Damage Assessment Methodology Research Articles

Quantitative assessment of Hurricane Ian’s damage on urban vegetation dynamics utilizing Landsat 9 in Fort Myers, Florida

Florida’s unique climatic and geographical features have profoundly influenced its hurricane history. This study quantitatively examines the effects of Hurricane Ian on urban vegetation in Fort Myers, Florida, using remote sensing data. We analyzed pre- and post-hurricane vegetation indices, including NDVI (Normalized Difference Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index). Our findings reveal varied spatial impacts, with NDVI changes ranging from −0.03 to 0.333, ARVI changes from −0.016 to 0.25, and SAVI changes from −0.04 to 0.5. Negative values indicate vegetation damage, while positive values suggest resilience or recovery. The study area experienced a 63.75% reduction in vegetation cover, from 67.10 km2 before Hurricane Ian to 24.325 km2 after. Pre-hurricane NDVI ranged from −0.2298 to 0.5663, while post-hurricane values ranged from −0.189 to 0.521, indicating overall vegetation stress. ARVI maxima decreased from 0.379 to 0.352, and SAVI maxima from 0.849 to 0.782, further confirming vegetation damage. Support Vector Machine classification achieved 89% accuracy (Kappa = 0.85) for pre-hurricane and 87% (Kappa = 0.83) for post-hurricane vegetation mapping. These findings enhance our understanding of hurricane impacts on urban green infrastructure, with significant implications for urban planning and disaster preparedness in coastal cities prone to extreme weather events. The outcomes enhance damage assessment methodologies and provide valuable insights into the ecological consequences of hurricanes on urban ecosystems.

Rapid damage assessment effectiveness for the 2023 Kahramanmaraş Türkiye earthquake sequence

The recent devastating 2023 Türkiye earthquakes underscored the urgent need for an early estimate of quantitative losses following destructive seismic events. This study aims to propose and validate a Rapid Damage Assessment (RDA) methodology based solely on open data and tools, easily and readily available globally immediately after a seismic event. In this context, the 2023 Türkiye earthquakes are used as a case study. For the application of the proposed methodology there are two prerequisites: (1) ground shaking data, herein obtained from the US Geological Survey (USGS) ShakeMap system, and (2) a predictive seismic risk model appropriate for the area of interest, consisting of an exposure and a vulnerability model, derived from the 2020 European Seismic Risk Model (ESRM20). Scenario-type analyses for the two major events, the M7.8 - Pazarcik and the M7.5 – Elbistan earthquake, are performed, and the results are compared with the officially reported damages. The main goal is to assess the accuracy of RDA immediately after an earthquake, and whether and to what extent the RDA results can be improved as the USGS ShakeMaps are updated with the incorporation of additional data, and with disaggregation of the exposure model at a finer resolution. The results indicate that the exposure model's disaggregation reduces the damage estimates' standard deviation. At the same time, the effect of the ShakeMap version is minimized when the complete fault rupture is incorporated into the model. The estimated damages are in good agreement with the officially announced data at large scale, which corroborates the proposed methodology, as well as the adopted components of ESRM20.

Applying the multi-dimensional damage assessment (MDDA) methodology to the Cumbre Vieja volcanic eruption in La Palma (Spain)

Applying the multi-dimensional damage assessment (MDDA) methodology to the Cumbre Vieja volcanic eruption in La Palma (Spain)

Development of a spatial framework for flash flood damage assessment and mitigation by coupling analytics of machine learning and household level survey data – A case study of rapid collaborative assessments and disbursement of public funds to the affectees of floods 2022, Punjab Pakistan

