Enshaeian, Alireza
(2024)
Vibration-Based Nondestructive Estimation of Neutral Temperature in Continuous Welded Rails.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
The longitudinal stress in continuous welded rails (CWRs) is a key parameter to guarantee safe operations and avoid rail buckles (sun kink) and pull-aparts occurring at extreme warm and cold temperatures, respectively. To mitigate the effect of longitudinal stress due to temperature variation, any CWR is typically pretensioned to a certain value of the rail neutral temperature (RNT) that is the temperature at which the net longitudinal stress in the rail is zero. However, over the years the RNT decreases to unknown values due to multiple factors, increasing the risk of thermal buckling. Therefore, rail owners and transportation agencies require inspection methods to evaluate the RNT in CWR.
In this study, a novel nondestructive testing (NDT) technique based on finite element analysis, rail vibrations, and machine learning is investigated to infer the RNT in rails. The overarching approach consists of triggering and measuring rail vibrations. The lateral and torsional frequency components of the few lowest modes (< 1 kHz) of vibrations are extracted and fed to a machine learning algorithm (MLA) previously trained with finite element data or benchmark experimental data. This method is expected to predict the longitudinal stress and the RNT with very few experimental data to be collected anytime anywhere. In the long-term, the key advantages of the proposed technique are the: (1) simplicity of the setup to be carried in the field; (2) low-cost of the instrumentation; (3) short duration of the needed measurements.
This dissertation presents the principal results of the study including the implementation of a finite element model of CWR, and the setup and results of one laboratory experiment and two field tests conducted at the Transportation Technology Center in Pueblo (CO) on rails on concrete and wood ties. The results of the experiments demonstrate that the success of the technique is dependent on the accuracy of the numerical model and the ability to properly identify the dynamic characteristics of the rail. The results also show that this methodology is able to predict successfully the neutral temperature of the tested rail, specifically when the MLA is trained on benchmark experimental data.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
3 June 2024 |
| Date Type: |
Publication |
| Defense Date: |
6 March 2024 |
| Approval Date: |
3 June 2024 |
| Submission Date: |
19 March 2024 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
207 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Civil and Environmental Engineering |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
continuous welded rails, rail neutral temperature, longitudinal stress, vibration, machine learning |
| Date Deposited: |
03 Jun 2024 14:38 |
| Last Modified: |
03 Jun 2024 14:38 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/45882 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}