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

With recent advances in non-contact sensing technology such as cameras, unmanned aerial
and ground vehicles, the structural health monitoring (SHM) community has witnessed a …

In this study, an efficient implementation of machine learning models to predict compressive
and tensile strengths of high-performance concrete (HPC) is presented. Four predictive …

In this paper, a deep collocation method (DCM) for thin plate bending problems is proposed.
This method takes advantage of computational graphs and backpropagation algorithms …

Artificial intelligence (AI) is proving to be an efficient alternative approach to classical
modeling techniques. AI refers to the branch of computer science that develops machines …

In the past two decades, structural health monitoring (SHM) systems have been widely
installed on various civil infrastructures for the tracking of the state of their structural health …

Population growth, economic development, and rapid urbanization in many areas have led
to increased exposure and vulnerability of structural and infrastructure systems to hazards …

The compressive and tensile strength of high-performance concrete (HPC) is a highly
nonlinear function of its constituents. The significance of expert frameworks for predicting the …

In this study, we propose a novel deep learning-based method to predict an optimized
structure for a given boundary condition and optimization setting without using any iterative …

This article introduces a simple and effective adaptive surrogate model to structural reliability
analysis using deep neural network (DNN). In this paradigm, initial design of experiments …