Seismic Damage States Research Articles

Seismic damage assessment of bonded versus unbonded laminated rubber bearings: A deep learning perspective

Laminated Rubber Bearings (LRBs), widely employed in highway bridges worldwide, are vulnerable to shear failure, squeezing or displacing during seismic events, potentially compromising bridge safety. Establishing a reliable approach for assessing the damage of LRBs is crucial for evaluating the seismic performance of bridges and preventing serious damage. To address this concern, this paper proposes a novel approach for LRBs seismic damage assessment based on computer vision (CV) technique. First, quasi-static loading tests are conducted on both bonded and unbonded LRBs to effectively enhance the understanding of their nonlinear properties. Subsequently, a large number of bearing samples are investigated to develop a comprehensive image database for a CV-based damage assessment model. A deep convolutional network (CNN) is designed to segment damage pixels, which are then combined with damage indicators to quantify the impact of various influencing factors on seismic damage using the interpretable machine learning technique, Shapley value. The results demonstrate that the CNN model achieves high-precision pixel segmentation and accurate predictions of seismic damage to LRBs, with RMSE consistently below 0.009 and R2 exceeding 95 %. The primary factor influencing LRB damage is the type of constraint, which interacts with loading characteristics and geometric dimensions to produce various coupled effects. This high-precision CV-based approach shows significant potential for estimating seismic damage states of critical bridge components and building seismic protection.

Research and Modification on the Park-Ang Damage Index for Railway Rectangular Piers

ABSTRACT This study addressed the issue of distortion in evaluating the seismic damage state of railway bridge piers using the original Park-Ang damage index. By constructing a parameterized co-simulation program with two finite element models, OpenSees and Abaqus, five sets of flexural failure and five sets of flexural-shear failure rectangular pier quasi-static test results were selected to validate the rationality of the calculation program. Four hundred and eighty parameterized calculations were conducted using this program. The combination coefficient β and critical damage index DI within the Park-Ang damage index were modified using nonlinear regression algorithm and convolutional neural network algorithm. The adaptability of the damage index calculation was ultimately verified through an engineering case study of a simply supported beam bridge. The research findings are as follows: 1) The main factor influencing β is the longitudinal reinforcement ratio, while the axial compression ratio, shear span ratio, volumetric stirrup ratio, aspect ratio, concrete strength, and steel strength are secondary parameters influencing β. 2) Compared to the nonlinear regression algorithm, the convolutional neural network algorithm can better establish a calculation model between pier structural parameters and β, clarifying their relationship. 3) It is recommended to use critical values of 0.11, 0.29, 0.49, and 1.00 for assessing pier damage as no, slight, moderate, severe, and collapse damage when using the damage index calculation model. 4) The Park-Ang index corrected by convolutional neural networks demonstrates improved accuracy and computational stability, making it suitable for practical assessment of seismic damage in bridge structures.

Post-earthquake damage probability assessment of high-speed railway simply supported bridge based on traffic capacity

Railway transportation is an important channel for emergency rescue and disaster relief. Traditionally, the method of evaluating the damage degree of bridges under different seismic intensities from the perspective of structural damage cannot be directly used to judge the traffic capacity of trains on bridges after earthquakes. It is impossible to guarantee the running safety and affect the transportation of materials after the earthquake. Because the damage state of track structure directly affects the running safety of trains, this study combines the relationship between the damage of track structure and the deformation of bridge structure after earthquake, and puts forward the transverse relative displacement of girder end (TRDGE) as the damage index of train running function of railway bridge after earthquake. Then, the OpenSees-TRBF (Train-Railway-Bridge-Foundation Coupled System Dynamic Analysis Platform) co-simulation method is used to carry out the train-track-bridge coupling dynamic calculation under different seismic damage states, and the running safety analysis database under different TRDGEs is established. Then, the Particle Swarm Optimization (PSO) algorithm is used to classify the bridge damage index of traffic capacity through different target safety speeds. Based on the probabilistic seismic demand model, the exceedance probability curve of traffic capacity damage under different seismic intensities is established. Finally, based on the curve, the running safety risk domain of trains on the bridge under different seismic intensities is obtained with a confidence level of 95 %. This study proposes a post-earthquake damage assessment method for railway bridges based on the assurance of running safety probabilities, evaluating the damage degree of the bridge with the residual traffic capacity of the bridge after the earthquake. This study can be applied to assess earthquake damage on existing railway bridges and provide references for the safe speed of trains on the bridge after an earthquake.

