Gun Jin Yun

Stress sensing performance using mechanoluminescence of SrAl2O4:Eu (SAOE) and SrAl2O4:Eu, Dy (SAOED) under mechanical loadings

In this paper, the stress sensing performance of two well-known mechanoluminescence (ML) sensing materials, (1) SrAl2O4:Eu (SAOE) and (2) SrAl2O4:Eu, Dy (SAOED), has been experimentally studied. Under the same input loadings and strain rates, changes of the light intensity have been characterized in terms of sensitivity, repeatability and linearity. Effects of the strain rate on the light intensity changes have also been investigated for both ML sensing materials. SAOED appears to perform better as an ML stress sensor than SAOE because it shows higher sensitivity and no saturation of light during the loading history. Although SAOE showed saturation of light emissions, its initial sensitivity to loading was higher than that of SAOED. Therefore, SAOE appears to be more suitable for sensors for monitoring dynamic active cracks.

Improved Damage Localization and Quantification Using Subset Selection

Because a structure’s modal parameters (natural frequencies and mode shapes) are affected by structural damage, finite- element model updating techniques are often applied to locate and quantify structural damage. However, the dynamic behavior of a structure can only be observed in a narrow knowledge space, which usually causes nonuniqueness and ill-posedness in the damage detection problem formulation. Thus, advanced optimization techniques are a necessary tool for solving such a complex inverse problem. Furthermore, a preselection process of the most significant damage parameters is helpful to improve the efficiency of the damage detection procedure. A new approach, which combines a parameter subset selection process with the application of damage functions is proposed herein to accomplish this task. Starting with a simple 1D beam, this paper first demonstrates several essential concepts related to the proposed model updating approach. A more advanced example considering a 2D model is then considered. T...

Effects of persistent luminescence decay on mechanoluminescence phenomena of SrAl 2 O 4 :Eu 2+ , Dy 3+ materials

This Letter reveals for the first time, to the best of our knowledge, the effects of stress-free persistent luminescence (PL) decay on the mechanoluminescence (ML) phenomena and the effects of stresses and strain rates on the PL decay of SrAl(2)O(4):Eu(2+), Dy(3+) (SAOED) materials. Previous research on ML phenomena in this material has focused on the effects of strain rates and stress variations on ML light intensity. However, experimental evidence provided herein shows that the ML light emission is also related to the PL decay time elapsed until the onset of stressing and the PL decay rate is dependent on the stress, strain rate, and the stress-free PL decay time interval. For quantitative stress measurements using SAOED materials, understanding of ML light sensitivity and its dependence on critical factors (strain rate, stress-free PL decay time interval, photoexcitation time, instantaneous PL decay rate, etc.) is crucially important. This Letter provides new and important perspectives that are essential for developing predictive models and/or calibration procedures for ML stress sensors.

A novel evolutionary algorithm for identifying multiple alternative solutions in model updating

This article proposes an evolutionary algorithm that is able to identify both global and local minima. This is accomplished by including two new operators to a traditional steady-state genetic algorithm. The proposed algorithm uses a single population in contrast to other evolutionary algorithms available in the literature. The algorithm is used to update a model of a structural system and provide the analyst with different plausible solutions for the updated models. Model updating techniques are used to enhance the behavior of numerical models of existing structures based on experimental data. Although the optimal updated model corresponds to the global minimum of the objective function, the model with the best physical representation of the structure could be a local minimum because of modeling errors, noise in the experimental data, errors in the extraction of system features from the experimental data and limited number sensors, among other factors. The evolutionary algorithm proposed in this article identifies global and local minima of the objective function, giving the analyst the option to choose the updated model from a set of plausible models. These models are specially designed to be as physically different as possible from each other providing the analyst with significantly different alternatives. The proposed methodology is validated with two numerical examples. The first example shows the capabilities of the technique with a mathematical function. A model updating problem using the American Society of Civil Engineering Structural Health Monitoring Benchmark structure is used for the second numerical example.

Reference-free damage detection for truss bridge structures by continuous relative wavelet entropy method

This article proposes a continuous relative wavelet entropy–based reference-free damage detection algorithm for truss bridge structures. Advantages of the proposed method are that (1) there is no need to measure dynamic response of pristine structures, in other words, the method is reference-free; (2) it is suitable for highly nonlinear and nonstationary random response data due to the multiresolution signal analysis feature of the continuous wavelet transform; and (3) it is sensitive to slight damage extents (i.e. 5%–10%) for the tested damage type (i.e. loosening of bolts). In order to demonstrate consistency and sensitivity of the proposed method, multiple experimental tests using a laboratory-size truss structure were mainly conducted for various damage scenarios and progressive damage states. The proposed continuous relative wavelet entropy–based reference-free damage detection algorithm showed reliable damage localization capabilities, and it is proven as an effective method compared to other damage detection methods that are dependent on the measurement signals from pristine structures. Due to the generality of the proposed method, applications to identify other types of damage based on different types of signals can be expected.

