Accurate Damage Prediction Research Articles

A Dynamic Damage Constitutive Model of Rock-like Materials Based on Elastic Tensile Strain

To accurately characterize the damage of rock-like materials under simultaneous or alternating tensile and compressive loading, a dynamic damage constitutive model for rock-like materials based on elastic tensile strain is developed by integrating the classical compressive plastic damage model and the tensile elastic damage model. The model is based on the Holmquist–Johnson–Cook (HJC) and Kuszmaul (KUS) models, categorizing the element stress state into tensile and compressive states through positive and negative elastic volumetric strain. It utilizes elastic tensile strain to enhance the calculation method for tensile cracks, determining the tensile strength of the principal direction based on the contribution rate of tensile principal stress for uniaxial/multiaxial loading. Additionally, it establishes a maximum elastic tensile strain rate function to rectify the model’s effect on the tensile strain rate. Through the LS-DYNA subroutine development, the model proficiently delineates the distribution of ring-shaped cracks on the frontal side and strip-shaped cracks on the rear side of the reinforced concrete slab subjected to impact loading. Numerical simulations demonstrate that the model provides more accurate damage prediction results for stress conditions involving simultaneous or alternating compression and tension, offering valuable insights for damage analysis in engineering blasting or impact penetration.

Multi-damage index-based interfacial debonding prediction for steel-concrete composite structures with percussion method

The interfacial debonding as an invisible damage significantly undermines the bearing capacity and durability of steel-concrete composite structures (SCCS). Although the percussion method has been widely utilized in practical applications, the single damage index (DI) extracted in the analysis process tends to be invalid to abnormalities and leads to misjudgment. The accurate damage prediction requires extracting multiple indicators strongly correlated to the interfacial debonding. Accordingly, this paper proposed a novel debonding detection methodology for a multi-indicator system by integrating the percussion method with machine learning. The classification framework includes two parts: (i) 79 DIs are calculated to represent the interface characteristics comprehensively, and the fused feature selection algorithms are investigated to determine the optimal DI subset. (ii) Five classical machine learning approaches after hyperparameter tuning are evaluated with the final objective to compare the prediction accuracy of each model. Aided by the experimental measurements and correlation analyses, 20 key DIs are filtered out as model input. Then, the evaluation performance reveals that the KNN classifier achieves the superlative identification results on the experimental dataset, presenting a prediction accuracy of 99.6 %. Finally, a new dataset confirms the accuracy and effectiveness of the proposed method in the form of 2D damage imaging. The research findings of this study are beneficial for improving the detection accuracy and provide significant guidance on the percussion method-based interfacial debonding detection for the SCCS.

Fragility of Indonesian houses: scenario damage analysis of the 2006 Yogyakarta and 2009 Padang earthquakes

Indonesia is located in one of the most seismically active regions in the world and often experiences damaging earthquakes. In the past the housing sector has sustained higher earthquake related damage and losses than other sectors. This is often attributed to the fact that the most common houses in Indonesia are non-engineered, built with poor quality workmanship, poor quality materials and without resilient seismic design features. However little effort has been made to quantify how fragile Indonesian houses are, or how their fragility may vary according to the population density or relative wealth of a region. It is not possible to derive empirical fragility functions for Indonesia due to insufficient damage data. The aim of this study is to determine whether existing earthquake fragility functions can be applied to common house types in Indonesia. Scenario damage analyses simulating the 2006 Yogyakarta and 2009 Padang events were undertaken several times testing different fragility functions. The simulated damage results were then compared to the damage observed post event to determine whether an accurate damage prediction could be achieved. It was found that the common house types in Yogyakarta and Central Java vary according to age of construction, location and relative wealth of a region and can be reasonably well represented by existing fragility functions. However, the houses in Padang and surrounding West Sumatra did not vary in a predictable manner and are more fragile than anticipated. Therefore, the fragility of the most common house types in Indonesia differs between Central Java and West Sumatra. This has important implications for seismic damage and risk assessment undertaken in Indonesia.

