Post-earthquake Assessment Research Articles

Exploring Key Capacities: Insights from Assessing the Resilience of the Public Health System Before and After the Kahramanmaraş Earthquakes

AbstractThe goal of this mixed-methods study was to identify and compare the key capacity considerations regarding public health system resilience before and after the 2023 Türkiye-Syria earthquakes. Public health system resilience was assessed through online and face-to-face workshops using the United Nations Public Health System Resilience Scorecard. The pre-earthquake evaluation was conducted in Istanbul and Trabzon in 2021; the post-earthquake evaluation took place in Hatay and Kahramanmaraş in 2023, with a total of 41 participants each. The online workshops lasted approximately four days, while the face-to-face workshops lasted one day. The study found a significant decrease in the scores for most scorecard resilience indicators in the post-earthquake assessment. Qualitative analysis showed that this decline was largely due to infrastructure collapse. Additionally, defining the roles of health disciplines in disaster management and having data transmission procedures between public health system stakeholders in disasters were among the main capacity considerations in both the pre-and post-earthquake assessments. The post-earthquake evaluation revealed several capacity gaps that had not been addressed in pre-earthquake assessments in areas such as disaster preparedness of vulnerable populations and logistics. The findings highlight the critical importance of strengthening building stock and infrastructure to establish a disaster-resilient public health system.

Study of Earthquake Landslide Hazard by Defining Potential Landslide Thickness Using Excess Topography: A Case Study of the 2014 Ludian Earthquake Area, China

Influenced by the combined effects of crustal uplift and river downcutting, rivers with significant potential energy are often found in high mountain and canyon areas. Due to the active tectonic movements that these areas have experienced or are currently experiencing, geological hazards frequently occur on the mountains flanking the rivers. Therefore, evaluating the susceptibility and risk of earthquake landslides in river segments of these high mountain and canyon areas is of great importance for disaster prevention and mitigation, as well as for the safe construction and operation of hydropower stations. Currently, a major challenge in the study of landslide susceptibility and hazard is determining the thickness of potential landslide bodies. The presence of excess topography reflects the instability of the disrupted slopes, which is also a fundamental cause of landslides. This study takes the example of the Ludian earthquake in 2014, focusing on the IX and VIII intensity zones, to extract the excess topography in the study area and analyze its correlation with seismic landslides. The correlation between the critical acceleration value and the excess topography was validated using the Spearman’s rank correlation coefficient, resulting in a correlation coefficient of −0.771. This indicates a strong negative correlation between the excess topography and critical acceleration, with significant relevance. The landslide susceptibility distribution obtained by setting the potential landslide thickness based on the excess topography and proportion coefficient showed an ROC curve analysis AUC value of 0.829. This is higher than the AUC value of 0.755 for the landslide susceptibility result using a uniform potential landslide thickness of 3 m, indicating the higher model evaluation accuracy of this approach. Earthquake landslide hazard predictions for rapid post-earthquake assessments and earthquake landslide hazard zoning for pre-earthquake planning were made using actual seismic ground motion and a 2% exceedance probability in 50 years, respectively. Comparing these with the 10,559 coseismic landslides triggered by the Ludian earthquake and evaluating the seismic landslide development rate, the results were found to be consistent with reality. The improved model better reflects the control of excess topography and rock mechanics properties on the development of earthquake landslide hazards on high steep slopes. Identifying high-risk seismic landslide areas through this method and taking corresponding preventive and protective measures can help plan and construct safer hydropower and other infrastructure, thereby enhancing their disaster resistance.

Incorporating multi-source remote sensing in the detection of earthquake-damaged buildings based on logistic regression modelling