In August 2022, one of the most devastating floods in the history of Pakistan was triggered due to heavy and exceptionally high monsoon rainfall. It has affected thirty-six million people across the country and agriculture losses were recorded in the most productive Indus plains which aggravated the food insecurity in the country. This study focuses on the two districts of Punjab provinces includes DG Khan and Rajanpur, where huge flood damages were reported. As part of damage and loss assessment methodologies, GIS/RS based approach is developed in this study to estimate flood extent in DG Khan and Rajanpur. Field survey was conducted using android based devices with the help of military and government organizations such as PDMA, to verify and record flood damages. Information regarding damage includes property, livestock, people, crop and other damages with real-time images captured and stored via android app and flood data monitoring was visualized on a web-based dashboard. SAR data (Sentinel 1-A) was used to calculate and verify extent of the flood along with Sentinel 2-A dataset to extract Land cover classes. SAR flood maps were validated using the survey data retrieved during flood events. This data was compared with the Sentinel-1A dual polarised (VV & VH). The SAR data (Sentinel-1A) was also collected for the same day to extract the Flood Extent of August/September 2022 using Modified Normalized Difference Water Index (mNDWI). Hence, flood inundation map was prepared using polarisation. This examination is confirmed through the kappa coefficient (κ), which is 0.88 for VV polarisation and 0.86 for VH polarisation. In VV polarisation, PA and UA of ‘water’ are less than of all ‘other classes. Mauza level mapping was also conducted to identify the damaged mauzas in both floods affected districts to figure out damaged villages and find out damaged property with khasra or mauza name. GIS RS based and survey-based model was developed in this research, which replicated on two districts of Punjab to assess flood damage, it will help Pakistan government to always asses flood damages to crop, houses, livestock and other assets. This helped to communicate with the local governments and identify and rank high affected villages; so that priority shall be given as per intensity of the damages. An android application for flood survey was developed. The flood survey application provided facility to estimate the on-ground reality of each individual, property, crop and livestock damage due to flood. This application was mainly used for the assessment purpose that provides detail information with precise location and real visualization of the damage assets. Joint survey teams were constituted including members from the Provincial Disaster Management Authority, National Disaster Management Authority, Pakistan Army, Local Government and the Urban Unit. Each village classified as flood damages/affected was visited by the survey team through a proper survey plan. After all the flood data was recorded, now the next step was to build transparent system to distribute funds to the flood victims. For this purpose, Bank has developed dashboard to distribute funds to the affected by the Government of Punjab.

Mitigating the risk for creep-fatigue cracking in steam turbine rotors: an end user's perspective

Over the last few years, cracks appeared at an early stage of operation on several steam turbine rotors due to low cycle fatigue damage induced by transient thermal events. These cracks led to expensive repair and associated unavailability of the units. A proper risk assessment is therefore key, especially given the expected increase in transient operation required from the conventional power plants in the future.To manage the risk for the ENGIE fleet, with a wide variety of different steam turbine rotors, ENGIE Laborelec has developed a fleet risk management program. At the heart of this are numerical and finite element (FE) models, along with damage assessment methodologies that can offer estimates on the past and future lifetime consumption. Experience with cracked rotors provided the unique opportunity to calibrate the approach, enabling a more accurate prediction for the rest of the ENGIE fleet.Based on the outcome of the lifetime study, different mitigation actions are proposed. Besides the reduction of operational gradients, a very interesting approach is to pro-actively machine the rotor surface before the onset of cracking.

Світовий досвід повоєнної компенсаційної політики

Today, the government of Ukraine and international and domestic experts estimate the damage from the war in the hundreds of billions of US dollars. However, this is usually a summary assessment data. Object-specific expert evaluation has a single application and requires the development of specific mechanisms for its implementation. The essential basis for this is international experience. Therefore, the article aims to study international reactions to economic challenges caused by war and the generalization of international approaches to the definition and procedures for compensation of losses to enterprises of victim countries caused by armed aggression. The study results confirm the expediency of partially applying the practices of Kuwait to create a corresponding mechanism in Ukraine. The primary basis for financing compensation commissions for the payment of reparations to Ukraine, in particular, confiscated Russian assets and deductions from the aggressor's exports, has been determined. The need to develop low-cost damage assessment methods due to the limited possibilities of professional assessment (limited personnel, financial and time resources) is substantiated. The need for creating a simplified mechanism for recording and assessing losses, which will be available for use by a wide range of specialists and separate sectoral methods of assessing losses (agricultural sector, transport, etc.), was highlighted. The shortcomings of the damage and loss assessment methodology chosen by the Government of Ukraine are substantiated, including the lack of an opportunity to determine and demand compensation for indirect damages (forgotten profit). The hypothesis about the need for revisions and additions in the domestic policy of assessing losses and compensations for the consequences of war, in particular, in terms of focusing on an expert approach in assessment and judicial and arbitration practice of compensations, the introduction of an accounting approach to assessing losses, has been proven.