Predicting Damage Characteristics of Heavy Rain Disasters Using Artificial Neural Network

Heavy rain disaster is a representative natural disaster in Korea, and causes considerable damage every year as a result of typhoons and heavy rain. Various preventive measures and reduction facilities have been introduced to reduce heavy rain damage, but damage continues to occur. Accurate damage prediction is necessary for preemptive response to heavy rain disasters. A prediction study of the damage characteristics of such disasters using machine learning was conducted. Artificial neural network (ANN) was applied as a machine learning technique, and damage data of the aforementioned disasters from 1999 to 2019 were used. Damage prediction using an ANN was analyzed in terms of total rainfall, maximum daily rainfall, and total damage amount. The prediction of the damage characteristics was most accurate for the total damage amount. Total and daily maximum rainfall are less correlated with the characteristics of various damage amounts, which may explain the low accuracy of the analysis.

A continuum damage model for Mg/Al composite sheets rolling: Theoretical development and application

Edge cracks, which are typical formability defects, severely limit the widespread application of Mg/Al composite sheets. Accurate prediction of damage is crucial for understanding the underlying mechanisms behind crack formation. In this study, a continuum damage model that incorporates the stress-state function and effective equivalent plastic strain into the standard Lemaitre model is proposed. This enhanced model effectively addresses the issues of damage-evolution linearization and tension–compression asymmetry in the standard Lemaitre model. Thus, it can be successfully applied to predict the fracture response of ductile composite materials under pressure-forming conditions. Considering AZ31B Mg alloy and 5052 Al alloy as experimental materials, physical experiments and numerical validation are performed under a 50% reduction and 350 °C. The findings show that the proposed model effectively captures crack initiation and propagation during the rolling process, with errors of only 23.1% and 19.9% for average crack quantity and length, respectively. Results of numerical analysis reveals that the high-stress triaxiality at the edge of the sheet contributes significantly to crack formation. Additionally, the strain along the normal direction in the Al alloy significantly affects crack propagation and the formation of serrated cracks on the side of Mg alloy. This study provides important theoretical foundations for the development of Mg-based composite sheets.

Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage.

Soybean (Glycine max (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide use, leading to reduced yields. Identifying the level of leaf damage aids yield projections, pesticide, and fertilizer decisions. Deep learning models (DLMs) and neural networks mastering tasks from abundant data have been used for binary healthy/unhealthy leaf classification. However, no DLM predicts and categorizes soybean leaf damage severity (five levels) for tailored pesticide use and yield forecasts. This paper introduces a novel DLM for accurate damage prediction and classification, trained on 2930 near-field soybean leaf images. The model quantifies damage severity, distinguishing healthy/unhealthy leaves and offering a comprehensive solution. Performance metrics include accuracy, precision, recall, and F1-score. This research presents a robust DLM for soybean damage assessment, supporting informed agricultural decisions based on specific damage levels and enhancing crop management and productivity.

Prediction of magnesium alloy edge crack in edge-constraint rolling process by using a modified GTN model

Edge cracks are a type of damage that critically affect the quality and properties of rolled Mg alloys sheet. Accurate damage prediction plays a key role in root cause analysis and process optimization. Although the phenomenological prediction of edge damage in Mg alloy rolling has made some progress so far, the prediction of edge crack behavior and analysis of the mechanisms of crack formation and evolution during rolling have not yet been well reported. In this research, a modified Gurson-Tvergarrd-Needleman (GTN) model was proposed by modifying the growth mode of shear damage. The three-dimensional single-unit finite element (FE) model test results indicated that the proposed model improved the accuracy of the fracture response under negative stress triaxiality. Using AZ31B Mg alloy as the case study material, physical experiments and FE analysis of the rolling process were conducted. The simulations showed that the proposed model can accurately reflect the morphology and location of cracks during the rolling process, and revealed that the high-stress triaxiality at the Mg alloy edge was a significant cause of edge cracking. On this basis, an edge-constraint rolling process was proposed to inhibit edge cracking by embedding the billet into a U-shaped sheet. The numerical results showed that the minimum value of edge stress triaxiality could be reduced from -0.8 to -2.05 during edge-constraint rolling, and the accumulation of damage could be limited. The sheet forming quality was verified with different reduction rates of 30, 35, and 40%, and the results showed that the edge cracks could be effectively avoided during edge-constraint rolling, which provides an important basis for optimizing the rolling deformation process of Mg alloys.