After an earthquake, efficiently and accurately acquiring information about damaged buildings can help reduce casualties. Earth observation data have been widely used to map affected areas after earthquakes. However, fine post-earthquake assessment results are needed to manage recovery and reconstruction and to estimate economic losses. In this paper, for quantification and precision purposes, a method of earthquake-induced building damage information extraction incorporating multi-source remote sensing data is proposed. The method consists of three steps： (1) Analysis of multisource features that describe texture, colour, and geometry, (2) rough set theory is carried out to further determine the feature parameters, (3) Logistic regression model (LRM) was built to describe the relationship between the occurrence and absence of destroyed buildings within an individual object. Old Beichuan County (centered at approximately 31.833︒N, 104.459° E), China, the area most devastated by the Wenchuan earthquake on May 12, 2008, is used to test the proposed hypothesis. Multi-source remote sensing imagery include optical data, synthetic aperture radar (SAR) data, and digital surface model (DSM) data generated by interpolating light detection and ranging (LiDAR) point cloud data. Through comparison with the ground survey, the experimental results show that the detection accuracy of the proposed method is 94.2 %; the area under the receiver operating characteristic (ROC) curve is 0.827. The efficiency of the proposed method is demonstrated using 6 modes of data combination acquired from the same area in old Beichuan County. The approach is one of the first attempts to extract damaged buildings through the fusion of three types of data with different features. The approach addresses multivariate regression methodologies and compares the potential of features for application in the damage detection field.

Cascaded framework for earthquake building damage detection combining spatial and frequency domain feature integration

Abstract. Building collapse is a major cause of casualties after an earthquake, so accurately extracting building damage information is critical for post-earthquake assessment and rescue. Currently, most deep learning methods focus on the end-to-end detection of building collapse. However, in real-world earthquake scenarios, the end-to-end computational process often lacks flexibility and struggles to meet the requirements of rapid emergency response. To address this issue, this paper proposes a cascaded framework that combines pre-earthquake building extraction and post-earthquake building damage classification. The proposed framework includes two sections: (1) Progressive building semantic segmentation model in the joint frequency domain. This model is designed to accurately extract buildings prior to an earthquake, with the goal of minimizing error propagation throughout the cascading process. The model addresses the spatial similarity of buildings under complicated backgrounds, as well as the high internal heterogeneity of buildings, by utilizing frequency domain techniques. It compensates for the shortcomings of traditional models in terms of incomplete information extraction through the effective integration of global and local information. Finally, the model employs edge priors for edge regularization. (2) Rapid building damage classification process. Based on the accurate building extraction results, a fast and efficient classification process is developed. This process uses a simple and lightweight classification network to effectively extract building damage information caused by the earthquake. The superiority of the proposed framework is validated through comparison with traditional cascading architectures and end-to-end models. The results show that the cascading framework not only provides accurate pre-earthquake building extraction, but also enables efficient and accurate post-earthquake damage classification, which meets the requirements of rapid post-earthquake emergency response. This balance of accuracy and speed is essential for effective disaster management and recovery.

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved.

A spectral assessment for instant preliminary evaluation of structures after seismic events

This article introduces a simple, efficient spectral-based approach for rapid preliminary seismic assessment of earthquake-affected structures. Performance is validated using data from three highly earthquake-affected structures in New Zealand, where visual inspection with subjective outcomes missed damage, resulting in inaccurate and delayed decisions with significant social and economic losses. The three structures considered include (1) the instrumented Bank of New Zealand (BNZ) building; (2) the un-instrumented Queensgate Mall (QM) Complex; and (3) the un-instrumented Canterbury Television (CTV) building. This study uses these cases to highlight the importance of structural health monitoring (SHM) instrumentation and reliable quantified post-earthquake assessment methods in earthquake-prone areas, where each damaging earthquake and subsequent further-damaging aftershocks demand continuous monitoring to continuously assess damage and life safety risk. The simple, low-cost spectral analyses in this study clearly show the existence of damage and deterioration not fully discovered with standard visual inspection methods. This outcome highlights the importance of sensor networks and SHM instrumentation so quantitative, post-event analysis can rapidly augment and target further, more subjective visual inspection results.

Experimental Investigations on the Residual Performance of Earthquake-Damaged Low-Rise-Reinforced Concrete Shear Walls

This study investigates the seismic residual performance of reinforced concrete (RC) shear walls with different levels of earthquake damage through reversed cyclic loading tests. The test specimens include slightly damaged walls, moderately damaged walls, and repaired specimens after moderately damaged walls. From the test and analysis results, it is evident that the residual strength and deformation capacity of slightly and moderately damaged RC shear walls do not decrease compared with the undamaged wall. However, the residual stiffness and energy dissipation capacity of damaged walls significantly decrease. The test results are also compared with the reduction factors for residual stiffness, strength, deformation capacity, and energy dissipation capacity in the FEMA 306 and JBDPA post-earthquake assessment guidelines to assess their applicability. Additionally, this study proposes a simplified model to describe residual lateral capacity curves of damaged RC walls at different damage levels for nonlinear pushover analysis, allowing engineers to easily convert the capacity curves of undamaged walls to damaged ones.

