Disasters can have devastating impacts on communities and economies, underscoring the
urgent need for effective strategic disaster risk management (DRM). Although Artificial …

This study investigates the seismic behavior of eccentrically braced frames (EBFs) taking
into account the influence of masonry infill walls using a nonmodel scenario-based machine …

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

Recently developed Machine Learning (ML) interpretability techniques have the potential to
explain how predictors influence the dependent variable in high-dimensional and non-linear …

Artificial intelligence is the branch of computer science that attempts to model cognitive
processes such as learning, adaptability and perception to generate intelligent behavior …

This study aims to determine the optimal number and combination of input parameters from
machine learning (ML) techniques, encompassing both earthquake and bridge parameters …

Despite the great promise of machine learning in the structural seismic analysis, the
deployment of advanced neural networks has been limited in practical applications because …

It is obvious that one of the most significant challenges posed by Twitter is the proliferation of
fraudulent and fake accounts, as well as the challenge of identifying these accounts. As a …

Structural health monitoring (SHM) is an important and practical procedure for ensuring the
structural integrity and serviceability of civil engineering structures such as bridges …

The present study investigates the best seismic parameters for modeling the dynamic
response of various non-linear structural systems by comparing different Machine Learning …