Three-dimensional probabilistic assessment of tunneling induced structural damage using Monte-Carlo method and hybrid finite element model

Probabilistic assessment methods can be used to estimate engineering demand parameters under significant uncertainty, such as in the assessment of building damage due to tunneling. This paper develops a new empirical, theoretical and finite element hybrid three-dimensional model, in which the soil–structure interaction is considered and window and door openings are modeled explicitly, to achieve a reasonably fast and accurate damage prediction. The proposed model is embedded in a Monte-Carlo based probabilistic assessment approach, so that the building damage probabilities under different input uncertainty can be computed. Typical methods to quantify the input uncertainty in tunnel-soil–structure interaction practice are suggested. The probabilistic assessment method and a variance-based (Sobol’s) sensitivity analysis are implemented with a computer program that runs in higher-performance computing clusters, and demonstrated with two case studies. The significance of the soil–structure interaction effect and three-dimensional building effect are studied.

영향권을 활용한 태풍정보에 따른 피해정도 예측 분석

According to the Disaster Yearbook, the damage caused by typhoon amounts to about 210 billion won per year. In particular, the occurrence period of typhoon overlaps with the harvest period of crops, causing great damage to farms. Therefore, this study aims to predict the degree of damage to typhoon that will occur in the future by analyzing the damage caused by typhoon information. In order to utilize various points of the typhoon for analysis, the concept of influential sphere was introduced and analyzed. As a result of the analysis, the best results were shown in the case where the inverse distance weighted average was used as the representative value of typhoon information in the random forest model. Through the results of variable importance, it was found that the most important variable in the model was the distance. Research should be conducted from various perspectives to provide a more accurate degree of damage in the future, and it is expected that if appropriate measures are prepared based on accurate damage prediction, many farmers will be reduced from typhoon.

Material selection for solar central receiver tubes

The severe operation conditions and great capital investment of solar power tower central receivers motivate the lifetime analysis of a molten-salt external-cylindrical-tubular receiver, considering five alloy alternatives for its tubes manufacturing: Haynes 230, alloy 316H, Inconel 625, 740H and 800H. An analytical low-computational cost methodology is employed, considering the temperature dependence of tube material properties, elastic-plastic stresses/strains and stress relaxation. Thus, creep and fatigue experimental data available in the literature for these alloys are compiled in this work, providing the coefficients required for the methodology followed. A great alloys operation limitation is the film temperature to avoid corrosion issues; the most permissive are H230, 740H and 800H (650 °C), followed by Inconel 625 (630 °C) and 316H (600 °C). This, and the twice the yield strength, is regarded to set the heliostat field aiming strategy as equatorial as possible for each alloy, resulting in great power production divergences: 24% and 65% less for 625 and alloy 316H receivers with respect to the 740H receiver. Then, the lifetime analysis for a clear design day operation, representative of the receiver during ideal operation, is performed. The stress relaxation regard becomes critical for the accurate damage prediction; alloys 316H and 800H show stress reset during operation, not benefitting from a global stress relaxation. Thus, 800H exhibits a poor endurance. For the clear-day assumption, 740H shows the best lifetime and costs/power performance; the levelized cost of alloy of H230, 625 and alloy 316H is 0.01, 0.09 and over 0.25, respectively, with respect to 740H. • Integral analysis of molten salts solar central receivers in different alloys. • The coefficients to characterize creep and fatigue in these alloys are obtained. • Maximum film temperature allowable of each alloy sets a remarkable operation limit. • The stress relaxation without stress reset greatly extends the receiver lifetime. • Levelized cost of alloy metric for comparison by means of costs and energy produced.

Linear dependence of post-irradiation input bias currents on pre-irradiation values in silicon bipolar microcircuits

We find in experiments a linear dependence of ionization irradiation-induced degradations on pre-irradiation values of the input bias current in bipolar devices with simple input stages. The dependence is found to generally exist in all studied cases of different device types and different irradiation conditions. A unique behavior of the energy distribution of the interface states (Dit) under irradiation is suggested as the origin of the observed phenomenon: the generation of interface traps through the depassivation of SiH bonds located near the pre-irradiation interface traps displays a Dit as an enlargement of the initial Dit and results in the general linear dependence. A more accurate damage prediction method by using the pre-irradiation values is proposed based on the observed phenomenon.

