Add to ORKGDeep learning (DL) is currently being researched and implemented to solve civil engineering related problems, including autonomous inspection and inventory of civil infrastructure projects. The article introduces DL, specifically a convolutional neural network and the supervised learning process used to train a model that will enable professional structural engineers to automatically detect types of earthquake damage
With Deep Learning (DL) emerging from Machine Learning (ML) to become one of the greatest technologi...
The construction industry is known to be overwhelmed with resource planning, risk management and log...
An illustrative non-technical review was published on Towards Data Science regarding our recent Jour...
This paper presents a brief overview of vibration-based damage identification studies based on Deep ...
The amount of structural damage image data produced in the aftermath of an earthquake can be stagger...
A resilient infrastructure system remains a top priority for Canada as it is hinged to strong econo...
Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have recently...
Research on damage detection of structures using image process- ing techniques has been actively con...
This study focuses on the development and evaluation of deep learning image classification models fo...
The adoption of artificial intelligence in post-earthquake inspections and reconnaissance has receiv...
Purpose: The massive number of pavements and buildings coupled with the limited inspection resources...
This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fract...
This paper reports the early findings of an ongoing project aimed at developing new methods to upgra...
Previously, it was nearly impossible to use raw time series sensory signals for structural health mo...
The objective of the group is to investigate the application of Reinforced Deep Learning to autonomo...
With Deep Learning (DL) emerging from Machine Learning (ML) to become one of the greatest technologi...
The construction industry is known to be overwhelmed with resource planning, risk management and log...
An illustrative non-technical review was published on Towards Data Science regarding our recent Jour...
This paper presents a brief overview of vibration-based damage identification studies based on Deep ...
The amount of structural damage image data produced in the aftermath of an earthquake can be stagger...
A resilient infrastructure system remains a top priority for Canada as it is hinged to strong econo...
Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have recently...
Research on damage detection of structures using image process- ing techniques has been actively con...
This study focuses on the development and evaluation of deep learning image classification models fo...
The adoption of artificial intelligence in post-earthquake inspections and reconnaissance has receiv...
Purpose: The massive number of pavements and buildings coupled with the limited inspection resources...
This paper is devoted to the development of a deep learning- (DL-) based model to detect crack fract...
This paper reports the early findings of an ongoing project aimed at developing new methods to upgra...
Previously, it was nearly impossible to use raw time series sensory signals for structural health mo...
The objective of the group is to investigate the application of Reinforced Deep Learning to autonomo...
With Deep Learning (DL) emerging from Machine Learning (ML) to become one of the greatest technologi...
The construction industry is known to be overwhelmed with resource planning, risk management and log...
An illustrative non-technical review was published on Towards Data Science regarding our recent Jour...