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Applied Sciences
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Machine Learning and Data Mining in Vibration Control and Structural...
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Machine Learning and Data Mining in Vibration Control and Structural Health Monitoring

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 2990

Special Issue Editor

Dr. Hesheng Tang

E-Mail Website
Guest Editor
1. Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China
2. State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
Interests: AI methodologies for SHM and vibration control; AI-driven discovery and innovation; hybrid physics-based and data-driven modeling

Special Issue Information

Dear Colleagues,

Artificial intelligence (machine learning, deep learning) and data science are rapidly advancing, which has the potential to enable the solution of a variety of complicated engineering challenges as well as the development of new ideas for structural health monitoring (SHM) and vibration control.

This Special Issue will be dedicated to the novel theory, technology and method of vibration control and SHM based on artificial-intelligence-based data-driven strategies.

The topics of interest include, but are not limited to, the following:

  • Data science and smart engineering for vibration control and SHM;
  • Application of digital twin technology in vibration control and SHM;
  • Novel ML paradigm for structural forward and inverse problems;
  • Smart methods, techniques or theories for the prediction of residual life of structure, localization and identification of damage, as well as vibration control;
  • Data- and physics-driven ML approaches for vibration control and SHM;
  • Smart multi-functional equipment for vibration control and SHM (e.g., sensors);
  • Novel numerical methods, experimental techniques or theories for SHM systems or the investigation of structural damage;
  • Heterogeneous data fusion approaches for SHM and vibration control.

Dr. Hesheng Tang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • data mining
  • structural health monitoring
  • vibration control
  • data- and physics-driven machine learning approaches
  • AI methodologies for SHM and vibration control
  • forward and inverse problems
  • smart multi-functional equipment
  • digital twins
  • heterogeneous data fusion technology

Published Papers (2 papers)

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Research

21 pages, 3806 KiB  
Article
Automated Identification and Localization of Rail Internal Defects Based on Object Detection Networks
by Sicheng Wang, Bin Yan, Xinyue Xu, Weidong Wang, Jun Peng, Yuzhe Zhang, Xiao Wei and Wenbo Hu
Appl. Sci. 2024, 14(2), 805; https://doi.org/10.3390/app14020805 - 17 Jan 2024
Cited by 1 | Viewed by 853
Abstract
The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high [...] Read more.
The timely identification of rail internal defects and the application of corresponding preventive measures would greatly reduce catastrophic failures, such as rail breakage. Ultrasonic rail defect detection is the current mainstream rail defect detection method thanks to its advantages of strong penetration, high accuracy, and ease to deploy. The 2D B-scan image output by ultrasonic detectors contains rich features of defects; however, rail engineers manually identify and localize the defect image, which can be time-consuming, and the image may be subject to missing identification or mistakes. This paper adopted state-of-the-art deep learning algorithms for novel B-scan images for the automatic identification and localization of rail internal tracks. First, through image pre-processing of classification, denoising, and augmentation, four categories of defect image datasets were established, namely crescent-shaped fatigue cracks, fishbolt hole cracks, rail web cracks, and rail base transverse cracks; then, four representatives of deep learning object detection networks, YOLOv8, YOLOv5, DETR, and Faster R-CNN, were trained with the defects dataset and further applied to the testing dataset for defect identification; finally, the performances of the three detection networks were compared and evaluated at the data level, the network structure level, and the interference adaptability level, respectively. The results show that the YOLOv8 network can effectively classify and localize four categories of internal rail defects in B-scan images with a 93.3% mean average precision at three images per second, and the detection time is 58.9%, 376.8%, and 123.2% faster than YOLO v5, DETR, and Faster R-CNN, respectively. The proposed approach could ensure the real-time, accurate, and efficient detection and analysis of internal defects to a rail. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining in Vibration Control and Structural Health Monitoring)
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21 pages, 9258 KiB  
Article
Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
by Yangyang Liao, Hesheng Tang, Rongshuai Li, Lingxiao Ran and Liyu Xie
Appl. Sci. 2023, 13(10), 5918; https://doi.org/10.3390/app13105918 - 11 May 2023
Cited by 1 | Viewed by 1678
Abstract
Dynamic analysis of structures is very important for structural design and health monitoring. Conventional numerical or experimental methods often suffer from the great challenges of analyzing the responses of linear and nonlinear structures, such as high cost, poor accuracy, and low efficiency. In [...] Read more.
Dynamic analysis of structures is very important for structural design and health monitoring. Conventional numerical or experimental methods often suffer from the great challenges of analyzing the responses of linear and nonlinear structures, such as high cost, poor accuracy, and low efficiency. In this study, the recurrent neural network (RNN) and long short-term memory (LSTM) models were used to predict the responses of structures with or without nonlinear components. The time series k-means (TSkmeans) algorithm was used to divide label data into different clusters to enhance the generalization of the models. The models were trained with different cluster acceleration records and the corresponding structural responses obtained by numerical methods, and then predicted the responses of nonlinear and linear structures under different seismic waves. The results showed that the two deep learning models had a good ability to predict the time history response of a linear system. The RNN and LSTM models could roughly predict the response trend of nonlinear structures, but the RNN model could not reproduce the response details of nonlinear structures (high-frequency characteristics and peak values). Full article
(This article belongs to the Special Issue Machine Learning and Data Mining in Vibration Control and Structural Health Monitoring)
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