Fatigue damage assessment methodology for a deepwater subsea wellhead based on monitoring data

ABSTRACT Deepwater subsea wellhead system is subjected to the various cyclic loads transmitted from the drilling riser due to ocean waves, currents and platform motion during drilling operation; therefore, the fatigue damage is accumulated. A fatigue damage assessment methodology and its detailed analysis procedure for subsea wellheads utilising transfer function theory based on monitoring acceleration data at the drilling riser are developed. Studies on an actual monitoring case for a subsea wellhead under drilling operation with 336 h of monitoring events are conducted and a verification method by comparison of the measured fatigue results and predicted fatigue results based on wave and current data are introduced. The results for wave and vortex-induced vibration (VIV) fatigue life based on measured acceleration data are calculated, and the fatigue bias with predicted results are summarised. The developed fatigue damage assessment method and monitoring case studies can be valuable references for deepwater drilling engineering.

ASSESSMENT OF COASTAL DAMAGE FOR THE COOLING WATER INTAKE STRUCTURE OF A COAL-FIRED POWER PLANT

Last October 2020, a strong tropical cyclone with international name Molave (local name: Quinta) caused widespread damage to agricultural and infrastructure sectors in the Philippines. Among those affected is a coalfired power plant located within the vicinity of Tayabas Bay. The velocity cap at the head of the cooling water intake structure of the power plant was found washed ashore in the morning of October 26, 2020 (PH time) after the onslaught of Typhoon Molave. Underwater site investigations post-typhoon revealed that the walls and other components of the intake structure were damaged as well. The intake structure head was initially built at seabed depths of 9m below Mean Tide Level (MTL), while the velocity cap was topped at 6m below MTL. This paper presents the forensic analysis and coastal damage assessment methodology carried out to identify the nearshore hydrodynamic conditions during the passage of potentially critical typhoons throughout the useful life of the intake structure (2017 to 2020).

Probabilistic seismic damage and loss assessment methodology for wastewater network incorporating modeling uncertainty and damage correlations

Maintaining the functionality of wastewater networks is critical to individual well-being, business continuity, public health, and safety. However, seismic damage and loss assessments of wastewater networks traditionally use fragility functions based on median repair rates without considering relevant sources of uncertainty and correlations of damage when estimating potential damage states and pipe repairs. This study presents a probabilistic methodology to incorporate modeling uncertainty (e.g. model parameter and model class uncertainty) and spatial correlations (e.g. spatial auto- and cross-correlation) of pipe repairs. The methodology was applied to a case study backbone system of a wastewater network in Portland, OR, using the expected hazard intensity maps for multiple deterministic earthquake scenarios, including a moment magnitude M6.8 Portland Hills Fault and M8.1, M8.4, M8.7, and M9.0 Cascadia Subduction Zone (CSZ) events. As spatial-correlation models of pipeline damage were non-existent in the literature and local information on costs to repair the pipes was limited at the time of this study, correlation methods and repair costs were proposed to estimate lower and upper bounds of pipe damage and loss. The results show how the consideration of different levels of uncertainty and spatial correlation for pipe repair rate could lead to different probabilistic estimates of damage and loss at the system level of the wastewater network, even though the point estimates, such as the mean and median, remain essentially unaltered.

Issues of Improving the Methodology of Agricultural Damage Assessment

The main role in the agricultural insurance program is damage assessment, which is very peculiar due to its specificity. Damage assessment is carried out by determining the percentages of loss, final real production and expected real production according to the insurance conditions of the plant damaged by insurance risks, this process is carried out by an agro expert. The damage assessor evaluates the deviation of the condition of the vegetative and generative parts of the plant, fruits from the standard/initial condition as a result of injuries caused by the insured risk. The assessment is done only to determine the percentage of quantitative and not qualitative losses. The purpose of determining the percentage of damage is to determine by how much the percentage of the crop/plants that existed before the occurrence of the insured risk occurred/will occur, compared to what it was before the occurrence of the risk. Perfection of damage assessment methodology in the agricultural insurance program is vital for the farmer. The specifics of properly conducted agro loss assessment will contribute to the development of agriculture and agro-insurance. As the research shows, a lot of work is needed to regulate agricultural losses. In order to perfect the methodology of damage assessment, it is necessary to conduct practical studies and to certify agricultural experts. Furthermore, it is necessary to share the world experience for the development of agro-insurance, taking into account the local specifics; Moreover, the perfection of the necessary agricultural insurance state program for correct assessment of risks, adequate determination of insurance premium and full compensation of financial losses of farmers are necessary; It is quite important to create and to improve damage assessment methods for different cultures individually. Keywords: Insurance, farmer, damage, appraisal, expert, agriculture.

