An arrival time of an elastic wave is the important parameter to visualize the locations of the failures and/or elastic wave velocity distributions in the field of non-destructive testing (NDT). The arrival time detection is conducted generally using automatic picking algorithms in a measured time-history waveform. According to automatic picking algorithms, it is expected that the detected arrival time from low S/N signals has low accuracy if low S/N signals are measured in elastic wave measurements. Thus, in order to accurately detect the arrival time for NDT, the classification of measured elastic waves is required. However, the classification of elastic waves based on algorithms has not been extensively conducted. In this study, a classification method based on self-organizing maps (SOMs) is applied to classify the measured waves. SOMs visualize relation of measured data wherein the number of classes is unknown. Therefore, using SOM selects high and low S/N signals adequately from the measured waves. SOM is validated on model tests using the pencil lead breaks (PLBs), and it was confirmed that SOM successfully visualize the classes consisted of high S/N signal. Moreover, classified high S/N signals were applied to the source localization and it was noteworthy that localized sources were more accurate in comparison with using all of the measured waves.

Many numerical analysis methods for predicting the motion of soil or snow avalanches may set the characteristics of the dynamic friction obtained from field tests, such as the measurement of real avalanche and model slope tests. However, the friction characteristics of the actual flow of objects are influenced by changes in the velocity and density of the flowing objects and are not necessarily constant. In addition, determining the shear strain rate dependence of the frictional properties at low shear strain rates is important for accurately predicting the avalanche reach distance. In this research, model tests using a rotating drum device were carried out, and an artificial snow avalanche was generated. Then, the flow velocity distribution of the flow was extracted by organizing the motion of the artificial snow avalanche flowing in the drum device using the Digital Image Correlation method. Moreover, the changing characteristics of the viscosity coefficient of the pseudo flow were estimated using an inverse simulation. For the results, it was suggested that the method of estimating flow characteristics and friction characteristics from the artificial avalanche generated by the rotating drum and the time inverse analysis proposed in this study was effective, but it is necessary to confirm the issue of the need for a similar analysis using real scale. If it is found to be applicable to real scales in the future, it will contribute to the development of this field because it will expand the range of methods for analyzing avalanches using model experiments.

The impact elastic wave method (IEW) has been applied to evaluate the thickness and internal defects of the target structure based on the dominant frequency of the response wave that is formed by the repeated reflections in the thickness direction. However, it is difficult to evaluate the size and position of the defect by IEW if the size and depth are relatively small and deep, respectively, and further, it is known that the technique is inapplicable if the target is not a plate-like structures. Therefore, the authors propose a new technique that uses Difference value as a new evaluation index to overcome the limitations of the conventional methods. Difference value shows the change of the response waveform in the time domain; it is computed by using a response waveform of the structures in sound condition as a reference. In this paper, the practicality of the Difference value is investigated by performing experiments using concrete specimens. The results of the experiments demonstrate that Difference value changes by the influence of internal defects, and Difference value evaluates the location of the relatively small defect that is difficult to evaluate by the conventional technique.

This study developed a novel acoustic emission (AE) tomography algorithm for non-destructive testing (NDT) based on Lasso regression (LASSO). The conventional AE tomography method takes considerable measurement data to obtain the elastic velocity distribution for structure evaluation. However, the new algorithm in which the LASSO algorithm is applied to AE tomography eliminates these deficiencies and reconstructs equivalent velocity distribution with fewer event data to describe the defected range. Three numerical simulation models were studied to reveal the capacity of the proposed method, and the functional performance was verified by three different types of classical concrete damage numerical simulation models and compared to that of the conventional SIRT algorithm in the experiment. Finally, this study demonstrates that the LASSO algorithm can be applied in AE tomography, and the shadow parts are eliminated in resultant elastic velocity distributions with fewer measurement paths.

Acoustic emission (AE) source localization has been used to visualize progress failures generated in a wide variety of materials. In the conventional approaches, AE source localization algorithms assume that the AE signal is propagated as a straight line. However, it is supposed that progress failures form heterogeneity of elastic wave velocity distributions. Hence, with the conventional source localization, it is expected that the localization accuracy is reduced in heterogeneous materials since diffraction and refraction waves are generated. Thus, if the straight propagation waves are classified, conventional source localizations are performed in the heterogeneous materials. The self-organizing map (SOM) is one of the unsupervised learning methods, and the SOM has potential to classify straight propagation waves for the source localizations. However, the application of classified AE signals in source localization is not popular. If classified AE signals are applied to the time difference of arrival (TDOA) method, which is the popular localization method, it is expected that number of visualized sources are decreased because the algorithm does not consider the selection of the propagated wave. Although ray tracing has potential to localize a larger number of sources than the TDOA method, it is expected that the localized sources are less accurate in comparison with results of the TDOA method. In this study, classified waves were applied to two of the source localizations, and model tests based on pencil-lead breaks (PLBs) generating artificial AE sources were conducted to validate the performance of the source localizations with classified waves. The results of the validation confirmed that the maximum error in the TDOA method is larger in comparison with ray tracing conducted with 20 mm intervals of source candidates. Moreover, ray tracing localizes the same number of sources as the number of PLB tests. Therefore, ray tracing is expected to more practically localize AE sources than the TDOA method if classified waves are applied.

In the field of non-destructive investigations, the arrival time of the elastic waves is the important parameters to visualize the locations of the failures and/or elastic wave velocity distributions that are generally used for the soundness evaluation. The accuracy of the visualization depends on the accuracy of the arrival times, and it is demanded that the arrival times are detected automatically with required accuracy from huge amount of the elastic wave measurement data for the practical applications. In this paper, a method of the arrival time detection is proposed on the basis of the sparse matrix computation. The root mean square voltages in the noise of the wave form, is used as an index to separate the elastic waves between the noise and the signal, and the boundary of the index between the noise and the signal in the wave forms is determined in the sparse matrix. Therefore, the arrival times located in the boundary of the noise and it is detected by sparse matrix computations. The proposed method is verified on the model tests and compared with the conventional method, and it was confirmed that the proposed method detects more arrival time accurately in comparison with the conventional method.