Review Article
Vibration-Based Failure Diagnosis of Warren Truss Structures Using Supervised Machine Learning Techniques
Issue:
Volume 13, Issue 1, February 2025
Pages:
1-26
Received:
6 January 2025
Accepted:
23 January 2025
Published:
11 February 2025
DOI:
10.11648/j.ijmea.20251301.11
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Views:
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.
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 lea...
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Research Article
Parametric Analysis and Optimization of TIG Welding for Enhanced Structural Integrity of Mild-Steel Sktm13a Pipe Butt Joints
Issue:
Volume 13, Issue 1, February 2025
Pages:
27-52
Received:
14 January 2025
Accepted:
27 January 2025
Published:
21 February 2025
DOI:
10.11648/j.ijmea.20251301.12
Downloads:
Views:
Abstract: The structural integrity of welded joints are critical factors that influence the overall safety and durability of various engineering structures, especially in the fields of construction, automotive, and pipeline industries.. This research systematically investigate the effects and interactions of welding parameters such as welding current, welding voltage, gas flow rate and welding speed for enhanced structural integrity of mild-steel SKTM13A pipe butt joints. Central Composite Design (CCD) based Response Surface Methodology (RSM) was used to investigate and optimized these Tungsten Inert Gas (TIG) welding process dependent variables to minimize responses such as residual stress, distortion in weld-ment, heat flux, and maximize Peak Temperature, tensile strength of the welded joints. The results indicated model F-values of 29.81 at a P-value of <0.0001 for the tensile strength explained the significance of the employed model. Optimal tensile strength of 308.56Mpa, minimum distortion in weldment of 0.2, Peak Temperature of 1518.45°C, residual stress of 282.724Mpa and heat flux of 1500.26Kw/min were achieved at a welding current of 140A, welding voltage 24V, gas flow rate 12lit/min and welding speed of 150 cm/min. Overall, these statistics suggest that the regression model for the desired responses are robust and adequately captures the relationship with the predictor variables. In conclusion, this research has provided valuable insights into the optimization of welding parameters using Response Surface Methodology (RSM) that can be effectively apply to drive innovation and competitiveness in the welding industry.
Abstract: The structural integrity of welded joints are critical factors that influence the overall safety and durability of various engineering structures, especially in the fields of construction, automotive, and pipeline industries.. This research systematically investigate the effects and interactions of welding parameters such as welding current, weldin...
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,
Susilo Adi Widyanto
