Pacific Earthquake Engineering Research Research Articles

The Derivation of Vertical Damping Reduction Factors for the Design and Analysis of Structures Using Acceleration, Velocity, and Displacement Spectra

Damping reduction factors (DRFs) play a vital role in the seismic design of structures. DRFs have been widely studied due to their primary importance to the lateral resistance of structures subjected to earthquakes. On the other hand, devastating earthquakes have occurred all over the world, and recently, the Kahramanmaraş earthquakes in Turkey revealed the import of the vertical component of earthquakes and their impact on structures and infrastructures. Considering the importance of this parameter, this paper aims to develop new damping reduction factor (DRF) equations for the acceleration (DRFa), velocity (DRFv), and displacement spectra (DRFd) of the vertical components of earthquakes. For this purpose, 775 real ground motion records were selected from the Pacific Earthquake Engineering Research (PEER) strong motion database, and the vertical elastic response spectra of selected records were computed according to linear dynamic analysis. Taking the 5%-damped vertical response spectra as the target, the vertical spectral damping reduction factors (DRFa, DRFv, and DRFd) were computed for 1%, 3%, 10%, 15%, 20%, 30%, and 40% damping ratios. The effect of the earthquake magnitude, distance, and soil types on the DRFs was investigated. The results indicated that magnitude, distance, and soil type had no particular effect on the trend in the DRFs. Based on the evaluations, extensive statistical analyses were carried out, and new prediction equations were developed according to the nonlinear regression method. The developed equations were then compared to those found in the literature and seismic design codes. The comparisons proved that the proposed DRFa, DRFd, and DRFv models are strongly compatible with real DRFs and show strong robustness compared to existing models.

Multi-visual modality micro drone-based structural damage detection

Accurate detection and resilience of object detectors in structural damage detection are important in ensuring the continuous use of civil infrastructure. However, achieving robustness in object detectors remains a persistent challenge, impacting their ability to generalize effectively. This study proposes DetectorX, a robust framework for structural damage detection coupled with a micro drone. DetectorX addresses the challenges of object detector robustness by incorporating two innovative modules: a stem block and a spiral pooling technique. The stem block introduces a dynamic visual modality by leveraging the outputs of two Deep Convolutional Neural Network (DCNN) models. The framework employs the proposed event-based reward reinforcement learning to constrain the actions of a parent and child DCNN model leading to a reward. This results in the induction of two dynamic visual modalities alongside the Red, Green, and Blue (RGB) data. This enhancement significantly augments DetectorX's perception and adaptability in diverse environmental situations. Further, a spiral pooling technique, an online image augmentation method, strengthens the framework by increasing feature representations by concatenating spiraled and average/max pooled features. In three extensive experiments: (1) comparative and (2) robustness, which use the Pacific Earthquake Engineering Research Hub ImageNet dataset, and (3) field-experiment, DetectorX performed satisfactorily across varying metrics, including precision (0.88), recall (0.84), average precision (0.91), mean average precision (0.76), and mean average recall (0.73), compared to the competing detectors including You Only Look Once X-medium (YOLOX-m) and others. The study's findings indicate that DetectorX can provide satisfactory results and demonstrate resilience in challenging environments.

The influence of soils of selected sites of Addis Ababa on predicted earthquake ground motions

One-dimensional nonlinear ground response analyses were conducted on selected sites of Addis Ababa. The sites were characterized based on the results of seismic and geotechnical surveys. Input motions were selected from the Pacific Earthquake Engineering Research Center (PEER) ground motion database. The results of the analyses show that the selected sites have a potential amplification significantly higher than the current seismic code specifications. In particular, the average PSA obtained at the ground surface is more than double the code values in the lower period range of up to 1.3 s. Considering that most buildings lie within this range, this calls for a revisiting of the code provisions for such sites of seismic prone areas in the region in general.

