The digital twin concept represents an appealing opportunity to advance condition-based
and predictive maintenance paradigms for civil engineering systems, thus allowing reduced …

In this article, a deep neural network (DNN)-driven metamodel is first introduced to damage
identification of trusses utilizing acceleration signals incompletely measured from limited …

In this paper, the transient dynamic response of shear type multi-storey buildings subjected
to earthquake ground motion is generated via adversarial learning technique under different …

Stochastic approaches to structural health monitoring (SHM) are often inevitably limited by
computational constraints. For instance, for Markov chain Monte Carlo algorithms relying …

This article first proposes a multistage damage identification approach for trusses using time-
series data relied upon model order reduction (MOR) and deep neural network (DNN). In the …

This paper proposes an approach for the reliable identification of subsurface damages in
thermal images of concrete structures. The work explores how to mitigate false positives in …

An approach based on a Physics-Informed Neural Network (PINN) is introduced to tackle the
two-dimensional (2D) rheological consolidation problem in the soil surrounding twin tunnels …

In the last years, there has been a growing interest in the emerging concept of digital twins
(DTs) among software engineers and researchers. DTs not only represent a promising …

Abstract The concept of Digital Twin (DT) has emerged as a trend in various industries
development, enabling the creation of virtual models of physical objects. We conduct a …

In the application of data driven structural damage identification (SDI) based on supervised
deep learning technology, valid data demarcation is the foundation; a convolutional neural …