A numerical model database for rapid seismic damage assessment of typical regular reinforced concrete frame structures in urban building clusters

A vast city usually encompasses hundreds of thousands or even millions of buildings, majority of which are regular buildings with typical structural configurations. In the seismic damage simulation of cities, efficient and reliable simplified models are required to represent these buildings. A numerical model database of typical regular reinforced concrete (RC) frame structures was developed, utilizing shear models to simulate these RC frame structures. In contrast to existing shear models that are automatically generated based solely on the fundamental rules regulated by the theories of structures and the design codes, the shear models in this study were calibrated using refined numerical models to guarantee precise depiction of RC frame structures. The effects of reinforcement corrosion and the year of construction on structural performance were also considered. First, 108 RC frame structures were designed with typical configurations, strictly adhering to the regulations of design codes. Second, the refined models of the RC frame structures were established with modified material parameters to account for reinforcement corrosion, resulting in 540 refined models. Afterward, the parameters of each shear model were calibrated through cyclic pushover analysis of the corresponding refined numerical model. Finally, the characteristic points of the shear models were further modified to consider the different redundant capacities of the structures constructed in three time phases due to the update of design codes, by which a simplified numerical model database containing 1620 shear models was developed. The models' accuracy in predicting fundamental periods and seismic damage states was verified by comparing them to the refined models and three real RC frame buildings. The effects of the reinforcement corrosion and year of construction on the seismic damage states of the RC frame structures were also discussed. The developed numerical model database can be utilized for the seismic damage assessment of RC frame structures in urban building clusters.

Shaking table tests of a switching power cabinet considering physical damage and functionality loss

Switching power cabinets are critical equipment that converts alternating current (AC) to direct current (DC) for electrical power system of telecommunication buildings. Post-earthquake damage to the cabinet may result in entire functionality loss of telecommunication buildings, which will have a significant impact on post-earthquake emergency response. In this study, unidirectional shaking table tests were conducted separately in X-direction and Y-direction to evaluate the seismic responses of a switching power cabinet. The seismic input motion was generated based on required response spectrum of YD 5083 code. The peak table acceleration levels were scaled from 0.3 g to 2.5 g in order to capture progressive damage of the switching power cabinet. Based on the test results, the physical damage and functionality loss of tested cabinet were analyzed first. Then the response characteristics of the cabinet were evaluated and explained in terms of the natural frequency, acceleration, and deformation response. Both outside cabinet and in-cabinet acceleration amplification factors were analyzed. The seismic fragility curves of switching power cabinet were also developed considering four observed seismic damage states.

Failure modes-based seismic fragility analysis of concrete gravity dams considering concrete heterogeneity

The heterogeneity of mass concrete often leads to the initiation of micro-cracks in gravity dams, while the accumulation of damage of concrete on the meso-scale leads to the cracking behaviour on the macro-scale. To improve the seismic safety of the dam, a comprehensive study on the effect of heterogeneous properties is crucial in seismic fragility analysis. A novel methodology considering concrete heterogeneity for seismic fragility analysis was developed by combining damage element model for seismic failure analysis, a five-level standard for failure modes-based seismic damage state and a method to generate seismic fragility curves. Then, seismic failure analysis of the Koyna gravity dam was taken as an example to verify the correctness of the damage element model, the damage process of dam from micro-cracks to macro-cracks were simulated. Finally, the seismic fragility of a practical dam in China was studied to verify the effectiveness of the proposed methodology. The results showed that simulated failure modes-based fragility curves were suitable for the target dam and more capable of considering the uncertainty of concrete material parameters. The obtained fragility curve proved valuable in optimizing seismic design, reinforcement and maintenance measures and improve seismic capability of the dam.

