Damage Assessment of Reinforced Concrete Structures through Damage Indices:A State-of-the-Art Review

Due to the developments of computer science and technology in recent years,computer models and numerical simulations for large and complicated structures can be done.Among the vast information and results obtained from the analysis and simulations,the damage performance is of great importance since this damage might cause enormous losses for society and humanity,notably in cases of severe damage occurring.One of the most effective tools to handle the results about the damage performance of the structure is the damage index(DI)together with the damage states,which are used to correlate the damage indices with the damage that occurred in the actual structures.Numbers of damage indices proposed and developed rely on the fact that the damage causes noticeable changes in the structural and dynamic properties of the structural components or the whole structure.Therefore,this study presents a comprehensive review of the damage assessment of Reinforced Concrete(RC)structures.It presents step by step the development of the damage indices that are most widely used to estimate the performance of structural components in the structure and subsequently assess the damage degree of such these structures either based on the structural properties or dynamic properties of the structure.Also,several damage states have been introduced to estimate the performance level of the structure.Finally,case studies,methodologies,and applications on the damage assessment of RC structures are reviewed and presented.

Damage detection in steel plates using feed-forward neural network coupled with hybrid particle swarm optimization and gravitational search algorithm

Over recent decades,the artificial neural networks(ANNs)have been applied as an effective approach for detecting damage in construction materials.However,to achieve a superior result of defect identification,they have to overcome some shortcomings,for instance slow convergence or stagnancy in local minima.Therefore,optimization algorithms with a global search ability are used to enhance ANNs,i.e.to increase the rate of convergence and to reach a global minimum.This paper introduces a two-stage approach for failure identification in a steel beam.In the first step,the presence of defects and their positions are identified by modal indices.In the second step,a feedforward neural network,improved by a hybrid particle swarm optimization and gravitational search algorithm,namely FNN-PSOGSA,is used to quantify the severity of damage.Finite element(FE)models of the beam for two damage scenarios are used to certify the accuracy and reliability of the proposed method.For comparison,a traditional ANN is also used to estimate the severity of the damage.The obtained results prove that the proposed approach can be used effectively for damage detection and quantification.

Constructing Damage Indices Based on Publicly Available Spatial Data:Exemplified by Earthquakes and Volcanic Eruptions in Indonesia

This article demonstrates the construction of earthquake and volcano damage indices using publicly available remote sensing sources and data on the physical characteristics of events.For earthquakes we use peak ground motion maps in conjunction with building type fragility curves to construct a local damage indicator.For volcanoes we employ volcanic ash data as a proxy for local damages.Both indices are then spatially aggregated by taking local economic exposure into account by assessing nightlight intensity derived from satellite images.We demonstrate the use of these indices with a case study of Indonesia,a country frequently exposed to earthquakes and volcanic eruptions.The results show that the indices capture the areas with the highest damage,and we provide overviews of the modeled aggregated damage for all provinces and districts in Indonesia for the time period 2004 to 2014.The indices were constructed using a combination of software programs—ArcGIS/Python,Matlab,and Stata.We also outline what potential freeware alternatives exist.Finally,for each index we highlight the assumptions and limitations that a potential practitioner needs to be aware of.

Nonlinear dynamic behavior of rotating blade with breathing crack

This study aims at investigating the nonlinear dynamic behavior of rotating blade with transverse crack.A novel nonlinear rotating cracked blade model(NRCBM),which contains the spinning softening,centrifugal stiffening,Coriolis force,and crack closing effects,is developed based on continuous beam theory and strain energy release rate method.The rotating blade is considered as a cantilever beam fixed on the rigid hub with high rotating speed,and the crack is deemed to be open and close continuously in a trigonometric function way with the blade vibration.It is verified by the comparison with a finite element-based contact crack model and bilinear model that the proposed NRCBM can well capture the dynamic characteristics of the rotating blade with breathing crack.The dynamic behavior of rotating cracked blade is then investigated with NRCBM,and the nonlinear damage indicator(NDI)is introduced to characterize the nonlinearity caused by blade crack.The results show that NDI is a distinguishable indicator for the severity level estimation of the crack in rotating blade.It is found that severe crack(i.e.,a closer crack position to blade root as well as larger crack depth)is expected to heavily reduce the stiffness of rotating blade and apparently result in a lower resonant frequency.Meanwhile,the super-harmonic resonances are verified to be distinguishable indicators for diagnosing the crack existence,and the third-order super-harmonic resonances can serve as an indicator for the presence of severe crack since it only distinctly appears when the crack is severe.