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 …

The importance of designing safe and economic structures in seismically active areas is of
great importance. Thus, developing tools that would help in accurately predicting the …

One of the most important parameters used in the frame design process is the fundamental
period. Numerous relationships are provided in the regulations and articles to determine the …

The fundamental period is one of the most critical parameters for the seismic design of
structures. There are several literature approaches for its estimation which often conflict with …

Building seismic assessment is at the forefront of modern scientific research. Several
researchers have proposed methods for estimating the damage response of buildings …

Abstract Machine learning approaches can establish the complex and non-linear
relationship among input and response variables for the seismic damage assessment of …

Current building codes and design standards classify different structural components
according to their expected structural performance. Such classification is usually based on …

Abstract Machine learning (ML) has been recognized by researchers in the architecture,
engineering, and construction (AEC) industry but undermined in practice by (i) complex …

Structural health monitoring (SHM) is one of the main research topics in civil, mechanical
and aerospace engineering. In this regard, modal parameters and their trends over time can …

The compressive strength of masonry must be determined to design masonry constructions.
Although the compressive strength of masonry mostly depends on the compressive strength …