Monitoring the changes in the dynamic properties of an RC building experiencing column loss

In this study, an existing six-storey reinforced concrete building with an asymmetric structural plan and soft storey irregularity was used as a test specimen and subjected to three-step progressive structural damages to detect the variations in its dynamic properties. Mode shapes and dominant frequencies of the undamaged building were determined by the ambient vibration survey (AVS) and it was seen that its first three modes were torsion coupled. Besides, soft storey irregularity was evident due to the lack of masonry infill walls on its ground floor. Later on, three-step progressive damages were applied to the building. The first step targeted three columns and one beam of the building, located on a corner region of its ground floor to peel off their clear covers. The second step razed two adjacent corner columns which were already moderately damaged in the first step, while the third step knocked the third moderately damaged column down. After each damage step, AVS was repeated with the same details as applied for the undamaged building. The obtained dynamic properties for the four phases of the building were evaluated with the sustained damage. Numerical analyses with the finite element model of the building representing its four different phases were also performed and the unique responses due to damage effects on the structure were investigated numerically. As a result of induced damage, the quantified amount of frequency change in modes and the new mode observed after particularly column loss scenarios can be utilized for efficient structural health-monitoring strategies of plan-asymmetric buildings and post-earthquake assessment of partially damaged buildings where timely objective assessment is important.

A hybrid model-data method for seismic response reconstruction of instrumented buildings

This study presents a two-step hybrid (model-data fusion) method for reconstructing the seismic response of instrumented buildings at their non-instrumented floors. Over the past couple of decades, seismic data recorded within instrumented buildings have yielded invaluable insights into the behavior of civil structures, which were arguably impossible to obtain through numerical simulations, laboratory-scale experiments, or even in-situ testing. Recently, advances in sensing technology have opened new pathways for structural health monitoring (SHM) and rapid post-earthquake assessment. However, data-driven techniques tend to lack accuracy when structures have sparse instrumentation. In addition, creating detailed numerical models for the monitored structures is labor-intensive and time-consuming, often unsuitable for rapid post-event assessments. The common approach to address these challenges has been to use simple interpolation techniques over the sparse measurements. However, uncertainties associated with such estimates are usually overlooked, and these methods have certain physical limitations. In this study, we propose a two-step approach for reconstructing seismic responses. In the initial step, a coupled shear–flexural beam model is calibrated using data collected from instrumented floors. Next, the residual, representing the difference between measurements and the beam model’s predictions, is used to train a Gaussian process regression model. The combination of these two models provides both the mean and variance of the response at the non-instrumented floors. This new approach is verified by using simulated acceleration responses of a tall building. Validation is attained by using real seismic data recorded in two tall buildings and comparing the method’s predictions with actual measurements on floors not used for training. Finally, data recorded in a 52-story building during multiple earthquakes are used for demonstrating the practical application of the proposed approach in real-world scenarios.

Bidirectional graphics-based digital twin framework for quantifying seismic damage of structures using deep learning networks

