• Composites Research
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• v.12 no.2
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• pp.62-69
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• 1999

During fatigue loading of composite materials, damage accumulation can be monitored by measuring their material properties. In this study, fatigue modulus is used as the damage index. Fatigue life of composite materials may be predicted analytically using damage models which are based on fatigue modulus and resultant strain. Damage models are propesed as funtions of applied stress level, number of fatigue cycle and fatigue life. The predicted life was comparable to the experimental result obtained using E-glass fiber reinforced epoxy resin materials and pultruded glass fiber reinforce polyester composites under two-stress level fatigue loading.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.2
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• pp.44-50
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• 2015

This study investigated the durability of the under bar of a car through structural and fatigue analysis. Model 1 had the lowest value among three kinds of models. In the case of the maximum equivalent stress and displacement at structural analysis, model 1 showed the highest durability. Also, models 3 and 2 showed structural durability in order of this value. In the case of fatigue analysis, the maximum fatigue lives of the three models were equal to $2{\times}10^7$cycles. However, model 1 showed the highest value among the three models, as the minimum fatigue life of model 1 becames 92.56 cycles. Also models 3 and 2 showed fatigue durability in order of this value. The maximum possibility of fatigue damage for models1,2,and 3 became 30%. If the results of this study are applied to change the design shape of the under bar of cars, the ride comfort for automobile passengers and car durability can be improved.

• Structural Engineering and Mechanics
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• v.62 no.4
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• pp.455-464
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• 2017

Rayleigh damping model is recommended in the recently developed Performance-Based Earthquake Engineering (PBEE) methodology, but this methodology does not provide sufficient information due to the complexity of the damping mechanism. Furthermore, each Rayleigh-type damping model may have its individual limitations. In this study, Rayleigh-type damping models that are used widely in engineering practice are discussed. The seismic performance of a large-span single-layer latticed dome subjected to earthquake ground motions is investigated using different Rayleigh damping models. Herein a simulation technique is developed considering low cycle fatigue (LCF) in steel material. In the simulation technique, Ramberg-Osgood steel material model with the low cycle fatigue effect is used to simulate the non-uniformly distributed material damping and low cycle fatigue damage in the structure. Subsequently, the damping forces of the structure generated by different damping models are compared and discussed; the effects of the damping ratio and roof load on the damping forces are evaluated. Finally, the low cycle fatigue damage values in sections of members are given using these damping models. Through a comparative analysis, an appropriate Rayleigh-type damping model used for a large span single-layer latticed dome subjected to earthquake ground motions is determined in terms of the existing damping models.

• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.259-271
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• 2019

It is important to obtain reasonable predictions of the extent of the damage during maritime accidents such as ship collisions and groundings. Many fracture models based on different mechanical backgrounds have been proposed and can be used to estimate the extent of damage involving ductile fracture. The goal of this study was to compare the damage extents provided by some selected fracture models. Instead of performing a new series of material constant calibration tests, the fracture test results for the ship building steel EH36 obtained by Park et al. (2019) were used which included specimens with different geometries such as central hole, pure shear, and notched tensile specimens. The test results were compared with seven ductile fracture surfaces: Johnson-Cook, Cockcroft-Latham-Oh, Bai-Wierzbicki, Modified Mohr-Coulomb, Lou-Huh, Maximum shear stress, and Hosford-Coulomb. The linear damage accumulation law was applied to consider the effect of the loading path on each fracture surface. The Swift-Voce combined constitutive model was used to accurately define the flow stress in a large strain region. The reliability of these simulations was verified by the good agreement between the axial tension force elongation relations captured from the tests and simulations without fracture assignment. The material constants corresponding to each fracture surface were calibrated using an optimization technique with the minimized object function of the residual sum of errors between the simulated and predicted stress triaxiality and load angle parameter values to fracture initiation. The reliabilities of the calibrated material constants of B-W, MMC, L-H, and HC were the best, whereas there was a high residual sum of errors in the case of the MMS, C-L-O, and J-C models. The most accurate fracture predictions for the fracture specimens were made by the B-W, MMC, L-H, and HC models.

• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.

• Journal of Ocean Engineering and Technology
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• v.28 no.3
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• pp.185-192
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• 2014

The riser systems for floating offshore structures are known to experience tri-modal dynamic responses. These are owing to the combined loadings from the low-frequency response due to riser tension behavior, middle-range frequency response coming from winds and waves, and high-frequency response due to vortex induced-vibration. In this study, fatigue damage models were applied to predict the fatigue damages in a well-separated tri-modal spectrum, and the resultant fatigue damages of each model were compared with the most reasonable fatigue damage calculated by the inverse Fourier transform of the spectrum, rain-flow counting method, and Palmgren-Miner rule as a reference. The results show that the fatigue damage models developed for a wide-band spectrum are applicable to the tri-modal spectrum, and both the Benasciutti-Tovo and JB models could most accurately predict the fatigue damages of the tri-modal spectrum responses.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.2
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• pp.112-122
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• 2012

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

• The Transactions of the Korea Information Processing Society
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• v.13 no.5
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• pp.217-220
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• 2024

Predicting earthquake occurrences accurately is challenging, and preparing all buildings with seismic design for such random events is a difficult task. Analyzing building features to predict potential damage and reinforcing vulnerabilities based on this analysis can minimize damages even in buildings without seismic design. Therefore, research analyzing the efficiency of building damage prediction models is essential. In this paper, we compare the accuracy of earthquake damage prediction models using machine learning classification algorithms, including Random Forest, Extreme Gradient Boosting, LightGBM, and CatBoost, utilizing data from buildings damaged during the 2015 Nepal earthquake.

• Smart Structures and Systems
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• v.26 no.1
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.