Next Article in Journal
Super-Resolution Reconstruction of Reservoir Core CT Images via GAN: Advancing Energy Extraction Accuracy
Previous Article in Journal
Fault-Tolerant Control of AGVs via Deep Feature Enhancement and Multi-Source Verification in Complex Industrial Environments
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 26
Issue 11
10.3390/s26113430
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
Correction to Sensors 2026, 26(8), 2333.
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Correction

Correction: Kazemi et al. Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System. Sensors 2026, 26, 2333

by
Mahyar Jafar Kazemi
,
Maria Rashidi
*,
Won-Hee Kang
and
Mohammad Siahkouhi
Centre for Infrastructure Engineering, Western Sydney University, Sydney, NSW 2751, Australia
*
Author to whom correspondence should be addressed.
Sensors 2026, 26(11), 3430; https://doi.org/10.3390/s26113430
Submission received: 20 May 2026 / Accepted: 22 May 2026 / Published: 28 May 2026
(This article belongs to the Section Fault Diagnosis & Sensors)
Browse Figures
Versions Notes

Figure Correction

In the original publication [1], there were mistakes in Figures 1, 6, 17, 22 and 24 as published. The corrected Figure 1, Figure 6, Figure 17, Figure 22 and Figure 24 appear below.
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Kazemi, M.J.; Rashidi, M.; Kang, W.-H.; Siahkouhi, M. Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System. Sensors 2026, 26, 2333. [Google Scholar] [CrossRef] [PubMed]
Sensors 26 03430 g001
Figure 1. Schematic survey of the maintenance and renewal process.
Figure 1. Schematic survey of the maintenance and renewal process.
Sensors 26 03430 g001
Sensors 26 03430 g006
Figure 6. Digital twin for railway.
Figure 6. Digital twin for railway.
Sensors 26 03430 g006
Sensors 26 03430 g017
Figure 17. Overview of three Digital Twin (DT) frameworks applied in railway systems: (a) DT framework for a railway steel bridge focusing on structural health monitoring, (b) DT architecture for a switch machine enabling real-time fault detection, and (c) DT infrastructure.
Figure 17. Overview of three Digital Twin (DT) frameworks applied in railway systems: (a) DT framework for a railway steel bridge focusing on structural health monitoring, (b) DT architecture for a switch machine enabling real-time fault detection, and (c) DT infrastructure.
Sensors 26 03430 g017
Sensors 26 03430 g022
Figure 22. (a) The schematic of the hybrid 3D visualisation method for pre- and post-processing; (b) CTTSIM pre-processing interface; (c) CTTSIM post-processing interface; (d) test results of the rail profiles; (e) test results of the track random irregularity in the lateral direction; (f) test results of the track random irregularity in the vertical direction.
Figure 22. (a) The schematic of the hybrid 3D visualisation method for pre- and post-processing; (b) CTTSIM pre-processing interface; (c) CTTSIM post-processing interface; (d) test results of the rail profiles; (e) test results of the track random irregularity in the lateral direction; (f) test results of the track random irregularity in the vertical direction.
Sensors 26 03430 g022
Sensors 26 03430 g024
Figure 24. Overview of Japan’s Smart Maintenance Initiative (SMI): (a) CBM cycle, (b) post-maintenance data analysis, (c) maintenance support system, and (d) AI-based rail defect detection. Arrows indicate the process flow and relationships between stages, while highlighted circles and markers denote detected defects or regions of interest.
Figure 24. Overview of Japan’s Smart Maintenance Initiative (SMI): (a) CBM cycle, (b) post-maintenance data analysis, (c) maintenance support system, and (d) AI-based rail defect detection. Arrows indicate the process flow and relationships between stages, while highlighted circles and markers denote detected defects or regions of interest.
Sensors 26 03430 g024
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Kazemi, M.J.; Rashidi, M.; Kang, W.-H.; Siahkouhi, M. Correction: Kazemi et al. Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System. Sensors 2026, 26, 2333. Sensors 2026, 26, 3430. https://doi.org/10.3390/s26113430

AMA Style

Kazemi MJ, Rashidi M, Kang W-H, Siahkouhi M. Correction: Kazemi et al. Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System. Sensors 2026, 26, 2333. Sensors. 2026; 26(11):3430. https://doi.org/10.3390/s26113430

Chicago/Turabian Style

Kazemi, Mahyar Jafar, Maria Rashidi, Won-Hee Kang, and Mohammad Siahkouhi. 2026. "Correction: Kazemi et al. Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System. Sensors 2026, 26, 2333" Sensors 26, no. 11: 3430. https://doi.org/10.3390/s26113430

APA Style

Kazemi, M. J., Rashidi, M., Kang, W.-H., & Siahkouhi, M. (2026). Correction: Kazemi et al. Toward Smart Railway Infrastructure Predictive and Optimised Maintenance Through Digital Twin (DT) System. Sensors 2026, 26, 2333. Sensors, 26(11), 3430. https://doi.org/10.3390/s26113430

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop