In many countries, slope failure is a complex natural issue that can result in serious natural hazards, such as landslide dams. It is associated with the challenge of slope stability evaluation, which involves the classification problem of slopes and the regression problem of predicting the factor of safety (FOS) value. This study explored the implementation of machine learning to analyze slope stability using a comprehensive database of 880 homogenous slopes (266 unstable and 614 stable) based on a simulation model developed as a surrogate model. A classification model was developed to categorize slopes into three classes, including S (stable, FOS > 1.2), M (marginally stable, 1.0 ≤ FOS ≤ 1.2), and U (unstable, FOS < 1.0), and a regression model was used to predict the target FOS value. The results confirmed the efficiency of the developed classification model via testing, achieving an accuracy of 0.9222, with 96.2% accuracy for the U class, 55% for the M class, and 95.2% for the S class. When U and M are in the same class (i.e., the U + M class), the test accuracy is 0.9315, with 93.3% accuracy for the S class and 92.9% accuracy for the U + M class. The low accuracy level for class M led to minor inaccuracies, which can be attributed to a data imbalance. Additionally, the regression model was found to have a high correlation coefficient R-square value of 0.9989 and a low test mean squared error value of 5.03 × 10⁻⁴, which indicates a strong relationship between the FOS values and the selected slope parameters. The significant difference in the elapsed time between the traditional method and the developed surrogate model for slope stability analysis highlights the potential benefits of machine learning.

Recently, the acoustic emission (AE) technique has been widely applied in the field of geotechnical engineering. One of the main applications of this technique is to locate damage sources, which is known as the AE source location technique. In this research, the 3D AE source location technique based on the time difference of arrival is verified by conducting pencil lead break (PLB) tests on a cylindrical metal specimen. From the results of this study, the existing method is proven to have the least error in terms of distance from the coordinate origin, but with some errors along x, y, and z axes. When the PLB sources originate in the middle part of the specimen, the calculated result has a higher accuracy, as compared to other positions. It seems that the calculated AE sources tend to be concentrated on the central part with some errors. Moreover, outside noises induced by a hammer hit have virtually no effect on this AE source location technique.

Since acoustic emission(AE)could be produced because of sand particle crushing and/or sliding, more and more researchers have been exploring the potential of applying AE technique to localize such interactions on sand. In this paper, drained triaxial compression tests incorporating with an array of eight AE sensors were conducted on saturated dense coral sand under different constant loading rates, aimed at locating the shear band by AE source location technique. Like the back analysis of earthquake epicenter, the source location and the generation time of the AE event were calculated based on the principle of Time Difference of Arrival (TDOA) and an assumed constant wave velocity of 1500 m/s. Results showed that the AE sources were concentrated into an inclined band in the post-peak region, which was consistent with the appearance of the shear band developed in the specimens.