Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques

Bambang Sugiantoro; Susilo Adi Widyanto; Achmad Widodo; Sukamta Sukamta

doi:doi:10.11648/j.ijmea.20251301.11

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| Peer-Reviewed

Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques

Bambang Sugiantoro^*

, Susilo Adi Widyanto

, Achmad Widodo

, Sukamta Sukamta

Published in International Journal of Mechanical Engineering and Applications (Volume 13, Issue 1)

Received: 6 January 2025 Accepted: 23 January 2025 Published: 11 February 2025

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Abstract

This article explores advancements in damage detection and structural diagnostics for steel bridges by proposing an integrated analysis method for failure patterns and structural feasibility validation. The approach incorporates the correlation between damage causes and vibrational data classified by intensity levels. Using a supervised machine learning framework, training datasets are developed by analyzing structural behavior identified through specific vibration characteristics, specifically examining the Warren Truss type. It explored a system that diagnosed failure sequences based on vibration-classified structures within the steel bridge frame. The system generated data on the feasibility conditions by analyzing the vibration characteristics of structural elements with varying levels of damage. This vibration classification could be used as a reference for structural maintenance and repair. Machine learning diagnosis involved investigating bridge collapses to identify the types of elements and their positions within the structure, with forecasts serving as the basis for interference detection. Identifying and classifying vibration patterns in bridge structures focuses on assessing their response to potential damage and dysfunctions to ensure their safety and long-term durability. This involves using vibration-based structural health monitoring (SHM) systems that detect anomalies or changes in the dynamic behavior of bridges. The primary objective is correlating specific vibration signatures with structural defects, such as fatigue cracks, material degradation, or connection failures. This assessment categorized structural degeneration into three levels: moderate (30%), urgent (50%), and severe/critical (≥70%). The findings of the assessment group informed the design of management strategies, technical maintenance plans, and overall structural performance improvements for Warren Truss Bridges. Factual values and ductility measurements were also considered. The study provided a more detailed summary of relevant research outcomes and the developmental stages of a recent vibration-based diagnostic system for future research.

Published in	International Journal of Mechanical Engineering and Applications (Volume 13, Issue 1)
DOI	10.11648/j.ijmea.20251301.11
Page(s)	1-26
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Fatigue Analysis, Machine Learning (ML), Warren Truss, Diagnostic, Structural Health Monitoring

References

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APA Style

Sugiantoro, B., Widyanto, S. A., Widodo, A., Sukamta, S. (2025). Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques. International Journal of Mechanical Engineering and Applications, 13(1), 1-26. https://doi.org/10.11648/j.ijmea.20251301.11

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ACS Style

Sugiantoro, B.; Widyanto, S. A.; Widodo, A.; Sukamta, S. Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques. Int. J. Mech. Eng. Appl. 2025, 13(1), 1-26. doi: 10.11648/j.ijmea.20251301.11

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AMA Style

Sugiantoro B, Widyanto SA, Widodo A, Sukamta S. Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques. Int J Mech Eng Appl. 2025;13(1):1-26. doi: 10.11648/j.ijmea.20251301.11

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@article{10.11648/j.ijmea.20251301.11,
  author = {Bambang Sugiantoro and Susilo Adi Widyanto and Achmad Widodo and Sukamta Sukamta},
  title = {Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {13},
  number = {1},
  pages = {1-26},
  doi = {10.11648/j.ijmea.20251301.11},
  url = {https://doi.org/10.11648/j.ijmea.20251301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20251301.11},
  abstract = {This article explores advancements in damage detection and structural diagnostics for steel bridges by proposing an integrated analysis method for failure patterns and structural feasibility validation. The approach incorporates the correlation between damage causes and vibrational data classified by intensity levels. Using a supervised machine learning framework, training datasets are developed by analyzing structural behavior identified through specific vibration characteristics, specifically examining the Warren Truss type. It explored a system that diagnosed failure sequences based on vibration-classified structures within the steel bridge frame. The system generated data on the feasibility conditions by analyzing the vibration characteristics of structural elements with varying levels of damage. This vibration classification could be used as a reference for structural maintenance and repair. Machine learning diagnosis involved investigating bridge collapses to identify the types of elements and their positions within the structure, with forecasts serving as the basis for interference detection. Identifying and classifying vibration patterns in bridge structures focuses on assessing their response to potential damage and dysfunctions to ensure their safety and long-term durability. This involves using vibration-based structural health monitoring (SHM) systems that detect anomalies or changes in the dynamic behavior of bridges. The primary objective is correlating specific vibration signatures with structural defects, such as fatigue cracks, material degradation, or connection failures. This assessment categorized structural degeneration into three levels: moderate (30%), urgent (50%), and severe/critical (≥70%). The findings of the assessment group informed the design of management strategies, technical maintenance plans, and overall structural performance improvements for Warren Truss Bridges. Factual values and ductility measurements were also considered. The study provided a more detailed summary of relevant research outcomes and the developmental stages of a recent vibration-based diagnostic system for future research.},
 year = {2025}
}