Evaluating the grain-scale deformation behavior of a single-phase FCC high entropy alloy using synchrotron high energy diffraction microscopy

Although the deformation behavior of high-entropy alloys (HEAs) has been extensively studied at the macroscale, many important properties have yet to be explored for these alloys at the microscale, thus hampering accurate prediction of damage and failure processes. Synchrotron high-energy diffraction microscopy (HEDM) and fast-Fourier transform-based crystal plasticity modeling was conducted to investigate the three-dimensional (3D) grain-resolved micromechanical response for approximately 1,900 constituent grains within a single-phase FCC HEA up to 1% applied strain. The evolution of grain-resolved elastic strains, lattice reorientations, and maximum resolved shear stresses (mRSS) were evaluated to quantify elastic, yield, and fully plastic behavior. Overall, the initial critical resolved shear stress (CRSS), determined via in situ HEDM and companion modeling, was found to be > 20% higher than estimated using the classical polycrystalline Taylor factor (M = 3.06). However, a descriptive parameter based on the average grain-resolved Taylor factor (M¯) was found to show excellent agreement with plastic yielding behavior observed within HEDM datasets. Noticeable deviations in HEDM lattice reorientations compared to both EVP-FFT simulations and classical predictions for FCC polycrystals were discovered, highlighting the complexity in correlating local lattice reorientations, Taylor, and Schmid factors with plastic response for this material at the grain-scale. Therefore, it is anticipated that the overall trends and parameter identification of 3D grain-resolved properties in this study can serve as an important foundation for continued mesoscale investigation on both well-established and newly developed Cantor-like HEAs.

Dynamic fracture in CFRP laminates: Effect of projectile mass and dimension

The diverse mechanical properties and geometric configurations of the projectiles usually cause challenges to the accurate damage prediction of the carbon fiber reinforced plastic (CFRP) laminates. In this study, the mass and dimension of rigid projectiles are considered to explore their influence on the damage behaviors of CFRP, including dynamic deformation, fracture pattern, failure, etc. Experiments of different projectiles impacting on CFRP laminates are carried out, and a wide range of projectile kinetic energy (from 4.1 J to 548.4 J) are considered. The high-speed photography and non-invasive detection technique are used to capture dynamic fractures in CFRP laminates. Besides, a validated progressive damage numerical model is established for further investigation. The result indicates that the projectile dimension is associated with the penetration and delamination mode of CFRP, while the projectile mass affects dynamic deformation.

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry

This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

Research on the damage prediction method of offshore wind turbine tower structure based on improved neural network

Tower structure is a basic part of offshore wind power system, and how to ensure the stability and safety of tower structure in wind turbine operation and develop the efficient and accurate damage prediction method to judge the damage location and degree of tower structure has become an important research topic in the offshore wind power industry. With high adaptability, nonlinearity and strong function approximation ability, Artificial neural network (ANN) has been widely used in structural damage prediction and showed better performance than other diagnostic methods, however, traditional Back Propagation neural network (BPNN) has shortcomings of complex training parameters and low accuracy. To address this issue, this paper considered an improved neural network damage prediction method based on step-by-step identification for offshore wind turbine tower structure, an experimental research was carried out to simplify wind turbine model and experimentally measure the intact steel cylinders with different damage locations and different damage degrees. Then, the modal analysis was carried out by using the numerical analysis method and the results were compared with those of experimental measurement to modify the model. The training data are generated by Abaqus software to verify the proposed method. Results showed that the step-by-step prediction method could effectively reduce the complexity of the network, improve the prediction accuracy of the neural network, and save the training time.