Performance-based earthquake early warning for tall buildings

The ShakeAlert Earthquake Early Warning (EEW) system aims to issue an advance warning to residents on the West Coast of the United States seconds before the ground shaking arrives, if the expected ground shaking exceeds a certain threshold. However, residents in tall buildings may experience much greater motion due to the dynamic response of the buildings. Therefore, there is an ongoing effort to extend ShakeAlert to include the contribution of building response to provide a more accurate estimation of the expected shaking intensity for tall buildings. Currently, the supposedly ideal solution of analyzing detailed finite element models of buildings under predicted ground-motion time histories is not theoretically or practically feasible. The authors have recently investigated existing simple methods to estimate peak floor acceleration (PFA) and determined these simple formulas are not practically suitable. Instead, this article explores another approach by extending the Pacific Earthquake Engineering Research Center (PEER) performance-based earthquake engineering (PBEE) to EEW, considering that every component involved in building response prediction is uncertain in the EEW scenario. While this idea is not new and has been proposed by other researchers, it has two shortcomings: (1) the simple beam model used for response prediction is prone to modeling uncertainty, which has not been quantified, and (2) the ground motions used for probabilistic demand models are not suitable for EEW applications. In this article, we address these two issues by incorporating modeling errors into the parameters of the beam model and using a new set of ground motions, respectively. We demonstrate how this approach could practically work using data from a 52-story building in downtown Los Angeles. Using the criteria and thresholds employed by previous researchers, we show that if peak ground acceleration (PGA) is accurately estimated, this approach can predict the expected level of human comfort in tall buildings.

Hybrid Empirical Ground-Motion Models for the Island of Hawaii Based on an Updated Strong Ground-Motion Database

ABSTRACT In this study, we develop ground-motion models (GMMs) for the Island of Hawaii. This area has been the site of several significant earthquake events with a growing database of strong ground-motion observations. Ground-motion modeling on the Island of Hawaii is challenging due to different anelastic attenuation characteristics, the volcanic origin of some of the events, and event depth distribution. Only a few GMMs have been developed for the Island of Hawaii. In this study, we apply a hybrid empirical method (HEM) to develop two separate GMMs for shallow (hypocentral depth ≤ 20 km) and deep (hypocentral depth > 20 km) earthquakes on the Island of Hawaii. We utilize the ratio of the stochastic point-source model in the target and host regions as an appropriate adjustment factor. We apply these adjustment factors to convert the GMMs from the host (western North America) to the target (Island of Hawaii) region. We considered five GMMs proposed in the Next Generation Attenuation Phase 2 project by the Pacific Earthquake Engineering Research Center to model ground motions in the host region. We developed GMMs to predict peak ground acceleration and 5%-damped pseudospectral acceleration at periods T = 0.01–10 s, for moment magnitudes (M) in the range of 3–7.5, and for Joyner–Boore distances in the RJB≤200 km range. The applicability of HEM to develop GMMs for the Island of Hawaii and the growing strong ground-motion data result in further improvements in the capability of GMMs.

Experimental model updating of slope considering spatially varying soil properties and dynamic loading

AbstractThe widespread threat posed by slope structure failures to human lives and property safety is widely acknowledged. Additionally, natural soil often displays spatial variability due to geological deposition and other factors. Therefore, predicting the seismic response of slopes subjected to ground motions and inversely analyzing the spatial distribution of soils remains an unresolved issue. In the present work, a shaking table experimental test is first designed and carried out, in which a soft‐soil slope dynamic system is established. To capture the seismic response of the soft‐soil slope, specifically the experimental characteristic of acceleration and soil pressure response in both spatial domain and time domain, a series of sensors were pre‐embedded in the slope. Subsequently, a model updating approach is proposed for slope seismic analysis that incorporates spatial variability of soil. In addition, to enhance computational efficiency, the dimensionality reduction of Karhunen–Loève expansion method is introduced to reduce inverse analysis parameters. On the basis of 34 samples collected from experimental data, it is shown that near‐fault pulse‐like ground motions deliver greater concentrated energy, causing more severe damage to slope structures, especially the topsoil layer. Furthermore, using data obtained from a shaking table test subjected to ground motion Recorded Sequence Number 158H1 from the Pacific Earthquake Engineering Research Center NGA‐West2 database as an example, it is also shown that the proposed approach demonstrates high accuracy in predicting the spatial distribution of the maximum shear modulus in soil slope dynamic systems. The present work not only addresses the challenges posed by mainshock–aftershock effects but also highlights the potential of model updating approaches to enhance the understanding of slope behavior under seismic loading conditions.

