Objectives: The rapid proliferation of mobile technologies has exponentially increased smartphone reliance among youth, turning excessive use into a critical contemporary concern that negatively impacts well-being. This study aimed to predict smartphone addiction risk in university students using behavioral, demographic, and usage indicators through interpretable machine learning (ML) methods. Methods: Data from 304 valid student responses were analyzed using the Smartphone Addiction Scale-Short Version (SAS-SV) and a demographic questionnaire. A binary risk label was established based on eight behavioral indicators (mean: 2.85). A cut-off threshold of 3.0 classified students into LowRisk (less than 3.0) or AtRisk (greater than or equal to 3.0). Six ML algorithms—Decision Tree, Efficient Logistic Regression (ELR), Kernel Naive Bayes, K-Nearest Neighbours, Linear Discriminant Analysis, and Linear Support Vector Machine (LSVM)—were evaluated using 10-fold cross-validation and Chi-square feature selection. SHAP analysis enhanced the best-performing model’s interpretability. Results: The LSVM and ELR models demonstrated the most balanced performance, achieving an accuracy of approximately 77%. SHAP analysis revealed that feedback from the individual’s social circle regarding their smartphone usage, along with physical discomfort associated with phone use, were the most significant variables explaining addiction risk. Conclusion: These results suggest that interpretable machine learning methods can successfully identify individuals at risk using demographic and behavioral metrics, serving as effective decision-support tools for early intervention in smartphone addiction.

Metallic tubes, under axial loading, are frequently preferred in many different industries due to their high energy absorption efficiency. The studies to increase the energy absorption capacity (EAC) of these structures are still up to date. In this study, the deformation behavior and EAC of regionally pre-deformed identical aluminum and steel tubes under axial loading were investigated experimentally. Regional strength increase in tubes was obtained by the radial wall crushing (RWC) process. The RWC process is carried out by radially crushing the desired areas of the metallic tube from the outside with the aid of a specially designed device. Strengthening (hardening) occurs in the pre-deformed regions and as a result, the EAC of the metallic tubes can be increased. It is also noteworthy that the increase in absorbed energy is provided without reinforcement materials. When the experimental results were examined, it was seen that the RWC increased the energy absorption value of aluminum and steel tubes approximately 23% and by 33%, respectively. In addition to energy increases, the folding beginning of the metallic tubes can also be directed by using the RWC process.