A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm

In the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodology for rapid damage assessment into modern software development platforms, while the developed graphical user interface promotes the ease of use even from non-experts. The aim is to foster actions both in the pre- and post-earthquake phase. The structure of the source code permits the usage of the application either autonomously as a software tool for rapid visual inspections of buildings prior to or after a strong seismic event or as a component of building information modelling systems in the framework of digitizing building data and properties. The methodology implemented for the estimation of the RC buildings’ damage states is based on the theory and algorithms of pattern recognition problems. The effectiveness of the developed software is successfully tested using an extended, numerically generated database of RC buildings subjected to recorded seismic events.

ЗАРУБІЖНІ ПРАКТИКИ ОЦІНЮВАННЯ ЗБИТКІВ ДЛЯ ПОВОЄННОГО ВІДНОВЛЕННЯ ДОВКІЛЛЯ

The article examines in detail the experience of the Iraq-Kuwait war in terms of receiving compensation for the damage caused by the war to the surrounding natural environment. In particular, the "Habitat Equivalency Analysis" methodology, which was used to determine the amount of compensatory restoration to cover losses in ecological services that were provided by natural resources before their damage. The methodology aims to estimate the total loss of services provided by a damaged or lost habitat. The FAO methodology for damage and loss assessment in agriculture was also analyzed. In which the focus is on the continuous collection of data on damage and loss in agriculture, recording the smaller and localized effects of disasters. The focus in the review of the methodology is focused on the determination of damages and losses in forestry. Such components as: damages and losses of forestry; loss of forestry production; loss of forestry products; damage to forestry assets. It was established that the considered methodology is suitable for determining the damage caused by military actions for forest resources in the part that directly concerns the forest cover.
 One of the areas of damage assessment in the nature management sector is the determination of options for primary (restoration) recovery. This stage refers to actions aimed at restoring the damaged resource and, if possible, returning it to the basic level (before the war), which involves establishing recovery goals; determination of primary restoration options; selection of primary restoration options; assessment of intermediate damages, etc. The choice of primary restoration options should be the result of an evaluation process based on the following criteria, but not limited to cost of implementation; the length of time it will take for the recovery to be effective; probability of success, etc.
 The next steps should be a large-scale damage assessment in accordance with generally accepted methods, development of ways to restore the natural environment and identification of funds to minimize or eliminate the damage caused to the elements of the environment or natural resources.

A damage assessment methodology for structural systems using transfer learning from the audio domain

Neural network-based strategies require balanced training datasets to avoid creating unreliable classification and prediction models. While these strategies are commonly used to model the dynamics of structural and mechanical systems, the imbalanced composition of monitoring data is a fundamental challenge for damage assessment in structural systems. The monitoring data often contain abundant observations from structures in their normal operating conditions (undamaged state) and small and partial information from systems in the damaged state. Therefore, the model, trained by adopting deep learning approaches, tends to show an ill-conditioned nature, limited to specific structures in a narrow range of damage conditions.The current study presents a damage assessment strategy that overcomes the limitations of unbalanced datasets. To improve the model’s ability to distinguish between different health conditions, informative features are utilized to facilitate the differentiation of multiple classes according to the frequency content of vibration signals. The model acquires this ability by learning from a rich dataset of human voices (source domain), where low-level features that denote the vibration traits of human waveforms are extracted. Subsequently, this knowledge is transferred to the features of a target domain that has limited data for damage detection.The proposed methodology relies on creating an informative feature extractor training a Time-Delay Neural Network (TDNN) using a collection of human voice recordings. Cepstral and pitch features derived from the speech data are used as input features for the TDNN. This network is used to derive low-level features at intermediate layers of the network, called “x-vectors”. These features store non-case-dependent information about the frequency content of the signals and depict the ability to distinguish between different classes according to a change in the frequency content of the investigated system. This is not a unique attribute of the original audio source domain, and it can be employed to help differentiate categories for any vibrating system where a modification in the frequency content is representative of a transition between classes, including the structural and mechanical systems. Because of the generalization trait of the x-vector, they can be employed to construct a Probabilistic Linear Discriminant Analysis model able to classify various damage classes considering vibration measurements obtained from a different system, i.e., a structural system (target domain).Initially, the simulated acceleration response from the 12-degree of freedom structure are analyzed to affirm the effectiveness of the framework. Then, the method is further validated by using the field data of the Z24 bridge, to evaluate its reliability in real-world applications.

