In the history of our civilization, masonry structures date back thousands of years.
Considering the different types, geometries, and arrangements of masonry components, as …

Abstract Machine learning (ML) and deep learning (DL) are both branches of AI. As a form of
AI, ML automatically adapts to changing datasets with minimal human interference. Deep …

Structural health monitoring (SHM) is of great significance to maintain the safe operation of
infrastructure, particularly for historic structures such as masonry arch bridges, as their …

A deep learning model named Cracklab for pixel level segmentation and measurement of
concrete cracks is proposed. Cracklab excels at handling cracks at image edges and …

The prediction of the seismic risk and vulnerability of bridge clusters can contribute positively
to the development of large-scale regional earthquake resilience and loss models. Using …

The damage level (DL) is a fundamental metric in earthquake engineering and is particularly
effective for multiple purposes, such as correlating structural damage to consequences and …

Most masonry constructions exhibit significant crack patterns caused by differential
foundation settlements. While modern numerical methods effectively address forward …

The aim of this paper is to assess the fatigue damage of concrete structures using
automatically classified crack severity level information. To solve the problem that the label …

In this paper, we present a method based on an ensemble of convolutional neural networks
(CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls …

Abstract Design and in-situ assessment of masonry structures is a challenging task due to
the brittle and nonlinear nature of this widely used material, the complex interaction between …