maximum interstory response of the frame. However, much lower exceedance probability was observed in the UHS‐enhanced frame under the repair available limit state based on the residual interstory drift, indicating that the UHS‐enhanced RC frame had higher seismic resilience. In addition, compared to equal area substitution, equal strength substitution is a more ideal design method that can use fewer UHS steel bars to achieve comparable reparability and a smaller increase in maximum interstory drift.

Damage assessment of unreinforced masonry buildings incorporating damage accumulation

AbstractRecent earthquakes have shown susceptibility of unreinforced masonry (URM) buildings to damage accumulation in seismic sequences or long duration ground motions. Current structural modelling approaches commonly disregard the damage accumulation in URM buildings or they are unable to accurately capture this phenomenon unless sophisticated FEM models are employed. Such models are not feasible in risk‐based applications given the required large number of dynamic analyses needed to develop robust fragility and vulnerability curves. On the other hand, the common displacement‐based engineering demand parameters (EDP), such as inter‐story drift ratio, fail to capture the impact of damage accumulation in simpler, more manageable models. An alternative is to use advanced damage indices that are capable of monitoring the monotonic accumulation of damages. This study proposes a displacement‐ and energy‐based damage index that uses, as the basis, the Park and Ang damage index, modified and calibrated through experimental data of individual URM elements. Our calibration procedure maps the physical observed damage states to the hysteretic response of the elements. Consequently, damage on the individual elements is aggregated to define a global damage state at building level. Validation is carried out based on an equivalent frame model of a building tested on a shake table. Additionally, the model is subjected to multiple ground motions of seismic sequences and to long‐duration ground motions to evaluate the performance of the proposed damage index. In comparison with displacement‐based damage measures, the proposed damage index shows a superior ability to capture cumulative damage, even when simplified models are employed.

Bridge Capacity Assessment through LRFR Method and Bridge Seismic Performance Evaluation Using the PBSD Concept: Case Study

In this study, a comprehensive evaluation was conducted of the condition and performance of a concrete arch-type bridge located in close proximity to a fault. Utilizing the LRFR capacity assessment method and seismic performance analysis through the NLTHA process based on the PBSD concept, finite element modeling (FEM) was employed with a focus on construction stage analysis and model updating for calibration to site conditions. The assessment encompassed the determination of the rating factor for structural elements under service and ultimate limit state loading. Performance analysis under seismic loads includes an examination of engineering demand parameters such as concrete and reinforcement strains in columns, subjected to varying seismic hazard levels. Additional scrutiny involves assessing bearing displacement and overturning potential to identify potential damage before column failure. The primary objective of this research was to investigate pertinent and efficient techniques for evaluating bridge capacity and seismic performance. A thorough understanding of these methods is expected to facilitate the identification of suitable solutions to enhance the safety and reliability of bridges in Indonesia.

Seismic improvement of structures using hybrid self-centring dampers and rocking core

Extensive research on seismic-resistant systems aims to lower earthquake repair costs and downtime. This article presents a novel low-damage self-centring system that aims to improve the seismic resilience of buildings by enabling controlled rocking motion while reducing the risk of overturning. Significantly, the rotational movement can be entirely restored. This system combines a rocking core with damping devices, including disc springs and peripheral slit dampers. In this hybrid system, two damping devices are mounted to both first-floor columns of the rocking bays and the rigid movement of the rocking core triggers the dampers. Firstly, a comparative investigation was conducted to assess the efficacy of the endorsed hybrid system. The cyclic pushover results showed that the hybrid system is superior to other typical bracing systems in energy dissipation, with a five times higher margin. In addition, by employing the suggested system, the frame's drift ratio profile under nonlinear time history analysis (NTH) remained consistent throughout the structure, indicating its ability to withstand the soft storey mechanism. Secondly, a comprehensive assessment was conducted on the proposed system's collapse using incremental dynamic analysis (IDA) took into account various seismic intensities and engineering demand parameters for buildings with different bay lengths and included low, medium, and high-rise structures. The system's flag-shaped behavior and self-centring capability were confirmed by the average residual drift ratio of approximately 0.03% (almost zero) for all examined structures. Also, the collapse performance of the system exhibited a safety margin against collapse, with an average of 1.9 across all buildings and seismic intensities.

A full‐probabilistic cloud analysis for structural seismic fragility via decoupled M‐PDEM

AbstractPerformance‐based earthquake engineering (PBEE) is essential for ensuring engineering safety. Conducting seismic fragility analysis within this framework is imperative. Existing methods for seismic fragility analysis often rely heavily on double loop reanalysis and empirical data fitting, leading to challenges in obtaining high‐precision results with a limited number of representative structural analysis instances. In this context, a new methodology for seismic fragility based on a full‐probabilistic cloud analysis is proposed via the decoupled multi‐probability density evolution method (M‐PDEM). In the proposed method, the assumption of a log‐normal distribution is not required. According to the random event description of the principle of preservation of probability, the transient probability density functions (PDFs) of intensity measure (IM) and engineering demand parameter (EDP), as key response quantities of the seismic‐structural system, are governed by one‐dimensional Li‐Chen equations, where the physics‐driven forces are determined by representative analysis data of the stochastic dynamic system. By generating ground motions based on representative points of basic random variables and performing structural dynamic analysis, the decoupled M‐PDEM is employed to solve the one‐dimensional Li‐Chen equations. This yields the joint PDF of IM and EDP, as well as the conditional PDF of EDP given IM, resulting in seismic fragility analysis outcomes. The numerical implementation procedure is elaborated in detail, and validation is performed using a six‐story nonlinear reinforced concrete (RC) frame subjected to non‐stationary stochastic ground motions. Comparative analysis against Monte Carlo simulation (MCS) and traditional cloud analysis based on least squares regression (LSR) reveals that the proposed method achieves higher computational precision at comparable structural analysis costs. By directly solving the physics‐driven Li‐Chen equations, the method provides the full‐probabilistic joint information of IM and EDP required for cloud analysis, surpassing the accuracy achieved by traditional methods based on statistical moment fitting and empirical distribution assumptions.

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.

A New Method for Defining the Optimal Separation Gap Distance and the Acceptable Structural Pounding Risk on Multistory RC Structures

A proposal to control the structural pounding hazard imposed on multistory reinforced concrete (RC) structures is presented. The main goal is to guarantee the seismic performance of a structure with an acceptable (predefined) risk-targeted parameter without the need to eliminate structural pounding collisions. The key target parameters of this study are the annual probability of exceeding an engineering demand parameter (EDP) capacity level and the separation distance dg between adjacent structures. In this direction, a method that ensures the performance level of critical EDPs due to structural pounding conditions is proposed. The new method involves two decision frameworks that define (a) the optimal separation gap distance dg,minPt at a targeted value of pounding risk (probability per year) Pt (Decision A) and (b) the minimum acceptable structural pounding risk Pmindg,t at a targeted value of separation gap distance dg,t (Decision B). The demand parameters that are incorporated in the proposed method are the peak relative displacement δmax at the top level of colliding without considering pounding conditions and any other critical EDP due to the structural pounding effect. The overall method is based on two distinct acceptable performance objectives, the POs-δmax and the POs-EDP, defined as a function of P vs. dg. For this purpose, a seismic hazard curve compatible with Eurocode’s 8 hazard zone is adopted, and the corresponding demand hazard curves of δmax and EDP are developed. The proposed method is implemented to study the floor-to-floor structural pounding hazard of an eight-story RC frame taking into account different risk-targeted scenarios. The results show that the seismic risk (probability per year) of exceeding the EDP’s capacity level is significantly increased due to structural pounding in comparison to the case of no pounding. Calibration of the structural pounding risk can be obtained by adjusting the separation gap distance dg between the adjacent structures based on the acceptable POs. The POs-δmax is not always an accurate criterion for verifying the capacity level of the critical EDP. Finally, with the proposed method, a variety of POs-EDPs can be used to control the structural pounding risk in terms of dg,minPt and/or Pmindg,t.

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.

Shape memory alloy-reinforced UHPC tube confined bridge piers for enhancing the seismic resistance of highway bridges

Multi-span continuous highway bridges supported with reinforced concrete (RC) bridge piers may experience large permanent deformation after strong earthquakes. To overcome the limitations, an innovative UHPC tube-confined SMA reinforced bridge pier, i.e., shape memory alloy (SMA) replacing the steel rebars at the plastic hinge region and a prefabricated UHPC tube substituting the cover concrete, is proposed. To fully utilize the tensile strength of UHPC, the UHPC tube is embedded into the foundation. A performance-based seismic design procedure considering target residual drift as engineering demand parameter (EDP) is proposed to design this novel UHPC-SMA pier. The seismic responses of novel bridge systems supported by UHPC piers (Bridge II) and UHPC-SMA piers (Bridge III) are investigated and compared with reference bridge (Bridge I) having RC piers. The results indicate that both UHPC piers and UHPC-SMA piers are efficient in improving the seismic resistance of highway bridges. Compared with Bridge I, the prefabricated UHPC tube significantly decreases the residual deformation of Bridge II piers by 22.9 % as a result of the bridging effect of steel fibers. Due to the excellent self-centering property of SMA, the residual deformation of Bridge III piers decreases further by 57.8 % in comparison to reference bridge. The UHPC tube increases the low stiffness of bridge piers caused by SMA rebars at the plastic hinge region. The combination of UHPC tube and SMA can effectively prevent core concrete from severe damage with less dissipated energy of bridge piers.

Seismic response prediction using a hybrid unsupervised and supervised machine learning in case of 3D RC frame buildings

The seismic vulnerability assessment represents an important step in monitoring the buildings’ capacity and checking their performance during and after earthquake events. The Nonlinear Time History Analysis (NL-THA) is considered the most reliable method that is used to calculate the exact structural behavior of any building. However, this sophisticated method is known for its complexity, the use of Finite Element (FE) software, and computational time consuming, especially in the case of tall buildings. For that reason, An Artificial Neural network (ANN) is used to develop a new model able to predict the essential Engineering Demand Parameters (EDPs), i.e., the Maximum Base Shear (MBS), the Maximum Inter-story Drift (MIDR) and the Maximum Roof Drift Ratio (RDR). Unsupervised algorithms such as the Principal Component Analysis PCA and the Autoencoder are coupled with the ANN to reduce the dimensionality, improve the dataset quality, and reduce the irrelevant features. More than 192,000 buildings are analyzed using the NL-THA and eighty artificial ground motions (GMs) to generate the dataset. The buildings’ characteristics are generated randomly from the selection range. The results showed that the Autoencoder-ANN model represents the highest performance compared to the PCA-ANN and ANN models. The Autoencoder-ANN model could quickly and accurately predict the seismic responses of unseen ground motions using only the building’s characteristics and the GM parameters without using any FE software.