A framework for multi-sensor satellite data to evaluate crop production losses: the case study of 2022 Pakistan floods

In August 2022, one of the most severe floods in the history of Pakistan was triggered due to the exceptionally high monsoon rainfall. It has affected ~ 33 million people across the country. The agricultural losses in the most productive Indus plains aggravated the risk of food insecurity in the country. As part of the loss and damage (L&D) assessment methodologies, we developed an approach for evaluating crop-specific post-disaster production losses based on multi-sensor satellite data. An integrated assessment was performed using various indicators derived from pre- and post-flood images of Sentinel-1 (flood extent mapping), Sentinel-2 (crop cover), and GPM (rainfall intensity measurements) to evaluate crop-specific losses. The results showed that 2.5 million ha (18% of Sindh’s total area) was inundated out of which 1.1 million ha was cropland. The remainder of crop damage came from the extreme rainfall downpour, flash floods and management deficiencies. Thus approximately 57% (2.8 million ha) of the cropland was affected out of the 4.9 million ha of agricultural area in Sindh. The analysis indicated expected production losses of 88% (3.1 million bales), 80% (1.8 million tons), and 61% (10.5 million tons) for cotton, rice, and sugarcane. This assessment provided useful tools to evaluate the L&D of agricultural production and to develop evidence-based policies enabling post-flood recovery, rehabilitation of people and restoration of livelihood.

Damage localization and characterization using one-dimensional convolutional neural network and a sparse network of transducers

Early damage identification and continuous system monitoring save dramatically maintenance costs and increase the lifespan of priceless structures. Convolutional neural networks (CNNs) have attracted the attention of the structural health monitoring (SHM) community in recent years due to their great potential for identifying underlying data patterns. However, employing two-dimensional convolutional layers in a CNN necessitates the use of strong computing resources. Therefore, based on the present state-of-the-art technical solutions, a two-dimensional CNN is not suitable for real-time SHM applications with stand-alone processing units. One-dimensional convolutional networks (1D-CNN) have recently been employed in Ultrasonic Guided Wave-based (UGW-based) damage detection to address the aforementioned disadvantage. In this paper, a methodology for damage assessment at three levels – detection, localization, and characterization – based on 1D-CNN is put forward. Furthermore, the sequence length of the time-domain signals is significantly shortened by the application of a novel approach for processing them. Additionally, it is shown to what extend this method can improve the distinguishability between datapoints obtained from various damage scenarios. Consequently, by reducing the dimensionality of the problem, the proposed approach significantly reduces the memory usage of the classification algorithm. Experimental measurements as well as Numerical simulations, in which various damage scenarios such as corrosion, circular hole and cracks have been considered, are carried out to evaluate the efficacy of the proposed algorithm. It is shown that the suggested approach has benefits in terms of true classification rate of instances (above 93 percent for detection, localization, and characterization), computing time, in-situ monitoring, and noise resilience.

Mapping Cork Oak Mortality Using Multitemporal High-Resolution Satellite Imagery

In the Mediterranean region, a significant decline in the vitality of vegetation has been observed in the last two decades, with a high forest mortality rate for several species. The increase in mortality has been attributed to water stress resulting from an increase in temperature and long periods of drought. To detect and quantify the impact of these events on tree mortality, an efficient and easy-to-use methodology for rapid damage assessment is required. Our study aims to assess the potential of high spatial resolution multispectral images from the Pleiades constellation to detect and map cork oak mortality in a pasture environment with multiple forest species. An approach based on change detection and the use of an unsupervised classifier is proposed to detect mortality at the cork oak level. The change in the values observed for three vegetation indices, NDVI, RGI, and GNDVI, between two epochs is used in an unsupervised classification algorithm to estimate the dead tree class. The classification results are accurate, with precision and recall values higher than 90%. Detailed cork oak mortality mapping is of significant use in comprehending ecosystem change as a result of tree mortality and for the implementation of mitigation mechanisms for the ongoing desertification process.