Rapid damage state classification for underground box tunnels using machine learning

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

Influence of corrosion on failure modes and lifetime seismic vulnerability assessment of low‐ductility RC frames

AbstractCorrosion of reinforced concrete (RC) structures constitutes a critical form of environmental deterioration and may significantly increase the vulnerability of old non‐seismically designed buildings during earthquake events. This study proposes a probabilistic framework to evaluate the influence of corrosion deterioration on the lifetime seismic fragility of low‐ductility RC frame buildings. In contrast to limited past literature on this topic, the proposed framework offers novel contributions. This is one of the first study to consider potential alteration in failure modes of building components (from flexure to flexure‐shear) due to the time‐dependent aging process. Numerical models validated with past experimental test results are utilized to capture these failure modes, which are particularly relevant for low ductility RC frames designed prior to the introduction of modern seismic codes. Secondly, given the gamut of uncertainties associated with the corrosion process, this study develops condition‐dependent seismic fragility functions independent from an assumed exposure scenario, as often done in literature. These functions can be easily adopted by design engineers and stakeholders for prompt fragility assessment, and subsequent decision‐making without the need for computationally expensive finite element (FE) model runs. The proposed framework is demonstrated on a benchmark three‐story RC frame that considers time‐varying seismic demand models and damage state thresholds while accounting for the uncertain corrosion deterioration process and ground motion record‐to‐record variability.

Seismic damage rates of buildings considering different repair policies

An approach is proposed for assessing the mean number of times the maximum interstory drift (ID) incurs undesirable seismic damage states annually. The formulation is generalized and can be transformed into a classical formulation to evaluate seismic damage rates while considering structural repairs after sustaining any degree of damage. The formulation adopts a repair policy, which is expressed in terms of an ID threshold, that correlates with a certain level of damage. If this threshold is exceeded by a single earthquake or by a cumulative damage process due to an earthquake sequence, the building is repaired or reconstructed. The damage rates of the structural response of reinforced concrete buildings located in the Valley of Mexico were analyzed in a case study. Three repair policies were considered. The damage rates were found to be associated to an adopted repair policy.

Seismic Damage “Semaphore” Based on the Fundamental Period Variation: A Probabilistic Seismic Demand Assessment of Steel Moment-Resisting Frames

During strong earthquakes, structural damage usually occurs, resulting in a degradation of the overall stiffness of the affected structures. This degradation produces a modification in the dynamic properties of the structures, for instance, in the fundamental period of vibration (T1). Hence, the variation of T1 could be used as an indicator of seismic structural damage. In this article, a seismic damage assessment in four generic typologies of steel buildings was carried focused on verifying the variation of T1. To do so, several seismic damage states were calculated using the maximum inter-story drift ratio, MIDR, and following the Risk-UE guidelines. Then, a series of probabilistic nonlinear static analyses was implemented using Monte Carlo simulations. The probabilistic approach allows one to vary the main mechanical properties of the buildings, thus analyzing in this research 4000 buildings (1000 building samples for each of the four generic typologies). The variation of T1 was estimated using the capacity spectrum, and it was related to the MIDR for each damage state. As a main result of this study, the expected variation of T1 for several damage states is provided. Finally, a proposal for a seismic damage preventive “semaphore” and fragility curves are presented. These results may be useful as parameters or criteria in the evaluation of on-site structural monitoring for steel buildings.

Attention mechanism based neural networks for structural post-earthquake damage state prediction and rapid fragility analysis

This paper is devoted to the research on applying the deep learning method to nonlinear structural post-disaster damage state assessment. Transformer and Informer networks with a classification network customized according to the adopted damage assessment framework are proposed for data-driven structural seismic response and damage state modeling. Compared with recurrent neural network and convolution neural network, the networks in this paper can predict the elastoplastic response of nonlinear structures more effectively. In addition, this paper presents a method for rapid structural fragility analysis, which can consider multiple damage assessment indexes at the same time. The performance of the proposed approach is successfully demonstrated through two examples, including a numerical analysis validation and a field sensing validation. The results show that the Transformer network used in this paper is a reliable and computationally efficient approach for predicting the structural seismic response and damage category, and appears great potential in structural health monitoring and rapid assessment on post-disaster structural resilience.

Multiple earthquake-induced progressive failure of bedding slopes with a saturated weak layer: Shaking table model tests

The Longmenshan (LMS) orogenic belt located in the south-eastern Tibetan Plateau has suffered from frequent earthquakes. Despite the fact that multiple earthquakes can accelerate the catastrophic failure of deep-seated bedding landslides by causing rock mass damage accumulation, the seismic deformation evolution characteristics during the progressive failure have received limited attention. This study employs a physical model of deep-seated bedding landslides with a saturated weak layer as a slope prototype to conduct model tests that simulate the progressive failure induced by repeated earthquakes. Embedded sensors and digital image correlation (DIC) technology were used to monitor the seismic response and deformation evolution of the physical model. During the test, sensors embedded in the saturated weak layer recorded the excess pore water pressure (EPWP). The test results revealed that the EPWP responses are highly related to the input parameters of seismic waves and the seismic damage state of the weak layer. In addition, a deformation coefficient (Dc) was proposed to identify the deformation stages consisting of the release of steep tensile failure surface and the shear failure evolution from stable to accelerated deformation. Finally, the test results indicated that historical seismic damage accumulation will make an important contribution to later earthquake-induced coseismic instability, which provide new insight into the initiation of large landslides triggered by low magnitude earthquakes.

A hybrid deep belief network-based label distribution learning system for seismic damage estimation of liquid storage tanks

Liquid storage tanks play a vital role in the modern chemical process industry (CPI). The strong ground motion caused by large-scale earthquakes may easily impose severe structural damage on liquid storage tanks, leading to a series of catastrophic cascaded events. The seismic damage estimation of liquid storage tanks is a challenging problem, as the fluid-structure interaction exhibits extremely complicated and non-stationary response behavior. This study develops a novel data-driven methodology to estimate the seismic damage state probability distribution of liquid storage tanks in the contexts of label ambiguity and data imbalance. With the support of the advanced deep learning framework, synthetic oversampling methods, and label enhancement techniques, a hybrid deep belief network-based label distribution learning system (HDBN-LDLS) is proposed for probability distribution learning. The proposed HDBN-LDLS is evaluated on the widely used ALA database. Simulation results indicate that HDBN-LDLS can achieve a balanced estimation for all damage states while maintaining sufficient robustness to cope with label ambiguity. The reliability of the obtained data-driven model is validated by a damaged tank in the 2006 Silakhor earthquake. For practical applications, a more natural way to estimate a seismic damaged tank is to assign a membership degree to each possible damage state. The proposed methodology can quickly obtain the seismic damage state probability curves of a specific liquid storage tank, which can be used to support quantitative risk assessment and seismic design.

Experimental Evaluation of the Seismic Performance and Engineering Damage State of Reinforced Concrete Columns

Experimental Evaluation of the Seismic Performance and Engineering Damage State of Reinforced Concrete Columns

Seismic Response and Damage Analysis of Shield Tunnel with Lateral Karst Cavity under Oblique SV Waves

The dynamic effects of the lateral karst cavity on the shield tunnels under different incident angles of seismic waves are investigated by numerical analysis in this paper, based on the Dalian Metro Line 5 project. The viscous-spring artificial boundary is applied and verified to guarantee the accuracy of seismic input. A simplified finite element model of shield tunnel is established based on the equivalent bending stiffness model. This paper compares the seismic response characteristics and damage states of the tunnel under different incident angles by analyzing the axial deformation, stress distribution, and damage severity, respectively. A new damage state classification criterion is proposed by introducing the relationship between cracks and tensile damage. The results show that the tunnel’s affected scope by the lateral karst cavity is twice the cavity diameter. As the incident angle increases, the tunnel’s displacement and stress increase and show the structural spatial difference, and the tunnel’s damage state is increasingly severe. The displacement and stress reach the max values when the incident angle is 30°. The cracks along the axial direction extend on the outer surface of the vault and bottom, and the crack width is greater than 0.2 mm, as that angle is 30°. The damage severity at the tunnel’s central zone is minimum along axial directions during seismic action, while the damage concentration occurs on the bottom at the end of seismic. The lateral karst cavity plays an energy dissipation and vibration reduction role to a certain extent, but it also aggravates the local damage. This paper can serve as a reference for the seismic design of tunnels in karst regions.

Numerical evaluation of seismically retrofitted bridge concrete column under extreme loading

AbstractColumns have the most important role for overall stability of bridges and these elements are the most vulnerable structural components under extreme loading too. Therefore, studying and improving the performance of these elements under such loads has attracted widespread attention among researchers. The main objective of this paper is investigating the performance of bridge reinforced concrete columns which seismically retrofitted by steel or CFRP jacketing under blast load via finite element method. The finite element models' assumptions in Abaqus FE software are verified using the correlated experimental data from literature and good agreements have been obtained. Parametric studies are conducted to cover a wide range of factors including transverse and longitudinal reinforcement ratios, blast load intensity, type and thickness of conventional seismic retrofitting jacket. The results reveal that the longitudinal reinforcement ratio presents more effects on decreasing extreme loading demands than the transverse reinforcing. Moreover, utilizing seismically retrofitting jacket, especially CFRP jacket, tend to be an effective way to reduce demands of reinforced concrete column under extreme loading, where its effectiveness increases, in case of more severe events. Also, the current seismic damage state and performance level, can predict the damage level of columns in good agreement with the design specifications.