Multi-expression programming based prediction of the seismic capacity of reinforced concrete rectangular columns

This article presents an innovative artificial intelligence based multi-expression programming approach to predict the seismic capacity of reinforced concrete rectangular columns. For the evolution of these multi-expression programming based computational models, an investigational test database created by pacific earthquake engineering research centre was employed. This database consisted of the experimental record of 250 specimens of reinforced concrete rectangular columns exposed to seismic loading. The overall seismic capacity of the aforementioned columns was considered to be the function of their flexural and shear capacities, for the prediction of which five input variables were used. Once these models were evolved utilizing MEPX version 2022.3.19.0-beta, their performance was determined on the basis of ten most frequently used performance indicators, one of which is the coefficient of determination i.e., R2. Based on the findings of this research, a strong correlation (i.e., an R2 of 0.9747 and 0.9664 for the flexural and shear capacity prediction models respectively) was found to exist between the predicted and tested seismic capacities of reinforced concrete rectangular columns. Moreover, the proposed seismic capacity prediction models were found to outperform that of ACI 318-19 in terms of precision and prediction accuracy. The proposed seismic capacity prediction models were also found to be well trained on the correlation between input and output variables which verifies their ability to capture the underlying physical processes involved in the seismic action on reinforced concrete rectangular columns. Therefore, the proposed multi-expression programming based computational models can be confidently recommended for predicting the seismic capacity of reinforced concrete rectangular columns for practical design applications.

Seismic analysis of buildings with basements

This research work analyzes how seismic waves affect buildings with basements that are usually studied independently: In surface structures the inertial forces originated by the movement of their base and in subway structures the deformations due to ground motion are considered. The building located in the city of Quito with twelve elevated floors and five subfloors is studied using nonlinear finite element modeling, considering that the subsoil depends on seismic excitations in a dynamic soil-basement-structure set, for which it is modeled together with its geotechnical components. The superstructure is integrated to the infrastructure and a time-history analysis is included with the most representative earthquakes can affect the building. The present investigation has three basic components: seismic, geotechnical, and structural, which are merged by means of specialized 3D structural engineering programs. In the seismic component, local and regional information about tectonic faults within Ecuador is collected; the building is then located on a seismic risk map and the magnitudes, types, and other parameters of the site where the building is located are determined. Using the results of the Earthquake Risk Assessment Project for the city of Quito (TREQ) and the Pacific Earthquake Engineering Research Center (PEER) database, three relevant seismic events are chosen for the time-history analysis that meet the seismic risk data for the building. For the geotechnical soil component, the geotechnical parameters of the original design of the building foundation, stratigraphy, and characteristics of soil solids, among others, are used. The structural component considers the type of material, geometry, dimensions, joints between elements and others of the original design for both basements and superstructure. The connection of these three components is carried out using Midas Gen and Midas GTS NX. The results of deformations as a function of time are compared with similar standards, studies, and investigations.

Random Forests Machine Learning Applied to PEER Structural Performance Experimental Columns Database

Columns play a very important role in structural performance and, therefore, this paper contributes to the critical need for failure mode prediction of reinforced concrete (RC) columns by exploring the capabilities of random forest machine learning (ML) based on a well-known experimental column database. Known as the PEER structural performance database, it assembles the results of over 400 cyclic, lateral-load tests of reinforced concrete columns. The database describes tests of spiral or circular hoop-confined columns, rectangular tied columns and columns with or without lap splices of longitudinal reinforcement at the critical sections. The efficiency towards the aforementioned goal of supervised ML methods such as random forests using a randomly assigned test set from the Pacific Earthquake Engineering Research Center (PEER) database is examined here. The overall accuracy score for rectangular RC columns is 94% and for circular RC columns is 86%. The latter performances are influenced by the size of the testing and training sets of data and are independent of the number of decision trees in the forest employed in the random forest algorithm. The performances of random forests in postdicting the failure mode of RC columns prove that ML has great promise in revolutionizing the profession of earthquake engineering.

Effect of lateral stiffness on expected economic losses in reinforced concrete shear wall buildings

This research paper evaluates the effect of lateral stiffness on expected economic losses in reinforced concrete shear wall buildings designed following current Chilean standards, including DS60 and DS61. Economic losses were evaluated for a group of four 20-story archetype buildings located in Santiago, Chile. The methodology developed by the Pacific Earthquake Engineering Research Center was considered to estimate economic losses. The expected annual loss (EAL) and the present value (PV) of the losses in 50 years were used as measures of economic loss. A probabilistic seismic hazard analysis, which considered the seismicity of central Chile, was performed to estimate both metrics. The results show that when the lateral stiffness of the building increases, the EAL also increases. This implies that stiffer buildings are more vulnerable from an economic point of view. This counter-intuitive finding results from the higher seismic hazard of stiffer buildings and the minimum design base shear required by DS61 that governed the design of the studied buildings. Additionally, it was found that the EAL and the PV of losses in 50 years for the four archetypes do not exceed 0.3% and 7.8% of the total construction cost of the buildings, respectively. These monetary losses are relatively low, which is consistent with the outstanding seismic performance of reinforced concrete shear wall buildings.

Comparison of ACI-318 and TS-500 codes for nonlinear seismic analysis of RC structures using force-based fiber element approach

The material properties such as compressive strength, tensile strength, and modulus of elasticity have been significant phenomena in the nonlinear seismic analysis of RC structures. In several national codes, different equations for the material properties of concrete based on compressive strength are suggested. The effectiveness of these equations in the analysis of RC structures should be investigated to determine the seismic response of the structure. In this study, the effectiveness of ACI-318 and TS 500 design codes is investigated in the numerical modeling of RC structures. For this purpose, the seismic responses of two RC structures experimented in the laboratory are used. The first structure is a two-dimensional three-bay and four-story RC bare frame structure that was tested in European Laboratory for Structural Assessment. The second structure is an RC tall bridge pier that was tested at Pacific Earthquake Engineering Research Center. The numerical solutions are obtained by using the Force-based Fiber Element Approach. The experimental and numerical analysis results are compared in terms of top displacements and damage zones. The concrete material properties which provide the best approximation to the experimental results are investigated for both design codes. It is concluded that the material properties of RC structures determined according to the ACI-318 code can be used for the Force-based Fiber Element Approach with regard to displacement response and damage regions.

Structural Assessment of Historical Stone Bridges with the Finite Element Method under Dynamic Effects of Arch Shape: The Antik Iscehisar Bridge

In this study, the effect of the variation of the arch form in thickness and height on the bridge was investigated as a numerical analysis. For this purpose, the historic Antik Iscehisar Bridge located in the Iscehisar district of Afyonkarahisar in the Aegean Region was elected as a numerical application. The bridge was subjected to its own weight and moving load as a static analysis. For dynamic analysis, the effect of 10 different fault movements with historical character obtained from The Pacific Earthquake Engineering Research Center (PEER) on the bridge was investigated. The areas of principal stress and deformation resulting from the applied analyses were determined. Contour diagrams, tables, and charts were given in a comparative manner based on the results of the analysis applied to the bridge. At the end of the analysis, it was observed that the displacements decreased as the arch thickness increased under its own weight in the bridge. In addition, under the influence of live loads and earthquakes, it was observed that the displacements decrease as the arch thickness increases. A seismic reliability assessment was made using the performance criteria provided in this study. If the security level is below what it should be, reinforcement applications can be designed. Accordingly, future maintenance and monitoring planning can be made.

The effect of the ground motion frequency content on seismic performance and fragility curve of special concentric braced frames

Several articles have investigated the effect of various seismic parameters on the response of the structure. However, there has been limited discussion on the impact of seismic frequency content on the brace failure modes and the collapse mechanism of this system. This study investigated the effect of this parameter, considering three special concentric bracing frames of 2, 4, and 6 stories. For the frame dynamic analysis, the open system for earthquake engineering simulation software (OpenSees) is used. To evaluate the effect of frequency content, 60 earthquake records with high, medium and low frequency content are selected from the pacific earthquake engineering research center ground motion database, considering the peak ground acceleration divided by the peak ground velocity ratio. Incremental dynamic analysis is conducted to draw fragility curves and examine the response of structures.The results indicated that the collapse capacity of structures decreased as the frequency content was reduced. The percentage of reduction in collapse capacity under earthquakes with high to the low frequency content in 2, 4 and 6 story structures were 68, 67 and 71, respectively. In fact, reducing the frequency content increased the probability of occurrence of low cycle fatigue mode in the brace.

Economic Seismic Performance of Buildings with Peer Methodology and Fema P-58

The performance-based seismic design pretends to take care of the lives of the occupants and reduce the cost of damage caused by earthquakes. Several ways of estimating damage and economic losses have been developed, but most of them lack objectivity and have great dispersion in the results. In the last decade, the advancement of technology has allowed the appearance of new methodologies, such as the one developed by the Pacific Earthquake Engineering Research Center (PEER methodology). However, the information regarding application and scope is scarce. In the present investigation, the economic seismic performance for a steel building was determined by applying the PEER methodology with different levels of seismic intensity. A multi-family residential model of special moment frames (SMF) was used, and the structure was designed by means of modal analysis. Spectral, incremental dynamic non-linear analysis was performed where the structural response was determined, with the help of the PACT software. The seismic performance, expressed as the repair costs, repair times, deaths, and injuries, was defined. The results obtained indicate that buildings designed with traditional structural standards can be demolished after the action of an earthquake because the repair costs exceed 40% of the replacement cost. Rare and very rare seismic events can cause the total suspension of the service and a considerable number of deaths and injuries.

Developing software to assess the seismic risk of natural gas infrastructure: OpenSRA

As part of a multi-year study, the Pacific Earthquake Engineering Research (PEER) center in conjunction with Lawrence Berkeley National Laboratory (LBNL), the NHERI SimCenter at Berkeley, Slate Geotechnical Consultants (Slate), and several subcontractors, developed an open-source seismic risk analysis (OpenSRA) program that implements the performance-based earthquake engineering (PBEE) risk framework using a user-friendly interface, capable of operating on a desktop computer. This effort is made up of two elements: 1) research of seismic demand and capacity models, laboratory testing, and finite element modeling, and 2) implementing these findings into a backend calculation program with an easy-to-use interface. The NHERI SimCenter, Slate Geotechnical Consultants, and PEER researchers developed an integrative method to create an efficient code with a useful interface.OpenSRAimplements and updates current models addressing different forms of seismically induced ground deformation (e.g., fault rupture, liquefaction, and landslide) as well as ground shaking. It quantifies resistance to deformation in buried pipelines, well heads, caprocks, and aboveground components, and then provides a framework for estimating the uncertainty in both the models and parameters. The software allows the end-user to utilize these models according to the resolution of the available data (i.e., state-wide, regional, or site-specific). The interface provides a visualization of the inputs (e.g., ground motion, and liquefaction potential) and outputs (risk of leaks/breaks) overlaid on different maps of California.OpenSRAprovides the industry with updated seismic demand and capacity models, an efficient calculation method, and user-friendly software.OpenSRAenables end-users to estimate the risk of seismic damage to their infrastructure. Using risk-informed decisions enables utility owners to proactively and cost-effectively maintain infrastructure.

RPBEE: Performance-based earthquake engineering on a regional scale

Over the last two decades, the performance-based earthquake engineering (PBEE) framework developed by the Pacific Earthquake Engineering Research (PEER) Center has gained the attention of researchers and practitioners worldwide. This framework was originally focused on quantifying the seismic risk of individual structures. However, with the advent of the “seismic resilience” concept, broader definitions of stakeholders and performance metrics on a regional scale are desirable. In this context, we present a formalized extension of the PEER PBEE framework for its use in groups of structures spatially distributed over a region, herein referred to as RPBEE framework. As an example, we use the RPBEE framework for computing the total number of damaged structures, the expected total economic loss, and the probability distribution of total economic losses within a region. We highlight differences with the original PEER framework and challenges arising from the implementation of the new RPBEE framework for future research and adoption, along with potential ways to overcome them.