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TY  - JOUR
T1  - Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques
AU  - Bambang Sugiantoro
AU  - Susilo Adi Widyanto
AU  - Achmad Widodo
AU  - Sukamta Sukamta
Y1  - 2025/02/11
PY  - 2025
N1  - https://doi.org/10.11648/j.ijmea.20251301.11
DO  - 10.11648/j.ijmea.20251301.11
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 1
EP  - 26
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20251301.11
AB  - This article explores advancements in damage detection and structural diagnostics for steel bridges by proposing an integrated analysis method for failure patterns and structural feasibility validation. The approach incorporates the correlation between damage causes and vibrational data classified by intensity levels. Using a supervised machine learning framework, training datasets are developed by analyzing structural behavior identified through specific vibration characteristics, specifically examining the Warren Truss type. It explored a system that diagnosed failure sequences based on vibration-classified structures within the steel bridge frame. The system generated data on the feasibility conditions by analyzing the vibration characteristics of structural elements with varying levels of damage. This vibration classification could be used as a reference for structural maintenance and repair. Machine learning diagnosis involved investigating bridge collapses to identify the types of elements and their positions within the structure, with forecasts serving as the basis for interference detection. Identifying and classifying vibration patterns in bridge structures focuses on assessing their response to potential damage and dysfunctions to ensure their safety and long-term durability. This involves using vibration-based structural health monitoring (SHM) systems that detect anomalies or changes in the dynamic behavior of bridges. The primary objective is correlating specific vibration signatures with structural defects, such as fatigue cracks, material degradation, or connection failures. This assessment categorized structural degeneration into three levels: moderate (30%), urgent (50%), and severe/critical (≥70%). The findings of the assessment group informed the design of management strategies, technical maintenance plans, and overall structural performance improvements for Warren Truss Bridges. Factual values and ductility measurements were also considered. The study provided a more detailed summary of relevant research outcomes and the developmental stages of a recent vibration-based diagnostic system for future research.
VL  - 13
IS  - 1
ER  -

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Author Information

Bambang Sugiantoro

Department of Mechanical Engineering, Diponegoro University, Semarang, Indonesia

Contact Email

http://orcid.org/0000-0002-7036-7874
Susilo Adi Widyanto

Department of Mechanical Engineering, Diponegoro University, Semarang, Indonesia

Contact Email

http://orcid.org/0000-0003-1053-8675
Achmad Widodo

Department of Mechanical Engineering, Diponegoro University, Semarang, Indonesia

Contact Email

http://orcid.org/0000-0003-0509-4555
Sukamta Sukamta

Department of Civil Engineering, Diponegoro University, Semarang, Indonesia

Contact Email

http://orcid.org/0009-0006-7239-029X

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APA Style

Sugiantoro, B., Widyanto, S. A., Widodo, A., Sukamta, S. (2025). Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques. International Journal of Mechanical Engineering and Applications, 13(1), 1-26. https://doi.org/10.11648/j.ijmea.20251301.11

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ACS Style

Sugiantoro, B.; Widyanto, S. A.; Widodo, A.; Sukamta, S. Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques. Int. J. Mech. Eng. Appl. 2025, 13(1), 1-26. doi: 10.11648/j.ijmea.20251301.11