Delamination identification of laminated composite plates using a continuum damage mechanics model and subset selection technique

In this paper, a new model-based delamination detection methodology is presented for laminated composite plates and its performance is studied both numerically and experimentally. This methodology consists of two main parts: (1) modal analysis of an undamaged baseline finite element (FE) model and experimental modal testing of panels with delamination damage at single or multiple locations and (2) a sensitivity based subset selection technique for single or multiple delamination damage localizations. As an identification model, a higher-order finite element model is combined with a rational micromechanics-based CDM model which defines the delamination damage parameter as a ratio of delaminated area to entire area. The subset selection technique based on sensitivity of the dynamic residual force has been known to be capable of detecting multiple damage locations. However, there has been no experimental study specifically for the applications in laminated composite structures. To implement the methodology, a sensitivity matrix for the laminated composite plate model has been derived. Applications of the proposed methodology to an E-glass/epoxy symmetric composite panel composed of 16 plies [CSM/UM1208/3 layers of C1800]s = [CSM/0/(90/0)3]s with delamination damage are demonstrated both numerically and experimentally. A non-contact scanning laser vibrometer (SLV), a lead zirconate titanate (PZT) actuator and a polyvinylidene fluoride (PVDF) sensor are used to conduct experimental modal testing. From the experimental example, capabilities of the proposed methodology for damage identification are successfully demonstrated for a 2D laminated composite panel. Furthermore, various damage scenarios are considered to show its performance and detailed results are discussed for future improvements.

Development of Neural Network Based Hysteretic Models for Steel Beam-Column Connections Through Self-Learning Simulation

Beam-column connections are zones of highly complex actions and deformations interaction that often lead to failure under the effect of earthquake ground motion. Modeling of the beam-column connections is important both in understanding the behavior and in design. In this article, a framework for developing a neural network (NN) based steel beam-column connection model through structural testing is proposed. Neural network based inelastic hysteretic model for beam-column connections is combined with a new component based model under self-learning simulation framework. Self-learning simulation has the unique advantage in that it can use structural response to extract material models. Self-learning simulation is based on auto-progressive algorithm that employs the principles of equilibrium and compatibility, and the self-organizing nature of artificial neural network material models. The component based model is an assemblage of rigid body elements and spring elements which represent smeared constitutive behaviors of components; either nonlinear elastic or nonlinear inelastic behavior of components. The component based model is verified by a 3-D finite element analysis. The proposed methodology is illustrated through a self-learning simulation for a welded steel beam-column connection. In addition to presenting the first application of self-learning simulation to steel beam-column connections, a framework is outlined for applying the proposed methodology to other types of connections.

Experimental validation of multistep quantitative crack damage assessment for truss structures by finite element model updating

In this paper, a multistep damage quantification method has been experimentally validated by quantifying crack damage of load-carrying members of truss structures based on experimental vibration records. Damage quantifications are still challenging tasks for difficulties in interpreting response signals measured from engineering structures. Open crack depth is parameterized as a damage variable. The open crack in Euler–Bernoulli beam element is modeled by introducing local flexibility coefficients to the uncracked beam element with joint rotational flexibility. Mode shapes and natural frequencies measured from experimental modal testing of a damaged laboratory-size truss bridge are used in the finite element model updating for damage quantification. Predetermined curves derived for hollow circular sections with open crack are used to estimate crack depths from updated local flexibility coefficients. According to experimental validation test, the proposed approach is proven to be viable in quantifying crack damage.

Parameter estimation of a rate-dependent damage constitutive model for damage-tolerant brittle composites by Self-OPTIM analyses

This article demonstrates a novel parameter identification of a rate-dependent damage constitutive model using self-optimizing inverse method. In the self-optimizing inverse method, an implicit–explicit objective function is formulated as a function of two sets of full-field stresses/strains (implicit non-measurable variables) from two nonlinear finite element analyses, that is, force-driven and displacement-driven simulations, respectively, and global boundary displacements and forces (explicit measurable variables) from experimental tests. The self-optimizing inverse method can self-correct the damage parameter set through optimization procedures referring to global responses measured in laboratory tests. A micromechanics and fracture mechanics based damage constitutive law that accounts for the microcrack nucleation and growth is adopted. Synthetic data from impact tension test simulations were used to demonstrate successful performances of the self-optimizing inverse method in identifying the nonlinear constitutive and damage-related parameters. Comparative studies were conducted using two different optimization techniques – the simplex method and the steady-state genetic algorithm. The identified parameters proved to be identical to the reference values. Finally, in order to further verify the inverse identification method, self-optimizing inverse method analyses were conducted to identify the damage parameter set based on real experimental data from impact tension tests at different strain rates.

Nonlinear RC Structure Model Updating using Ambient Vibration Data

A new method is proposed for updating the nonlinear finite element (FE) model of a structural system. It has been recognized that in some classes of structures, the degradation of the capacity of the structure occurs with a change in the zero-crossing stiffness. A relationship is obtained between the damage parameters used in a numerical simulation and the FE model stiffness at the zero-load crossings. This relationship is used to update the state of the FE model to reflect the damage that is associated with dynamic parameters. The modal characteristics are identified using ambient vibration data. The approach has been applied to a numerical model of a RC beam-column building subassemblage under quasi-static loading to demonstrate the proposed method. For simulation purposes, a one-dimensional hysteretic load-deformation material model is used in the FE model to represent the nonlinear moment-rotation behavior of RC beam-column joints. A modal flexibility-based model updating procedure is performed to update the damage parameters based on the change in the dynamic characteristics at each zero-load crossing. Good agreement between the updating and simulated stiffness demonstrates the efficacy of the proposed method.