Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …

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 …

This paper proposed deep neural networks (DNNs) for the dynamic response of high-rise
buildings with one-outrigger systems under two types of seismic hazards. Using an existing …

Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been
implemented over the past two decades in different fields of simulation-based engineering …

Pan Evaporation (E p) plays a pivotal role in water resource management, particularly in arid
and semi-arid regions. This study assesses the predictive performance of a comprehensive …

In this paper, discrete element modelling and artificial neural networks (ANNs) were adopted
to evaluate seismic performance of a masonry-infilled reinforced concrete (RC) frame …

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 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 …

This study aims to develop machine learning-based prediction models for the out-of-plane
displacement of infill walls under earthquake excitation by using machine learning methods …

The accurate estimation of the fundamental time period is critical for the error-free risk and
reliability estimation of infrastructure systems. Although complex empirical models are …