Detection and classification of matrix cracking in laminated composites using guided wave propagation and artificial neural networks

Mardanshahi, Ali; Nasir, Vahid; Kazemirad, Siavash; Shokrieh, M.M.

doi:10.1016/j.compstruct.2020.112403

Cited by 76 publications

(35 citation statements)

References 45 publications

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“…[32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] For example, Dahmen et al 32 investigated the elastic properties of olive wood plates using the Lamb and bulk ultrasonic wave propagation methods. Mardanshahi et al 38 used the Lamb wave propagation and artificial intelligence methods to propose an intelligent model for the detection and classification of the matrix cracking in polymer composites. Fathi et al 34,35 assessed the sensitivity of Lamb waves to the MC variation beyond the fiber saturation point through the estimation of the elastic and viscoelastic properties of green poplar wood.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, different Lamb wave modes and excitation frequencies can be utilized for the detection of various types of defects and damage with different sizes and geometry. The guided wave propagation method has been extensively used for the characterization of different types of materials and structures and damage detection purposes 32–48 . For example, Dahmen et al 32 investigated the elastic properties of olive wood plates using the Lamb and bulk ultrasonic wave propagation methods.…”

Section: Introductionmentioning

confidence: 99%

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Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

Fathi

Kazemirad

Nasir

2021

Struct Control Health Monit

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Nondestructive characterization of wood exposed to ultraviolet (UV) radiation is a critical task for health monitoring purposes since photodegradation affects the mechanical properties of materials. The main purpose of this study was to assess the effect of UV radiation on the elastic and viscoelastic properties of fir, poplar, oak, and alder wood using the Lamb wave propagation method. The Lamb wave propagation and mechanical three-point bending tests were performed on the wood specimens exposed to UV radiation for different time periods. The modulus of elasticity (MOE) and the viscoelastic properties of the specimens including the storage and loss moduli and the loss factor were estimated using the experimentally measured wave characteristics. The difference between the MOE estimated from the Lamb wave propagation and three-point bending tests was less than 4%, proving the capability of the Lamb wave propagation method for accurate nondestructive and in situ assessment of wood. It was revealed that the UV radiation led to a decrease in the MOE and storage modulus of wood specimens and an increase in their loss factor, resulting in lower stiffness and higher viscoelasticity of wood. It was also shown that the sensitivity of the loss factor to UV radiation was higher than that of the MOE and storage modulus, emphasizing the importance of the viscoelasticity assessment of wood products under photodegradation, weathering, and extreme radiation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

Fathi

Kazemirad

Nasir

2021

Struct Control Health Monit

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“…ML models predict the MOE and MOR of the UV-degraded wood specimens in the longitudinal direction using the parameters listed in Table 1. Utilizing ML for developing datadriven models has been practiced for properties prediction, 22,53 damage and fault detection, 37 and health monitoring of materials and structures. 44,45,54,55 It accounts for materials and process high variability and facilitates developing intelligent in-situ monitoring models.…”

Section: Modelingmentioning

confidence: 99%

“…The Lamb wave velocity is related to the elastic properties of the propagation medium, and the decay in the displacement amplitude with the propagation distance is related to its viscoelastic properties. 37 The Lamb wave propagation method has been extensively used for damage detection and characterization of different types of materials and structures. [38][39][40][41][42][43][44] For example, Dahmen et al 38 investigated the elastic properties of olive wood plates using the Lamb and bulk ultrasonic wave propagation methods.…”

Section: Introductionmentioning

confidence: 99%

Combined machine learning–wave propagation approach for monitoring timber mechanical properties under UV aging

Nasir

Fathi

Kazemirad

2021

Structural Health Monitoring

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This study proposes a combined machine learning–wave propagation approach for nondestructive prediction of the modulus of elasticity (MOE) and rupture (MOR) of timber subjected to ultraviolet (UV) radiation. Fir, poplar, alder, and oak wood specimens were subjected to artificial UV aging and assessed using the Lamb wave propagation. Different features including the wave characteristics and the viscoelastic properties of the specimens were obtained from the Lamb wave propagation tests. The extracted features trained a decision tree model for MOE and MOR prediction. The UV radiation caused a decrease in the elastic properties of wood but increased its viscoelasticity. The results also showed a decrease in the wave velocity and an increase in the wave amplitude decay with the UV exposure time. It was revealed that compared with the wave velocity, the wave amplitude decay was better correlated to the MOE of MOR of UV-degraded wood. The MOE and MOR of UV-degraded wood were accurately predicted by the machine learning models fed by the features extracted from the Lamb wave propagation tests, where the shear storage modulus was found as the most important feature for training the models. It was concluded that the proposed approach offers a great tool for in-situ monitoring of wood structures under weathering and photodegradation conditions.

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“…A recent breakthrough in computation and artificial intelligence provides a new solution for porosity prediction. e machine learning method has been proved to be a powerful tool to deal with complex multiparameter and nonlinear problems, especially for dealing with highly nonlinear noise and incomplete data sets [21][22][23]. In recent years, machine learning approaches such as artificial neural networks (ANNs) have been widely used in the nondestructive testing area [24][25][26], especially for the porosity characterization of thermal barrier coatings.…”

Section: Introductionmentioning

confidence: 99%

Porosity Characterization of Thermal Barrier Coatings by Ultrasound with Genetic Algorithm Backpropagation Neural Network

Zhang

Guo

et al. 2021

Complexity

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Porosity is considered as one of the most important indicators for the characterization of the comprehensive performance of thermal barrier coatings (TBCs). In this study, the ultrasonic technique and the artificial neural network optimized with the genetic algorithm (GA_BPNN) are combined to develop an intelligent method for automatic detection and accurate prediction of TBCs’s porosity. A series of physical models of plasma-sprayed ZrO2 coating are established with a thickness of 288 μm and porosity varying from 5.71% to 26.59%, and the ultrasonic reflection coefficient amplitude spectrum (URCAS) is constructed based on the time-domain numerical simulation signal. The characteristic features f 1 , f 2 , A max , Δ A of the URCAS, which are highly dependent on porosity, are extracted as input data to train the GA_BPNN model for predicting the unknown porosity. The average error of the prediction results is 1.45%, which suggests that the proposed method can achieve accurate detection and quantitative characterization of the porosity of TBCs with complex pore morphology.

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Detection and classification of matrix cracking in laminated composites using guided wave propagation and artificial neural networks

Cited by 76 publications

References 45 publications

Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

Mechanical degradation of wood under ultraviolet radiation characterized by Lamb wave propagation

Combined machine learning–wave propagation approach for monitoring timber mechanical properties under UV aging

Porosity Characterization of Thermal Barrier Coatings by Ultrasound with Genetic Algorithm Backpropagation Neural Network

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