Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
Smart transportation: an overview of technologies and applications
As technology continues to evolve, our society is becoming enriched with more intelligent
devices that help us perform our daily activities more efficiently and effectively. One of the …
devices that help us perform our daily activities more efficiently and effectively. One of the …
A systematic review of convolutional neural network-based structural condition assessment techniques
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 …
and ground vehicles, the structural health monitoring (SHM) community has witnessed a …
Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review
Data-driven methods in structural health monitoring (SHM) is gaining popularity due to
recent technological advancements in sensors, as well as high-speed internet and cloud …
recent technological advancements in sensors, as well as high-speed internet and cloud …
Machine learning for crack detection: Review and model performance comparison
With the advancement of machine learning (ML) and deep learning (DL), there is a great
opportunity to enhance the development of automatic crack detection algorithms. In this …
opportunity to enhance the development of automatic crack detection algorithms. In this …
Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures
This paper proposes a customized convolutional neural network for crack detection in
concrete structures. The proposed method is compared to four existing deep learning …
concrete structures. The proposed method is compared to four existing deep learning …
Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage
This article proposes an unsupervised deep learning–based approach to detect structural
damage. Supervised deep learning methods have been proposed in recent years, but they …
damage. Supervised deep learning methods have been proposed in recent years, but they …
Review on automated condition assessment of pipelines with machine learning
Pipelines carrying energy products play vital roles in economic wealth and public safety, but
incidents continue occurring. Condition assessment of pipelines is essential to identify …
incidents continue occurring. Condition assessment of pipelines is essential to identify …
Anomaly detection of defects on concrete structures with the convolutional autoencoder
This paper reports the application of deep learning for implementing the anomaly detection
of defects on concrete structures, so as to facilitate the visual inspection of civil infrastructure …
of defects on concrete structures, so as to facilitate the visual inspection of civil infrastructure …
Scanning electron microscopy (SEM) image segmentation for microstructure analysis of concrete using U-net convolutional neural network
Scanning electron microscopy (SEM) images are used to evaluate the microstructure of the
concrete, there still remains challenges as the current methods are semi-automated, non …
concrete, there still remains challenges as the current methods are semi-automated, non …