A new seismic damage assessment method for single-layer spherical reticulated shells based on structural residual displacement under sequential earthquakes

Single-layer Spherical Reticulated Shell (SSRS) is usually utilized as an emergency shelter or a command post for disaster relief. However, the structural damage indicators in current studies are difficult to observe or record so as to hardly assess the damage state after the main shock. In order to explore the damage state assessment timely and accurately for SSRS, a new seismic damage assessment method based on structural residual displacement under main shock-aftershock ground motions is proposed in this paper. The finite element model of Kiewit 8-SSRS (K8-SSRS) is established in the ANSYS with 40 groups of actual sequential earthquake from the Pacific Earthquake Engineering Research Center (PEER) seismic database. Then, the dynamic responses of the model are analyzed to calculate the damage index and eight structural dynamic response indicators. Moreover, the correlation study between the damage index and eight indicators is carried out to make the comparison analyses. Finally, it is verified that the structural plasticity appeared under main shock can develop further under strong aftershock to cause greatly additional damage or even result in structural strength failure. It is concluded that the maximum residual displacement has the strongest correlation with the damage index of the structure under sequential earthquake, which means the most feasible indicator to characterize the structural damage under sequential earthquake.

Optimal Acceleration and Energy-Based Record Selection Using Artificial Intelligence Approaches and NP

ABSTRACT The new trend toward modern earthquake-resistant design of buildings based on nonlinear dynamic analysis accounting for peak and energy demands requires efficient ground motion record selection procedures. For this reason, in the present work, two record selection strategies based on acceleration and input energy spectrum considering the spectral shape via Np are presented. Furthermore, both record selection strategies are optimized by means of four artificial intelligence (AI) techniques: Genetic Algorithms (GA), Harmony Search (HS), Particle Swarm Optimization (PSO) and Vibrating Particle System (VPS). In particular, the effectiveness of each AI approach toward the best set of ground motion records for nonlinear dynamic analysis is compared. For this aim, spectral acceleration and input energy design spectra were considered, as well as 1024 seismic records obtained from the Pacific Earthquake Engineering Research Center. For all the AI or meta-heuristic approaches, the fitness function used is focused on minimizing the difference between the average spectrum of eleven ground motion records and the design spectrum using the well-known parameter Np, which represents the spectral shape in a range of periods. In addition, a penalization is included for those spectra with very large or low demands. Thus, 24 sets of eleven seismic records that can be used for nonlinear dynamic analysis of structures with a fundamental period of vibration of 0.6, 1.2 and 1.8 seconds were obtained and purposes. The results demonstrate the ability of the two records selection strategies analyzed and the four meta-heuristic procedures, achieving results quickly and simply regardless of the type of demands, intensities and periods considered. Finally, it is concluded that the VPS algorithm is better in comparison with GA, HS, and PSO since it obtains superior results in almost all the selected cases.

Estimating of Post-liquefaction Settlement Based on SPT Data in Klaten Regency, Central Java

A strong earthquake with Mw 6.3 occurred in Yogyakarta on May 27, 2006, causes of damage to DI Yogyakarta Province and Central Java Province, including Klaten Regency. The geological stratigraphy consists of volcanic breccias, lava and tuff in this area and generally shallow groundwater levels, which can trigger liquefaction. The potential liquefaction analysis in this study uses Standard Penetration Test (SPT) data carried out in 3 districts, namely Delanggu, Polanharjo, and Karanganom District uses Idriss-Boulanger method. The peak ground acceleration was calculated by Next Generation Attenuation (NGA) method using data obtained from the Pacific Earthquake Engineering Research (PEER) Center database. The peak ground acceleration that causes liquefaction in this study is at least 0.29 g in bedrock. All areas indicated potential liquefaction with varying depths. Polanharjo District is the most susceptible to potential liquefaction. The method proposed by Zhang estimates post-liquefaction settlement in this study from 3.3 – 42.4 cm (light to extensive damage) in Klaten Regency. Based on the results, further mitigation studies are needed in Klaten Regency to minimize settlement that will occur due to liquefaction. This study is required in planning the construction of toll road in Klaten Regency.

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.