A novel method of vibration modes selection for improving accuracy of frequency-based damage detection

Frequency-based damage detection techniques have been widely applied to structural health monitoring. By analysing the changes (shifts) in natural frequencies in a structure with and without damage, these techniques solve the inverse problem of determining size and location of damage. In the existing literature, the first few or random modes of frequency shifts are given to the inverse algorithms as inputs in order to predict the damage parameters. These frequency shifts can be either numerical or measured. While the accuracy of prediction in the former (numerical) case has been found to be satisfactory, the use of measured frequencies has often shown large errors. This can be attributed to unavoidable noise in frequencies, including the mismatch between FEM model and real structure, as well as the noise in the measurement itself. Previous research has shown that the noise in frequency will actually be magnified in the discrepancy of frequency shifts, and thus affect the damage prediction accuracy. And moreover, the same noise added to different modes of frequency of a damaged case will lead to the different levels of deviation in different modes of frequency shifts. This observation indicates that potentially some modes of frequency shifts are less affected by the noise than others for a given case. However, so far, there has been no studies that attempt to identify particular vibration modes of frequency shifts that are (a) less affected by the noise for all damage cases and (b) result in a more accurate prediction of damage. In this study, a novel concept of Noise Response Rate (NRR) is proposed to evaluate the sensitivity of each mode of the frequency shift to noise. Further, it is shown that selecting the vibration modes with low NRR values improves the prediction accuracy of frequency-based damage detection. The efficacy of NRR is demonstrated through a case study on a composite curved plate compared with the conventional method for damage detection.

Local microstructure-based material performance and damage in design and finite element simulations of cast components

AbstractA novel approach to incorporate local microstructure-based material performance into finite element method (FEM) simulations of cast components is presented. By adopting perspectives from natural designs as dinosaur skulls and trees, the discipline-wide approach enables accurate prediction of damage in structures based on a heterogeneous distribution of sub-scale features. It is shown that heterogeneous damage tolerance dictates the performance and failure of cast aluminum, and simulations are compared with experimental results of heterogeneous tensile samples using digital image correlation (DIC). The numerical application of the approach in the industrial product realization process of an industrial casting is demonstrated, and the applicability of the approach to understand the behavior and failure of natural as well as synthetic structures is discussed.Highlights A new discipline-wide approach to predict failure of heterogeneous structures. Based on natural designs, the approach enables tailored performance of castings. Heterogeneous damage tolerance reveal fracture and enables damage tolerant designs. The approach is implemented into numerical casting and load simulations. Future pathways to design high performing castings are explored.

Delamination analysis of woven fabrication laminates using cohesive zone model

A new test method was proposed to evaluate the cohesive strength of composite laminates. Cohesive strength and the critical strain energy for Mode-II interlamiar fracture of E-glass/epoxy woven fabrication were determined from the single lap joint (SLJ) and end notch flexure (ENF) test, respectively. In order to verify their adequacy, a cohesive zone model simulation based on interface finite elements was performed. A closed form solution for determination of the penalty stiffness parameter was proposed. Modified form of Park-Paulino-Roesler traction-separation law was provided and conducted altogether with trapezoidal and bilinear mixed-mode damage models to simulate damage using Abaqus cohesive elements. It was observed that accurate damage prediction and numerical convergence were obtained using the proposed penalty stiffness. Comparison between three damage models reveals that good simulation of fracture process zone and delamination prediction were obtained using the modified PPR model as damage model. Cohesive zone length as a material property was determined. To ensure the sufficient dissipation of energy, it was recommended that at least 4 elements should span cohesive zone length.

Identification and validation of an enhanced mesomodel for laminated composites within the WWFE-III

The accurate prediction of damage and failure in laminated composites is still a major issue in many structural applications. The paper provides a detailed description of a new damage mesomodel and examines its application to solve material and structural problems for the Test Cases proposed in the Third World-Wide Failure Exercise (WWFE-III). The cases cover various materials (glass/epoxy and carbon/epoxy), lay-ups (unidirectional, cross ply, quasi-isotropic, angle ply and general multi-directional laminates), loadings (uniaxial, biaxial, bending, thermal and loading-unloading) and features (ply sequence, size effects and open hole). The model deals with various damage scenarios and mechanisms of degradation, including diffuse intralaminar damage, diffuse interface damage, localized delamination, fibre breakage and plasticity, and can predict the evolution of a laminate’s response until final failure. Some issues concerning the limited information available in the exercise for the identification of the parameters of the mesomodel are also discussed.