Abstract Machine learning has become a powerful tool in structural and earthquake
engineering to accurately predict structural design parameters; however, these complex …

The natural period of vibration is one of the most significant factors used in the seismic
design of buildings. Although the building design codes and previous studies provide some …

In this study, we used four different machine learning models-artificial neural network (ANN),
support vector regression (SVR), k-nearest neighbor (KNN), and random forest (RF)-to …

In this work, the estimation of the fundamental period of vibration of masonry infilled RC
frame structures is achieved using both Machine Learning techniques and concise …

The precise prediction of the fundamental vibrational period for reinforced concrete (RC)
buildings with infilled walls is essential for structural design, especially earthquake-resistant …

The fundamental period (T FP) of vibration is the time required for a structure to complete
one full cycle of vibration, and it is one of the main features of the structural system. It highly …

The design of masonry structures requires accurate estimation of compressive strength (CS)
of hollow concrete masonry prisms. Generally, the CS of masonry prisms is determined by …

In conventional masonry buildings, masonry walls are key structural load-bearing elements.
Likewise, masonry infill walls strengthen framed constructions against lateral stress. The …

An accurate computation of the compressive strength of masonry structures is an
overarching factor in design and construction of masonry structures. This considerable …

Grouted masonry, a technique employing grout-filled hollow concrete blocks, offers superior
strength to conventional hollow blocks. Existing methods rely on empirical equations that …