Tremendous effort has been devoted toward developing automated post-earthquake inspection techniques, including automated image collection and damage identification. However, few studies have attempted to establish the complex relationship between visible damage and structural conditions. Moreover, the lack of training data further hinders the potential use of deep learning algorithms. This paper proposes a framework, termed Bidirectional Graphics-based Digital Twin (Bi-GBDT), that allows for assessment of the structural condition based on post-earthquake photographic surveys. The procedure contains two parts: (i) A GBDT of a target structure, containing a computer graphics model and a finite element (FE) model, is developed (ii) Synthetic data subsequently is generated from the GBDT to train neural networks to predict the damage measures and structural conditions. To demonstrate the proposed approach, a seismically-designed reinforced concrete shear wall is considered. First, the GBDT of the shear wall is constructed and calibrated so that the associated damage patterns simulated by the corresponding FE model match well with the experimental results. Next, synthetic images of the damage patterns are created from the validated GBDT, along with the corresponding structural damage measures, and used to train Residual Neural Network and Conditional Generative Adversarial Networks to determine the maximum drift, the stress/strain fields and the structural condition. The neural networks trained on synthetic data are shown to perform well for the experimental data, confirming the proposed approach. Subsequently, the neural networks are tested on the synthetic data for a wide variety of loading conditions to demonstrate the robustness of the approach. In addition, the realistic images rendered from the GBDT are utilized as input to predict the structural condition, showcasing the comprehensive Bi-GBDT framework. These results demonstrate the efficacy of the proposed approach to generate accurate digital twins and point toward its future application for development of automated post-earthquake assessment strategies.

Constant damage inelastic permanent period shift ratios spectra

The phenomenon of elongation of the structural period after earthquake excitation has been widely observed, and elongated period obtained after earthquake excitations can be used for post-earthquake assessment. The inelastic permanent period shift ratio, an indicator representing the degree of stiffness degradation and closely relating to the damage state, is defined as the ratio of the elongated period Tin of single-degree-of-freedom (SDOF) systems after earthquake excitations to the initial period Tel. The objective of this paper is to investigate the inelastic permanent period shift ratios under constant damage performances through nonlinear time-history analysis of SDOF systems, and the modified Park-Ang damage index is employed to characterize the structural damage. Inelastic permanent period shift ratio spectra are statistically constructed considering both the ground motion characteristics (such as site conditions, velocity pulse, magnitude, closet distance, directivity effects, significant duration and frequency contents) and structural parameters (including damage states, hysteretic models, ultimate deformation capacity, energy dissipation factor and postyield ratio). The results showed that the permanent period shift ratio is a reliable damage indicator for assessment of global damage states. The influence caused by ground motions on the estimation of the mean of the permanent period elongation ratio under constant damage performances is limited, while the effects caused by different structural damage and deformation capacity can reach up to approximately 70% and 33%, respectively. An expression for the relationship between the damage index and the permanent period shift ratio is established. Ignoring the differences of structural performances, using the period shift ratios to directly assess the global damage states will lead to erroneous results.

A digital tool based on code indicators to assess the seismic behaviour of existing masonry structures: Application to Croatian residential buildings

This paper proposes a methodology to define code-based indicators of the seismic behaviour of existing masonry structures. Given the geometrical information about the structure under investigation, the proposed method performs real-time structural checks according to the EN 1998–1 – “General rules, seismic actions and rules for buildings”. Then, modal analysis is performed to assess the structure's dynamic behaviour under investigation and to assist the user in defining retrofit measures. In this study, 103 masonry buildings located in Zagreb are analysed, accounting for the cross-sectional area of load-bearing walls, geometric requirements for shear walls, regularity of plan configuration and its symmetry, torsional effects induced by the distance of the centres of mass and stiffnesses, and modal analysis. The workflow is implemented into the environment offered by Rhino3D and Grasshopper, which allows for the parametric handle of a large set of information through object-oriented scripts, and it can be valuable for both pre-earthquake and post-earthquake assessments of masonry buildings.

Post-Earthquake Rapid Assessment Method for Electrical Function of Equipment in Substations

To assess the electrical function of equipment in substations after earthquakes, a rapid assessment method integrating electrical function conversion relation, numerical simulation and map proxy model was introduced. An intermediate mechanical index (IMI) directly related to electrical function could be defined through various theoretical analyses. And a simulation model of equipment is developed for obtaining seismic response data of IMI and easily monitored mechanical indices (EMI). Afterward, a composite map, specifically from the EMI to electrical function, could be formed mathematically by combining the aforementioned conversion and simulation model. Using the response data as training samples, the map would be represented by a proxy model embedded individual or ensemble machine learning algorithms. Once the actual seismic responses are collected by monitoring during earthquakes, the post-earthquake assessment would be conducted based on the input–output operation of the map proxy model. Finally, a practical case was studied using the approach for the electrical contact status of a grid-side bushing installed on a transformer. The error analysis corroborated the accuracy and usefulness of this proposed method. It can be employed analogously for post-earthquake assessments of other equipment and electrical functions, although only one example has validated the feasibility in this paper.

