Earth slopes’ stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations’ success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes’ stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu’s limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study’s results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85). Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments.

The geomechanical properties of rock materials, such as uniaxial compression strength (UCS), are the main requirements for geo-engineering design and construction. A proper understanding of UCS has a significant impression on the safe design of different foundations on rocks. So, applying fast and reliable approaches to predict UCS based on limited data can be an efficient alternative to regular traditional fitting curves. In order to improve the prediction accuracy of UCS, the presented study attempted to utilize the support vector machine (SVM) algorithm. Multiple training and testing datasets were prepared for the UCS predictions based on a total of 120 samples recorded on limestone from the Maragheh region, northwest Iran, which were used to achieve a high precision rate for UCS prediction. The models were validated using a confusion matrix, loss functions, and error tables (MAE, MSE, and RMSE). In addition, 24 samples were tested (20% of the primary dataset) and used for the model justifications. Referring to the results of the study, the SVM (accuracy = 0.91/precision = 0.86) showed good agreement with the actual data, and the estimated coefficient of determination (R²) reached 0.967, showing that the model’s performance was impressively better than that of traditional fitting curves.

Change detection is widely utilized across various domains, such as disaster monitoring, where it aids in identifying differences between images captured at different time intervals. However, current methods often lack constraints on intermediate features and fail to comprehensively model the temporal relationships among these features. Additionally, they rely on simplistic fusion mechanisms, leading to suboptimal network performance. In this paper, we propose: (1) a Feature Information Interaction Module (FIIM) based on spatial attention to enhance semantic information; (2) a Feature Pair Fusion Module (FPFM) with dual-branch structure to model bi-temporal relationships; and (3) a Multi-Scale Supervision Method (MSSM) using contrastive learning to better constrain intermediate features. Comparative experiments conducted on the CDD and LEVIR-CD datasets demonstrate the superiority of our proposed network over existing state-of-the-art methods. Code repository: https://github.com/joyeuxni/SNIIF-Net .

Change detection is widely utilized across various domains, such as disaster monitoring, where it aids in identifying differences between images captured at different time intervals. However, current methods often lack constraints on intermediate features and fail to comprehensively model the temporal relationships among these features. Additionally, they rely on simplistic fusion mechanisms, leading to suboptimal network performance. In this paper, we propose: (1) a Feature Information Interaction Module (FIIM) based on spatial attention to enhance semantic information; (2) a Feature Pair Fusion Module (FPFM) with dual-branch structure to model bi-temporal relationships; and (3) a Multi-Scale Supervision Method (MSSM) using contrastive learning to better constrain intermediate features. Comparative experiments conducted on the CDD and LEVIR-CD datasets demonstrate the superiority of our proposed network over existing state-of-the-art methods. Code repository: https://github.com/joyeuxni/SNIIF-Net.