Vibration-Based Nondestructive Estimation of Neutral Temperature in Continuous Welded Rails

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

Challenges in Bridge Health Monitoring: A Review

Bridge health monitoring is increasingly relevant for the maintenance of existing structures or new structures with innovative concepts that require validation of design predictions. In the United States there are more than 600,000 highway bridges. Nearly half of them (46.4%) are rated as fair while about 1 out of 13 (7.6%) is rated in poor condition. As such, the United States is one of those countries in which bridge health monitoring systems are installed in order to complement conventional periodic nondestructive inspections. This paper reviews the challenges associated with bridge health monitoring related to the detection of specific bridge characteristics that may be indicators of anomalous behavior. The methods used to detect loss of stiffness, time-dependent and temperature-dependent deformations, fatigue, corrosion, and scour are discussed. Owing to the extent of the existing scientific literature, this review focuses on systems installed in U.S. bridges over the last 20 years. These are all major factors that contribute to long-term degradation of bridges. Issues related to wireless sensor drifts are discussed as well. The scope of the paper is to help newcomers, practitioners, and researchers at navigating the many methodologies that have been proposed and developed in order to identify damage using data collected from sensors installed in real structures

Bridge Health Monitoring Using Strain Data and High-Fidelity Finite Element Analysis

This article presented a physics-based structural health monitoring (SHM) approach applied to a pretensioned adjacent concrete box beams bridge in order to predict the deformations associated with the presence of transient loads. A detailed finite element model was generated using ANSYS software to create an accurate model of the bridge. The presence of concentrated loads on the deck at different locations was simulated, and a static analysis was performed to quantify the deformations induced by the loads. Such deformations were then compared to the strains recorded by an array of wireless strain gauges during a controlled truckload test performed by an independent third party. The test consisted of twenty low-speed crossings at controlled distances from the bridge parapets using a truck with a certified load. The array was part of a SHM system that consisted of 30 wireless strain gauges. The results of the comparative analysis showed that the proposed physics-based monitoring is capable of identifying sensor-related faults and of determining the load distributions across the box beams. In addition, the data relative to near two-years monitoring were presented and showed the reliability of the SHM system as well as the challenges associated with environmental effects on the strain reading. An ongoing study is determining the ability of the proposed physics-based monitoring at estimating the variation of strain under simulated damage scenarios