Vibration-Based and Near Real-Time Seismic Damage Assessment Adaptive to Building Knowledge Level

This paper presents a multi-level methodology for near real-time seismic damage assessment of multi-story buildings, tailored to the available level of knowledge and information from sensors. The proposed methodology relates changes in the vibratory characteristics of a building—evaluated via alternative dynamic identification techniques—to the European Macroseismic Scale (EMS-98) damage grades. Three distinct levels of knowledge are considered for the building, with damage classification made through (i) empirical formulation based on quantitative ranges reported in the literature, (ii) analytical formulation exploiting the effective stiffness concept, and (iii) numerical modelling including a simplified equivalent single-degree-of-freedom model or a detailed finite element model of the building. The scope of the study is twofold: to construct a framework for integrating structural health monitoring into seismic damage assessment and to evaluate consistencies/discrepancies among different identification techniques and model-based and model-free approaches. The experimental data from a multi-story building subject to sequential shaking are used to demonstrate the proposed methodology and compare the effectiveness of the different approaches to damage assessment. The results show that accurate damage estimates can be achieved not only using model-driven approaches with enhanced information but also model-free alternatives with scarce information.

Quantitative Investigation of Water Sensitivity and Water Locking Damages on a Low-Permeability Reservoir Using the Core Flooding Experiment and NMR Test.

Production of oil and gas energy is often greatly hindered by reservoir formation damage, particularly the occurrences of water sensitivity and water locking damages on a low-permeability reservoir. For the purpose of this paper, a formation damage assessment methodology combining core flooding experiment and NMR (nuclear magnetic resonance) T2 relaxation tests is performed and applied to quantitatively determine water sensitivity/water locking damage on sandstone oil formation. XRD tests are used to analyze the mineral composition of cores. Core flooding experiments are designed to simulate the two damages and determine the permeability reduction. NMR tests are introduced to compare water saturation before and after flooding through rock cores, calculate the porosity damage and changes of the pore size, and analyze the mechanism of water sensitivity and water locking damages. Also, SEM experiments are used to determine the pore morphology before and after damage. Low-permeability sandstone rock cores cored from the Jilantai reservoir are assessed through this whole set of experiments. The results demonstrate that the permeability and porosity of core samples strongly decrease with the occurrence of water sensitivity/water locking damage, reflecting that the Jilantai reservoir has strong water sensitivity and is prone to be damaged by water locking. Compared with the previous formation damage assessment ideas, much attention is given to the microchanges of cores after damage, and using fluorinated oil instead of kerosene can help observe the distribution of water in rock core samples after each flooding by the NMR T2 spectra.

Dynamic behavior and damage assessment of RC columns subjected to lateral soft impact

In this study, the performance and damage behavior of reinforced concrete (RC) columns under lateral soft impact are comprehensively investigated using 3D finite element (FE) analysis. Firstly, refined FE models of RC columns are developed in LS-DYNA, and the corresponding simulation techniques are verified against existing experimental testing data. Then, response characteristics of a typical column under soft impact are examined, and a section damage factor is defined to quantify the damage evolution of impacted columns. After that, the effects of various design parameters, including impactor stiffness, impact elevation, axial force ratio, section dimension, and combination of impact mass and velocity, on the dynamic behavior of columns are examined. Finally, criteria for both flexural damage and shear damage are proposed based on the section damage factor, and the resistance dynamic increase factor (RDIF) of RC columns under soft impact is utilized to establish a rapid damage assessment methodology. From these results, it is proved that the dynamic responses and failure modes of RC columns under soft impact are quite different from those under hard impact. The impactor stiffness and impact elevation have a significant effect on the mechanical mechanism of columns, and the column resistance generally increases with the axial force to a certain extent. In addition, RDIF of columns decreases with the increase in the ratio of the impact time at peak force tp to the column natural period Tcol.

Damage assessment of RC bridges considering joint impact of corrosion and seismic loads: A systematic literature review

This paper systematically reviewed 84 journal articles published from 2010 to 2020 and structured the reviewed literature using the following categories: year-wise number of research articles, journals, country, and citations. Through a bibliometric and content review analysis, the present review found that the existing studies have mostly focused on corrosion effects, and less attention was paid to quantifying seismic damage of corroded RC bridges. It is required to develop a damage assessment methodology for corroded RC bridges based on a reliable damage index, which can consider the cumulative effects of repeated loading cycles during earthquakes combining the impact of corrosion.