Add to ORKGImprovement in the assessment of civil structures is an important issues, because the portfolio of bridges in many developed countries keeps aging. This study presents a specific application of neural network for the calibration and the damage assessment of bridges. The focus is on the implementation of the latest state of the art in deep learning to determine and to compute the parameters that fit the finite element model of a bridge to the actual standing structure. Back propagation algorithm with multiple hidden layers is used to train the neural network: The method is first developed in matlab and then deployed in CUDA C. One real bridges are presented, where the method was applied, and one data set of acceleration time histories are an...
In this work, we propose a combined approach of model-based and machine learning techniques for dama...
This work proposes a novel supervised learning approach to identify damage in operating bridge struc...
In this paper, artificial neural networks (ANNs) are used to develop an efficient method for rapid a...
This work proposes a supervised Deep Learning approach for damage identification in bridge structure...
In recent years, artificial neural networks (ANNs) have been employed for estimation and prediction ...
Structural health monitoring is a challenging task that has recently received great attention from r...
The key parameters affecting dynamic and static responses of structural systems often change during ...
In recent years, Artificial Neural Networks (ANN) have become widely popular tools in various discip...
This study focuses on the system identification and the damage detection of reinforced concrete brid...
The Bill Emerson Cable-stayed Bridge is a newly built 1206 meter long structure crossing the Mississ...
Bridges in Ukraine are one of the most important components of the infrastructure, requiring attenti...
Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of...
Damage in structures often leads to failure. Thus it is very important to monitor structures for the...
This paper proposes the use of transmissibility functions combined with a machine learning algorithm...
AbstractThis study aims to facilitate damage detection in concrete bridge girders without the need f...
In this work, we propose a combined approach of model-based and machine learning techniques for dama...
This work proposes a novel supervised learning approach to identify damage in operating bridge struc...
In this paper, artificial neural networks (ANNs) are used to develop an efficient method for rapid a...
This work proposes a supervised Deep Learning approach for damage identification in bridge structure...
In recent years, artificial neural networks (ANNs) have been employed for estimation and prediction ...
Structural health monitoring is a challenging task that has recently received great attention from r...
The key parameters affecting dynamic and static responses of structural systems often change during ...
In recent years, Artificial Neural Networks (ANN) have become widely popular tools in various discip...
This study focuses on the system identification and the damage detection of reinforced concrete brid...
The Bill Emerson Cable-stayed Bridge is a newly built 1206 meter long structure crossing the Mississ...
Bridges in Ukraine are one of the most important components of the infrastructure, requiring attenti...
Deep learning algorithms for Structural Health Monitoring (SHM) have been extracting the interest of...
Damage in structures often leads to failure. Thus it is very important to monitor structures for the...
This paper proposes the use of transmissibility functions combined with a machine learning algorithm...
AbstractThis study aims to facilitate damage detection in concrete bridge girders without the need f...
In this work, we propose a combined approach of model-based and machine learning techniques for dama...
This work proposes a novel supervised learning approach to identify damage in operating bridge struc...
In this paper, artificial neural networks (ANNs) are used to develop an efficient method for rapid a...