The integration of hoarding frames with metal-framed host structures is an essential aspect of temporary construction setups, especially in urban environments. These frames, often used as protective barriers around construction sites, can influence the structural response of the host framework, particularly during environmental loading such as wind and seismic events. This study investigates the impact of hoarding frames on the structural behavior of metal-framed buildings, with an emphasis on load distribution, dynamic response, and overall stability. Finite element analysis (FEA) and experimental validation are employed to assess the changes in stress distribution, deflection patterns, and vibration characteristics when hoarding frames are present. Results indicate that the presence of hoarding frames can lead to both positive and negative effects on the host structure, depending on factors such as the hoarding’s material properties, connection stiffness, and geometry. The findings suggest that, under certain conditions, the hoarding frame can act as a wind or seismic shield, reducing the overall loading on the host structure. However, improper design or installation may introduce additional forces that could compromise structural integrity. Recommendations for optimizing hoarding frame design to mitigate adverse impacts and enhance structural performance are also discussed.

The integration of hoarding frames with metal-framed host structures is an essential aspect of temporary construction setups, especially in urban environments. These frames, often used as protective barriers around construction sites, can influence the structural response of the host framework, particularly during environmental loading such as wind and seismic events. This study investigates the impact of hoarding frames on the structural behavior of metal-framed buildings, with an emphasis on load distribution, dynamic response, and overall stability. The Square Pole exhibits the highest natural frequency, indicating that it is the stiffest structure among the three. This makes it the most resistant to vibrational forces, meaning it will perform well in applications where minimizing movement and resisting oscillations is critical (e.g., in high wind zones or when subjected to mechanical vibrations).The Cylindrical Pole has a moderate stiffness compared to the other poles. Its frequency is lower than the square pole but higher than the multi-fillet pole, indicating a balance between flexibility and rigidity. The Multi-Fillet Pole has the lowest natural frequency, meaning it is the most flexible and the least resistant to dynamic forces among the three designs. Its design emphasizes rigidity, which is advantageous when structural integrity under dynamic loads is a priority.

The classification of EEG signals plays a critical role in the early detection of epileptic seizures, enabling timely intervention. Logistic Regression (LR) is a computationally efficient algorithm, making it particularly suitable for rapidly identifying seizure patterns within EEG data. This research introduces a machine learning (ML) method designed to categorize EEG patterns, distinguishing between normal and seizure-like signals. The proposed approach leverages a logistic regression-based seizure detection (LRSD) algorithm for accurate classification. In preprocessing, a finite impulse response (FIR) filter removes noise and artifacts from the EEG signals to improve signal quality. To boost the reliability of seizure detection, the algorithm utilizes seven distinct features, calculated to enhance statistical robustness. Additionally, multi-frequency characteristics are extracted using wavelet filters, enabling a comprehensive analysis in the frequency domain. This work benchmarks the proposed LRSD model against leading methods, with results showing that the new approach achieves a peak accuracy of 100% on the tested dataset.