The suitability of using accumulated plastic strain to assess the damage at the rail-wheel interfaces

Numerous Abaqus [1] finite element analyses have been carried out using various plasticity models to investigate the effect of friction force on the rail head in relation to both the development of the accumulated plastic strain (PEEQ) and the changes in the depth of PEEQ distribution in the wheel-rail contact. The normal force distribution on the rail head was assumed to be Hertzian. The tangential force was implemented as a fraction of the normal force in the subroutine. Each analysis was carried out for a single pass and the effect of various friction coefficient values has been observed.

Investigation of surface ratchetting due to rail/wheel contact

This project advances the knowledge of rail wear and crack formation due to rail/wheel contact in Australian heavy-haul railway lines. This comprehensive study utilised numerous techniques including: simulation using a twin-disk test-rig, scanning electron microscope particle analysis and finite element modeling for material failure prediction. Through this work, new material failure models have been developed which may be used to predict the lifetime and reliability of materials undergoing severe contact conditions.

A study on the use of acoustic emission technique as a structural health monitoring tool

As civil infrastructures such as bridges age, there is a concern for safety and a need for cost-effective and reliable monitoring tool. Different diagnostic techniques are available nowadays for structural health monitoring (SHM) of bridges. Acoustic emission is one such technique with potential of predicting failure. The phenomenon of rapid release of energy within a material by crack initiation or growth in form of stress waves is known as acoustic emission (AE). AEtechnique involves recording the stress waves bymeans of sensors and subsequent analysis of the recorded signals,which then convey information about the nature of the source. AE can be used as a local SHM technique to monitor specific regions with visible presence of cracks or crack prone areas such as welded regions and joints with bolted connection or as a global technique to monitor the whole structure. Strength of AE technique lies in its ability to detect active crack activity, thus helping in prioritising maintenance work by helping focus on active cracks rather than dormant cracks. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. One is the generation of large amount of data during the testing; hence an effective data analysis and management is necessary, especially for long term monitoring uses. Complications also arise as a number of spurious sources can giveAEsignals, therefore, different source discrimination strategies are necessary to identify genuine signals from spurious ones. Another major challenge is the quantification of damage level by appropriate analysis of data. Intensity analysis using severity and historic indices as well as b-value analysis are some important methods and will be discussed and applied for analysis of laboratory experimental data in this paper.

Fuzzy-logic-based health monitoring and residual-life prediction for composite helicopter rotor

A health-monitoring and life-estimation strategy for composite rotor blades is developed in this work. The cross-sectional stiffness reduction obtained by physics-based models is expressed as a function of the life of the structure using a recent phenomenological damage model. This stiffness reduction is further used to study the behavior of measurable system parameters such as blade deflections, loads, and strains of a composite rotor blade in static analysis and forward flight. The simulated measurements are obtained using an aeroelastic analysis of the composite rotor blade based on the finite element in space and time with physics-based damage modes that are then linked to the life consumption of the blade. The model-based measurements are contaminated with noise to simulate real data. Genetic fuzzy systems are developed for global online prediction of physical damage and life consumption using displacement- and force-based measurement deviations between damaged and undamaged conditions. Furthermore, local online prediction of physical damage and life consumption is done using strains measured along the blade length. It is observed that the life consumption in the matrix-cracking zone is about 12-15% and life consumption in debonding/delamination zone is about 45-55% of the total life of the blade. It is also observed that the success rate of the genetic fuzzy systems depends upon the number of measurements, type of measurements and training, and the testing noise level. The genetic fuzzy systems work quite well with noisy data and are recommended for online structural health monitoring of composite helicopter rotor blades.

A novel method of embedding distributed optical fiber sensor for structural health monitoring

A distributed optical fiber sensor based on Brillouin scattering (BOTDR or BOTDA) can measure and monitor strain and temperature generated along optical fiber. Because it can measure in real-time with high precision and stability, it is quite suitable for health monitoring of large-scale civil infrastructures. However, the main challenge of applying it to structural health monitoring is to ensure it is robust and can be repaired by adopting a suitable embedding method. In this paper, a novel method based on air-blowing and vacuum grouting techniques for embedding long-distance optical fiber sensors was developed. This method had no interference with normal concrete construction during its installation, and it could easily replace the long-distance embedded optical fiber sensor (LEOFS). Two stages of static loading tests were applied to investigate the performance of the LEOFS. The precision and the repeatability of the LEOFS were studied through an overloading test. The durability and the stability of the LEOFS were confirmed by a corrosion test. The strains of the LEOFS were used to evaluate the reinforcing effect of carbon fiber reinforced polymer and thereby the health state of the beams.

Assessment Of various sensors for structural health monitoring for bridge weigh-in-motion (B-WIM).

In recent years, Structural Health Monitoring (SHM) systems have been developed to monitor bridge deterioration, assess real load levels and hence extend bridge life and safety. A road bridge is only safe if the stresses caused by the passing vehicles are less than the capacity of the bridge to resist them. Conventional SHM systems can be used to improve knowledge of the bridges capacity to resist stresses but generally give no information on the causes of any increase in stresses (based on measuring strain). The concept of in Bridge Weigh-in-Motion (B-WIM) is to establish axle loads, without interruption to traffic flow, by using strain sensors at a bridge soffit and subsequently converting the data to real time axle loads or stresses. Recent studies have shown it would be most beneficial to develop a portable system which can be easily attached to existing and new bridge structures for a specified monitoring period. The sensors could then be left in place while the data acquisition can be moved for various other sites. Therefore it is necessary to find accurate sensors capable of capturing peak strains under dynamic load and suitable methods for attaching these strain sensors to existing and new bridge structures. Additionally, it is important to ensure accurate strain transfer between concrete and steel, the adhesives layer and the strain sensor. This paper describes research investigating the suitably of using various sensors for the monitoring of concrete structures under dynamic vehicle load. Electrical resistance strain (ERS) gauges, vibrating wire (VW) gauges and fibre optic sensors (FOS) are commonly used for SHM. A comparative study will be carried out to select a suitable sensor for a bridge Weigh in Motion System. This study will look at fixing methods, durability, scanning rate and accuracy range. Finite element modeling is used to predict the strains which are then validated in laboratory trials.

Smart structures health monitoring using artificial neural network

This paper presents a non-model based technique to detect and locate structural damage with the use of artificial neural networks. This method utilizes high frequency structural excitation (typically greater than 30 kHz) through a surface-bonded piezoelectric sensor/actuator to detect changes in structural point impedance due to the presence of damage. Two sets of artificial neural networks were developed in order to detect, locate and characterize structural damage by examining changes in the measured impedance curves. A simulation beam model was developed to verify the proposed method. An experiment was successfully performed in detecting damage on a 4-bay structure with bolted-joints, where the bolts were progressively released.