The application of fiber-reinforced polymer (FRP) for shear strengthening of concrete structures has become increasingly popular. However, the inherent scatter in shear test makes accurate prediction of the shear capacity a significant challenge, as traditional design code often struggle to capture the complex nonlinear interactions among multiple factors. To address this limitation, this study introduces a machine learning (ML) approach to develop a high-accuracy predictive model. A database comprising 552 experimental tests on FRP-strengthened concrete beams in shear was assembled. Three ensemble learning algorithms—Random Forest (RF), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost)—were systematically compared and evaluated against predictions from three existing design codes: ACI 440.2-23, FIB Bulletin 14, and GB 50608-2020. Results indicate that all ML models significantly outperform the existing code-based calculations. Among them, the XGBoost model demonstrated the best performance, achieving a coefficient of determination ($\mathrm{R}^2$) of 0.94 and a mean absolute percentage error (MAPE) as low as 12.81% on the test set. Interpretability analysis based on shapely additive explanations (SHAP) values further identified and elucidated the physical significance of key influencing features, such as FRP bonded height ($h_f$), beam width ($b$), and stirrup reinforcement ratio ($\rho_{s v}$), and elucidated their physical significance on the shear capacity. This study confirms the superiority and engineering application potential of data-driven approaches for predicting the shear performance of FRP-strengthened members. Moreover, high-accuracy capacity prediction enables more economical and environmentally friendly strengthening designs. This contributes to reducing material overuse, lowering construction energy consumption and carbon emissions, thereby supporting the sustainability goals of structural engineering.

Thermal ablation has been widely adopted for the treatment of liver tumors; however, treatment efficacy can be substantially compromised by vascular heat-sink effects arising from adjacent blood vessels. In this study, a comprehensive investigation was conducted to quantitatively compare radiofrequency ablation and microwave ablation with specific emphasis on vessel-induced thermal dissipation. A coupled multiphysics finite element framework was developed in COMSOL Multiphysics. For radiofrequency ablation, quasi-static electric currents were coupled with the bioheat transfer equation to model Joule heating, whereas for microwave ablation, electromagnetic wave propagation was coupled with bioheat transfer to represent dielectric heating. Thermal tissue injury was evaluated using an Arrhenius damage formulation, and treatment outcome was quantified in terms of necrotic volume fraction after a simulated ablation duration of 600 s. Two configurations were examined: a baseline liver tissue domain without vascular structures and a vascularized domain incorporating a representative 5 mm-diameter blood vessel positioned in proximity to the ablation applicator. In addition, a verification scenario was implemented by reproducing reported operating conditions from the literature to confirm the predicted magnitude of vessel-related lesion attenuation under a consistent necrosis definition. The results demonstrate that the presence of a blood vessel leads to a markedly greater reduction in predicted necrotic volume for radiofrequency ablation (34.08%) than for microwave ablation (18.96%). Furthermore, the simulated ablation morphology was found to be more spherical in radiofrequency ablation and more axially elongated in microwave ablation. These findings indicate that vascular heat-sink effects differentially influence ablation modalities and should be explicitly considered during modality selection and parameter optimization, particularly for tumors located adjacent to major hepatic vessels. The proposed computational framework provides a robust and extensible platform for pre-procedural planning and comparative evaluation of thermal ablation strategies in vascularized hepatic tissue.

This study examines Turkey’s electricity production and consumption values following the devastating earthquakes (7.7 and 7.6 MW) that occurred on February 6, 2023, affecting 14 million people in 11 provinces, and proposes solutions for system continuity in the face of natural disasters. The earthquakes severely disrupted electricity distribution systems, with average daily production falling from 859,820.94 MWh in January to 851,221.52 MWh in February, and consumption decreasing from 881,208.74 MWh to 863,619.86 MWh. This research analyses Turkey’s existing electricity infrastructure, focusing on the integration of photovoltaic (PV) systems and battery energy storage systems (BESS) as a suitable solution for maintaining electricity supply during and after natural disasters. With Turkey’s installed solar power capacity reaching 9.79% of total electricity production as of December 2022 and the rapid growth of unlicensed distributed generation systems, this study highlights how PV-BESS technology can support critical services such as communication, search and rescue, healthcare, heating, and lighting in emergencies. The findings demonstrate that strategically deploying distributed solar power systems combined with BESS can significantly increase energy resilience during emergencies and normal use, reduce reliance on fossil fuels, and ensure the continuity of essential services.