Moving vehicle load identification from bridge responses based on method of moments (MOM)

A MOM-based algorithm (MOMA) is proposed for identifying of the time-varying moving vehicle loads on a bridge in this paper. A series of numerical simulations and experiments in laboratory have been studied and the proposed MOMA are compared with the existing time domain method (TDM). A few main parameters, such as basis function terms, executive CPU time, Nyquist fraction of digital filter, two different solutions to the ill-posed system equation, etc, have been investigated. Both the numerical simulation and experimental results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent, but its CPU execution time is just less than one tenth of the TDM

Classification of railway bridges based on criticality and vulnerability factors

Bridges are currently rated individually for maintenance and repair action according to the structural conditions of their elements. Dealing with thousands of bridges and the many factors that cause deterioration, makes this rating process extremely complicated. The current simplified but practical methods are not accurate enough. On the other hand, the sophisticated, more accurate methods are only used for a single or particular bridge type. It is therefore necessary to develop a practical and accurate rating system for a network of bridges. The first most important step in achieving this aim is to classify bridges based on the differences in nature and the unique characteristics of the critical factors and the relationship between them, for a network of bridges. Critical factors and vulnerable elements will be identified and placed in different categories. This classification method will be used to develop a new practical rating method for a network of railway bridges based on criticality and vulnerability analysis. This rating system will be more accurate and economical as well as improve the safety and serviceability of railway bridges

Review: Acoustic emission technique - Opportunities, challenges and current work at QUT

Acoustic emission (AE) is the phenomenon where high frequency stress waves are generated by rapid release of energy within a material by sources such as crack initiation or growth. AE technique involves recording these stress waves by means of sensors placed on the surface and subsequent analysis of the recorded signals to gather information such as the nature and location of the source. AE is one of the several non-destructive testing (NDT) techniques currently used for structural health monitoring (SHM) of civil, mechanical and aerospace structures. Some of its advantages include ability to provide continuous in-situ monitoring and high sensitivity to crack activity. Despite these advantages, several challenges still exist in successful application of AE monitoring. Accurate localization of AE sources, discrimination between genuine AE sources and spurious noise sources and damage quantification for severity assessment are some of the important issues in AE testing and will be discussed in this paper. Various data analysis and processing approaches will be applied to manage those issues

Synthetic rating system for railway bridge management

Railway bridges deteriorate with age. Factors such as environmental effects on different materials of a bridge, variation of loads, fatigue, etc will reduce the remaining life of bridges. Bridges are currently rated individually for maintenance and repair actions according to the structural conditions of their elements. Dealing with thousands of bridges and several factors that cause deterioration, makes the rating process extremely complicated. Current simplified but practical rating methods are not based on an accurate structural condition assessment system. On the other hand, the sophisticated but more accurate methods are only used for a single bridge or particular types of bridges. It is therefore necessary to develop a practical and accurate system which will be capable of rating a network of railway bridges. This paper introduces a new method for rating a network of bridges based on their current and future structural conditions. The method identifies typical bridges representing a group of railway bridges. The most crucial agents will be determined and categorized to criticality and vulnerability factors. Classification based on structural configuration, loading, and critical deterioration factors will be conducted. Finally a rating method for a network of railway bridges that takes into account the effects of damaged structural components due to variations in loading and environmental conditions on the integrity of the whole structure will be proposed. The outcome of this research is expected to significantly improve the rating methods for railway bridges by considering the unique characteristics of different factors and incorporating the correlation between them

Separation and extraction of bridge dynamic strain data (in Chinese)

Through comparing the measured data of dynamic strains due to loading and temperature by the strain gauge and temperature sensor at the same location, the information in the strain data was divided into three parts in the frequency domain by using the defined index named PSD (power spectra density)- ratio. The three parts are dominated respectively by temperature varying, stresses and noises and can be distinguished from the determined values of the separatirix frequencies. Then a simple algorithm was developed to separate the three types of information, and to extract the strain caused mainly by structural stresses. As an application of the proposed method, the influence of strain deformation and noises. As an application of the proposed method, the influence of strain deformation and noises on the fatigue assessment was investigated based on the separated data. The results show that, the determined values of separatrix frequencies are valuable for the monitoring data from other bridges. The algorithm is a multi resolution and hierarchical method, which has been validated as a simple and effective method for data analyses, and is suitable for the compression and pre-processing of the great amount monitoring data and easy to be integrated in the SHM's (structural health monitoring)software system. The strain due to temperature varying attributes only a little to the errors of fatigue assessment. However, the noises or random disturbance existed in the monitoring data have much responsibility for the errors, the main reason is that the random disturbance shifts the real strain/stress amplitude picked up by real structural stress or strain

Damage quantification techniques in acoustic emission monitoring

Acoustic emission (AE) analysis is one of the several diagnostic techniques available nowadays for structural health monitoring (SHM) of engineering structures. Some of its advantages over other techniques include high sensitivity to crack growth and capability of monitoring a structure in real time. 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). In AE technique, these stress waves are recorded by means of suitable sensors placed on the surface of a structure. Recorded signals are subsequently analysed to gather information about the nature of the source. By enabling early detection of crack growth, AE technique helps in planning timely retrofitting or other maintenance jobs or even replacement of the structure if required. In spite of being a promising tool, some challenges do still exist behind the successful application of AE technique. Large amount of data is generated during AE testing, hence effective data analysis is necessary, especially for long term monitoring uses. Appropriate analysis of AE data for quantification of damage level is an area that has received considerable attention. Various approaches available for damage quantification for severity assessment are discussed in this paper, with special focus on civil infrastructure such as bridges. One method called improved b-value analysis is used to analyse data collected from laboratory testing

Hermeneutics in Primary Care: Corneal Ulceration Treated with Bandage Contact Lenses / Alternate title: Case Report - Corneal Ulceration Treated with Bandage Contact Lenses

Bandage contact lens use in primary care optometry can be important for allowing people to live without painful effects of corneal disease. This particular case study describes the initial healing of ulceration with a bandage contact lens inserted and also the long term issues of recurrence of ulceration with cessation of lens wear and management of microbial keratoconjunctivitis with bandage lens wear. In an attempt to address holistic themes this paper utilises a hermeneutical approach to clinical judgement in a primary care setting

Cybermobbing Behaviors on Social Networking Sites: A Social Impact and Dual Social Influences Perspective

Social networking sites (SNSs) have become not only a popular source for online social interactions, but also a central focus for cybermobbing activity. While researchers in the Information Systems (IS) field have initiated investigations into cyberbullying occurrences and the associative negative impacts, a theoretical understanding of what drives individuals to participate in cybermobbing, as a form of collective bullying behaviors on SNSs, remains scarce. Specifically, SNSs provide the capability to quickly receive and distribute social information; exposure to such information has substantial impacts on individuals’ attitudes and behaviors, potentially swaying them into joining others to attack a target. Drawing on social impact theory and dual social influence processes, we propose a research model to explain how social forces (as manifested through various social information cues) trigger two social influence processes (namely normative and informational), which in turn, lead individuals to participate in cybermobbing activity on SNSs. The research model will be tested with an online survey study using the structural equation modeling approach. The study is expected to contribute to the growing body of knowledge of cybermobbing on SNSs and provide recommendations for possible interventions