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AMA Style

Sugiantoro B, Widyanto SA, Widodo A, Sukamta S. Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques. Int J Mech Eng Appl. 2025;13(1):1-26. doi: 10.11648/j.ijmea.20251301.11

Copy | Download

@article{10.11648/j.ijmea.20251301.11,
  author = {Bambang Sugiantoro and Susilo Adi Widyanto and Achmad Widodo and Sukamta Sukamta},
  title = {Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {13},
  number = {1},
  pages = {1-26},
  doi = {10.11648/j.ijmea.20251301.11},
  url = {https://doi.org/10.11648/j.ijmea.20251301.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20251301.11},
  abstract = {This article explores advancements in damage detection and structural diagnostics for steel bridges by proposing an integrated analysis method for failure patterns and structural feasibility validation. The approach incorporates the correlation between damage causes and vibrational data classified by intensity levels. Using a supervised machine learning framework, training datasets are developed by analyzing structural behavior identified through specific vibration characteristics, specifically examining the Warren Truss type. It explored a system that diagnosed failure sequences based on vibration-classified structures within the steel bridge frame. The system generated data on the feasibility conditions by analyzing the vibration characteristics of structural elements with varying levels of damage. This vibration classification could be used as a reference for structural maintenance and repair. Machine learning diagnosis involved investigating bridge collapses to identify the types of elements and their positions within the structure, with forecasts serving as the basis for interference detection. Identifying and classifying vibration patterns in bridge structures focuses on assessing their response to potential damage and dysfunctions to ensure their safety and long-term durability. This involves using vibration-based structural health monitoring (SHM) systems that detect anomalies or changes in the dynamic behavior of bridges. The primary objective is correlating specific vibration signatures with structural defects, such as fatigue cracks, material degradation, or connection failures. This assessment categorized structural degeneration into three levels: moderate (30%), urgent (50%), and severe/critical (≥70%). The findings of the assessment group informed the design of management strategies, technical maintenance plans, and overall structural performance improvements for Warren Truss Bridges. Factual values and ductility measurements were also considered. The study provided a more detailed summary of relevant research outcomes and the developmental stages of a recent vibration-based diagnostic system for future research.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques
AU  - Bambang Sugiantoro
AU  - Susilo Adi Widyanto
AU  - Achmad Widodo
AU  - Sukamta Sukamta
Y1  - 2025/02/11
PY  - 2025
N1  - https://doi.org/10.11648/j.ijmea.20251301.11
DO  - 10.11648/j.ijmea.20251301.11
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 1
EP  - 26
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20251301.11
AB  - This article explores advancements in damage detection and structural diagnostics for steel bridges by proposing an integrated analysis method for failure patterns and structural feasibility validation. The approach incorporates the correlation between damage causes and vibrational data classified by intensity levels. Using a supervised machine learning framework, training datasets are developed by analyzing structural behavior identified through specific vibration characteristics, specifically examining the Warren Truss type. It explored a system that diagnosed failure sequences based on vibration-classified structures within the steel bridge frame. The system generated data on the feasibility conditions by analyzing the vibration characteristics of structural elements with varying levels of damage. This vibration classification could be used as a reference for structural maintenance and repair. Machine learning diagnosis involved investigating bridge collapses to identify the types of elements and their positions within the structure, with forecasts serving as the basis for interference detection. Identifying and classifying vibration patterns in bridge structures focuses on assessing their response to potential damage and dysfunctions to ensure their safety and long-term durability. This involves using vibration-based structural health monitoring (SHM) systems that detect anomalies or changes in the dynamic behavior of bridges. The primary objective is correlating specific vibration signatures with structural defects, such as fatigue cracks, material degradation, or connection failures. This assessment categorized structural degeneration into three levels: moderate (30%), urgent (50%), and severe/critical (≥70%). The findings of the assessment group informed the design of management strategies, technical maintenance plans, and overall structural performance improvements for Warren Truss Bridges. Factual values and ductility measurements were also considered. The study provided a more detailed summary of relevant research outcomes and the developmental stages of a recent vibration-based diagnostic system for future research.
VL  - 13
IS  - 1
ER  -

Copy | Download