Seismic response and damage evaluation for anchored and unanchored cylindrical above ground steel tanks

This paper aims to first present a review of studies on numerical modeling and seismic response analysis of above ground steel liquid storage tanks. On that basis, a procedure for estimating dynamic parameters associated with simplified models for anchored and unanchored conditions together with calculation methods of seismic responses and damage states of tanks are presented. In which, the nonlinear behavior of the bottom plate in the case of unanchored conditions caused by sliding and uplift phenomena is properly modeled based on the nonlinear static pushover analysis on a 3D finite element model. Finally, an example of the numerical modeling and seismic response analysis of a water tank is presented. The seismic responses and damage of both anchored and unanchored conditions are compared and evaluated in detail.

Rapid seismic damage state assessment of RC frames using machine learning methods

A rapid seismic damage state assessment of individual building is essential for a region-scale risk and vulnerability assessment that requires significant manpower, time, and computational efforts. In this study, three machine learning (ML) algorithms that exhibited high predictive accuracy in previous studies, namely random forest (RF), extremely gradient boosting (XGB), and active machine learning (AL) were used to develop models for rapidly assessing the seismic damage states of reinforced concrete (RC) frames after an earthquake. Compared to RF and XGB, the active machine learning develops an efficient model with a small number of instances by interactively selecting the valuable instances for desired outputs. Using these aforementioned algorithms, three predictive models were developed, tested, and validated using a comprehensive dataset which included a total of 9900 data points. The dataset was developed according to a non-linear time history analysis involving a combination of 199 RC frames and 50 ground motions. The results indicated that active machine learning predicted the damage states of RC frames with an accuracy of 84% in the testing dataset, followed by the XGB algorithm with an accuracy of 80%. These predictive models were also validated using actual damaged buildings in the Taiwan earthquake. Seismic design intensity (SDI) and spectrum intensity (SI) were the most important input features in the damage states of RC frames, with a relative importance factor exceeding 50% for the two features. Constructed periods have a non-negligible influence on the damage states of RC frames when these differ for regional buildings. Finally, an interactive and user-friendly graphical user interface (GUI) platform was created to provide a rapid seismic damage state assessment of RC frames. This study represents a pioneering step toward the application of AL in damage state assessment of existing RC frames.

Story-wise assessment of seismic behavior and fragility analysis of R/C frames considering the effect of masonry infills

The present paper investigates the story-wise damage response and the seismic fragility of planar multistory R/C buildings with various masonry infills' distributions. In order to fulfil this purpose, six planar R/C buildings (three moment frames and three dual structural systems consisting of frames and walls) are studied. For each structure several different masonry infills' distributions are taken into account. Then, the buildings are subjected to a set of appropriately chosen real earthquake records for which nonlinear time history analyses are conducted and the story-wise IDA curves for each building and infill distribution are constructed. The quantification of the expected seismic damage state is accomplished through the calculation of the maximum interstory drift ratio. Subsequently, based on the results of the IDA curves, the fragilities of each buildings' story as well as of the whole buildings are calculated. The results of this study demonstrate the role of masonry infills on the story-wise seismic damage of R/C buildings. The presence of the masonry infills cannot be ignored, since they can signifficantly modify the damage response and the seismic fragility of buildings depending on the structural system and on the infills' distribution.

Overall structural seismic damage rapid assessment method based on period and displacement response characteristics

The seismic damage state of building structure can be rapidly evaluated by coupling effect of structural displacement response and periodic characteristics. Firstly, the fundamental period calculation formula that adapts to the deformation pattern and distribution mode of horizontal seismic action for reinforced concrete frame structure is derived. Secondly, the seismic damage assessment standard of building structure considering period variation is established. Then, the seismic damage assessment method of building structure is constructed. Finally, the seismic damage example is used to verify the established evaluation method. The results show that the established research method has high accuracy and good engineering practicability.