Post-Earthquake Assessment for Seismic Risk Mitigation in Romania: Case-Studies Based on Recorded Data

"The seismic recordings from certain types of buildings in urban areas during moderate earthquakes from Vrancea-intermediate depth focal region are processed and analysed. One of the most important element in hazard evaluation at national level is the seismic recording network which has been upgraded in the last years up to over 20 strong motion accelerometers in Bucharest and 135 in Romania with easy data handling (accelerations easy to transform in displacements). The general behaviour of reinforced concrete buildings in Bucharest Metropolis during destructive seismic events from Vrancea-intermediate depth focal region is briefly described. The analysis of the elastic response spectra is made for four buildings subjected to the two recent earthquakes with magnitude higher than 4.8, of October 28, 2018 and January 31, 2020 from Vranceaintermediate depth seismic zone. The information consisting in maximum level of the pseudo-acceleration to which the buildings were subjected may be used to detect and quantify any exceedance of the code spectra of interest in near real-time. The recorded earthquakes data are transmitted in real time to the National Data Centre. The goal is to develop and implement modern techniques and tools able to estimate the effect of earthquakes on the built environment in the shortest time possible after a major earthquake. "

Performance-based damage quantification and evaluation for self-centering concrete frames

Self-centering concrete frames (SCCFs) are designed to provide superior seismic capacities with resilient features. Although a well-designed SCCF can limit damage development, it still impairs post-earthquake performance and shall be considered properly. Quantifying and evaluating structural damage can provide valuable guidance for seismic designs and post-earthquake assessments, yet related studies towards SCCFs are still limited. This paper aims to conduct damage quantification and evaluation for SCCFs. The damage development and distribution within SCCFs are investigated. A modified damage model is adopted to quantify the damage developed in beam-column joints. Weighting coefficients based on the hysteretic energy demands are introduced to assess story and integral structural damage. 12 SCCFs with different structural features are designed and analyzed under 44 ground motions. The damage development within structures is quantified and evaluated using the performance-based damage intervals. The results show that damage developed in SCCFs exhibit stable growth with seismic intensities, implying superiorities in the damage controllability and stability. Meanwhile, increasing the self-centering parameter λ proves effective in reducing damage developed in SCCFs. A method to estimate integral structural damage is proposed, while proper design parameters are suggested to further assist seismic designs for SCCFs based on analytical results.

An expeditious framework for assessing the seismic resilience (SR) of structural configurations

The assessment of the Seismic Resilience (SR) has become a fundamental issue in Structural Engineering to ensure rapid and efficient recovery to earthquake scenarios. Even if there are methodologies that assess SR, most of them are significantly time-consuming. In this paper, a framework for simplified assessments of SR is proposed based on the existing literature. The probability-based method of fragility curves is applied to estimate the losses and a novel definition of the recovery model is herein proposed. A case study is discussed to apply the proposed framework to two configurations: a fixed-base and a base-isolated structural system. The state-of-the-art platform Opensees has been applied to calculate the non-linear performance of the structures. The longitudinal drift ratio was chosen as the representative parameter to develop fragility curves and thus to quantify SR. The response of the 200 analyses (100 for each system) shows the benefit of applying base isolation technique in terms of SR, used as a comprehensive parameter to compare the two solutions. The proposed approach may be helpful for many stakeholders and decision makers for pre- and post-earthquake assessments.

Post-earthquake assessment of monumental building in Croatia by 3Muri software

Abstract. A series of earthquakes in and close to city of Zagreb, Croatia during the 2020 severely damaged the masonry cultural heritage buildings built in the 19th century. The article presents the assessment of post-earthquake design of retrofitting of damaged historic masonry structure of the former synagogue in city of Sisak, Croatia erected in 1890. The former synagogue was severely damaged during the recent earthquake. For the purposes of the post-earthquake retrofitting and strengthening, an analysis of the damaged and retrofitted structure has been carried out to justify the selected retrofitting measures that included a partial reconstruction of heavily damaged structure. The equivalent frame model based on the discretization in terms of piers and spandrels was created (SEM - Structural Elements Model) to obtain the simulation of earthquake response of structure using the 3Muri 13.9 software. In this paper the procedure and results of the analysis of seismic resistance of the strengthened structure is presented because of the successful cooperation of structural engineers, architects and conservators.

