The State of the Art of Data Science and Engineering in Structural Health Monitoring
By Yuequan Bao, Zhicheng Chen, Shiyin Wei, Yang Xu, Zhiyi Tang. Engineering, 2019
436 citations recorded by the source corpus.
Abstract Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.
Subfields: Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/9f2b2111cd65cc33c0c440f4f8e548b58d8dd851
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Civil and Structural Engineering
- Hui Li
- Arman Malekloo
- Mark A. Girolami
- Ekin Ozer
- Mohammad AlHamaydeh
- Armin Dadras Eslamlou
- Mohsen Azimi
- Gokhan Pekcan
Citations in the corpus
- Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, background): “Advances in Data Science: data is considered as the core component of any SHM application [15].”
- Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights (2021, background): “44 The authors imagine that the roadmap of utilizing new technologies such as ML is not limited to cases where damage is to be detected.”
- Machine learning paradigm for structural health monitoring (2020, methodology): “The recent success of ML in various domains has renewed attention to applying it in to SHM.”
- A systematic review of convolutional neural network-based structural condition assessment techniques (2021, methodology): “…of reinforced concrete bridges Kim et al. (2018) Component-level damage assessment R-CNN-based TL (ImageNet and Cifar10) Images from UAV Bao et al. (2019) Anomaly detection DNN-stacked AE and greedy layer- wise training techniques Acceleration data Xu et al. (2019) Damage assessment…”