Post-earthquake seismic assessment of residential buildings following Sarpol-e Zahab (Iran) earthquake (Mw7.3) – Part 2: Seismic vulnerability curves using quantitative damage index

One of the most crucial steps in disaster management and decision-making is a quick and reliable seismic evaluation of structures following an earthquake. Vulnerability curves, which relate the earthquake intensity measure to damage states, are key terms in performing post-earthquake assessments. In most previous research, empirical vulnerability curves were roughly developed using qualitative criteria. Current research proposes a novel damage index based on the suggested damage states resulting from field investigation of more than 81 damaged steel and RC buildings after the Sarpol-e Zahab (Iran) earthquake. The proposed damage index provides relatively precise information about damaged buildings. Then, using the spectral acceleration derived from the conditional ground motion intensity and the proposed damage index, empirical vulnerability curves are generated for RC and steel buildings and non-structural walls. The developed empirical vulnerability curves are valuable due to the lack of similar field studies on damaged buildings under severe earthquakes and are consistent with common practice of construction in Iran. Past earthquakes' data can be used in seismic risk assessment and planning for mitigation of losses in the future.

Machine learning-based collapse prediction for post-earthquake damaged RC columns under subsequent earthquakes

The rapid assessment for numerous post-earthquake damaged reinforced concrete (RC) structures is critical for making informed post-earthquake decisions. However, this often requires significant resources in terms of manpower and calculation costs. This study proposes a machine learning (ML)-based method for predicting the collapse state of post-earthquake damaged RC columns under subsequent earthquakes, which are a critical earthquake-resistant component in RC structures. First, the damage strain field theory is introduced to establish the numerical analysis models for 10000 post-earthquake damaged RC columns. A dataset is constructed using time history analysis results obtained from these numerical analysis models under the action of 1000 ground motion records. The Random Forest (RF) algorithm is then utilized to train an ML model that can predict the collapse state of post-earthquake damaged RC columns under subsequent earthquakes. To evaluate the accuracy of the ML prediction model, a testing dataset consisting of 47 full-size RC columns from the Pacific Earthquake Engineering Research Center (PEER) database is constructed. Results demonstrate that the prediction model has good accuracy, with an Area Under Curve (AUC) value and an F1 measure of 0.87 and 0.76, respectively. The SHapley Additive exPlanation (SHAP) algorithm is then utilized to interpret the ML prediction model, and the influences of design parameters, ground motion intensity parameters, and damage index on the prediction results are discussed. Finally, an interactive and user-friendly graphical user interface (GUI) tool is developed to provide a rapid collapse prediction of post-earthquake damaged RC columns. This study represents a pioneering step towards the application of ML in post-earthquake assessment for damaged RC columns.

Limit states for post-earthquake assessment and recovery analysis of ductile concrete components

Post-earthquake assessment procedures require component deformation limits to identify locations for visual inspection and locations needing structural repair. This study proposes a framework for defining component deformation limits for detailed visual inspection and repair for earthquake-damaged concrete buildings. First, observations from cyclic tests of ductile concrete components (beams, columns, and walls) suggested that the residual capacity (in terms of strength and deformation capacity) of such components is likely uncompromised if the deformation at the initiation of lateral strength loss (LSL) is not exceeded in prior loading histories. The results also revealed that the deformation at the initiation of LSL typically corresponds to the onset of longitudinal bar buckling in ductile components. Furthermore, using experimental data, multipliers are developed as fractions of ASCE/SEI 41 modeling parameters at lateral failure (i.e. a or d) to predict deformation at initiation of LSL. Subsequently, a probabilistic approach is proposed for defining the component deformation limits, considering uncertainty in both capacity and demand. Component deformation limits for detailed visual inspection are defined such that there is a low probability (adopted as <10%) of exceeding the deformation at the initiation of LSL. The component deformation limit for repair is defined as the median deformation at the initiation of LSL (i.e. 50% probability of exceedance).