An indirect bridge inspection method incorporating a wavelet-based damage indicator and pattern recognition

In recent years, there has been a move towards the development of indirect structural health monitoring (SHM)techniques for bridges; the low-cost vibration-based method presented in this paper is such an approach. It consists of the use of a moving vehicle fitted with accelerometers on its axles and incorporates wavelet analysis and statistical pattern recognition. The aim of the approach is to both detect and locate damage in bridges while reducing the need for direct instrumentation of the bridge. In theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach in detecting damage in a bridge from vehicle accelerations. For this purpose, the accelerations are processed using a continuous wavelet transform as when the axle passes over a damaged section, any discontinuity in the signal would affect the wavelet coefficients. Based on these coefficients, a damage indicator is formulated which can distinguish between different damage levels. However, it is found to be difficult to quantify damage of varying levels when the vehicle’s transverse position is varied between bridge crossings. In a real bridge field experiment, damage was applied artificially to a steel truss bridge to test the effectiveness of the indirect approach in practice; for this purpose a two-axle van was driven across the bridge at constant speed. Both bridge and vehicle acceleration measurements were recorded. The dynamic properties of the test vehicle were identified initially via free vibration tests. It was found that the resulting damage indicators for the bridge and vehicle showed similar patterns, however, it was difficult to distinguish between different artificial damage scenarios.

Development of a Bridge Weigh-in-Motion Sensor: Performance comparison using fibre optic and electric resistance strain sensor systems

This paper addresses the problems of effective in situ measurement of the real-time strain for bridge weigh in motion in reinforced concrete bridge structures through the use of optical fiber sensor systems. By undertaking a series of tests, coupled with dynamic loading, the performance of fiber Bragg grating-based sensor systems with various amplification techniques were investigated. In recent years, structural health monitoring (SHM) systems have been developed to monitor bridge deterioration, to assess load levels and hence extend bridge life and safety. Conventional SHM systems, based on measuring strain, can be used to improve knowledge of the bridge's capacity to resist loads but generally give no information on the causes of any increase in stresses. 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, adhesives layer, and strain sensor. The results show the benefits in the use of optical fiber networks under these circumstances and their ability to deliver data when conventional sensors cannot capture accurate strains and/or peak strains.

A role for whey acidic protein four-disulfide-core 12 (WFDC12) in the regulation of the inflammatory response in the lung

Introduction: Secretory leucocyte protease inhibitor and elafin are members of the whey acidic protein (WAP), or WAP four disulfide-core (WFDC), family of proteins and have multiple contributions to innate defence including inhibition of neutrophil serine proteases and inhibition of the inflammatory response to lipopolysaccharide (LPS). This study aimed to explore potential activities of WFDC12, a previously uncharacterised WFDC protein expressed in the lung. Methods: Recombinant expression and purification of WFDC12 were optimised in Escherichia coli. Antiprotease, antibacterial and immunomodulatory activities of recombinant WFDC12 were evaluated and levels of endogenous WFDC12 protein were characterised by immunostaining and ELISA. Results: Recombinant WFDC12 inhibited cathepsin G, but not elastase or proteinase-3 activity. Monocytic cells pretreated with recombinant WFDC12 before LPS stimulation produced significantly lower levels of the pro-inflammatory cytokines interleukin-8 and monocyte chemotactic protein-1 compared with cells stimulated with LPS alone. Recombinant WFDC12 became conjugated to fibronectin in a transglutaminase-mediated reaction and retained antiprotease activity. In vivo WFDC12 expression was confirmed by immunostaining of human lung tissue sections. WFDC12 levels in human bronchoalveolar lavage fluid from healthy and lung-injured patients were quantitatively compared, showing WFDC12 to be elevated in both patients with acute respiratory distress syndrome and healthy subjects treated with LPS, relative to healthy controls. Conclusions: Together, these results suggest a role for this lesser known WFDC protein in the regulation of lung inflammation.

A Review of Indirect Bridge Monitoring Using Passing Vehicles

Indirect bridge monitoring methods, using the responses measured from vehicles passing over bridges, are under development for about a decade. A major advantage of these methods is that they use sensors mounted on the vehicle, no sensors or data acquisition system needs to be installed on the bridge. Most of the proposed methods are based on the identification of dynamic characteristics of the bridge from responses measured on the vehicle, such as natural frequency, mode shapes, and damping. In addition, some of the methods seek to directly detect bridge damage based on the interaction between the vehicle and bridge. This paper presents a critical review of indirect methods for bridge monitoring and provides discussion and recommendations on the challenges to be overcome for successful implementation in practice.

Experimental validation of a drive-by stiffness identification method for bridge monitoring

An experimental investigation is carried out to verify the feasibility of using an instrumented vehicle to detect and monitor bridge dynamic parameters. The low-cost method consists of the use of a moving vehicle fitted with accelerometers on its axles. In the laboratory experiment, the vehicle–bridge interaction model consists of a scaled two-axle vehicle model crossing a simply supported steel beam. The bridge model also includes a scaled road surface profile. The effects of varying the vehicle model configuration and speed are investigated. A finite element beam model is calibrated using the experimental results, and a novel algorithm for the identification of global bridge stiffness is validated. Using measured vehicle accelerations as input to the algorithm, the beam stiffness is identified with a reasonable degree of accuracy.

Detection of Bridge Dynamic Parameters using an Instrumented Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure to provide adequate maintenance and guarantee the required levels of transport service and safety. In Europe, this is now a legal requirement - a European Directive requires all member states of the European Union to implement a Bridge Management System. However, the process is expensive, requiring the installation of sensing equipment and data acquisition electronics on the bridge. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges as an indicator of its structural condition. This approach eliminates the need for any on-site installation of measurement equipment. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the possibility of extracting the dynamic parameters of the bridge from the spectra of the vehicle accelerations. The effect of vehicle speed, vehicle mass and bridge span length on the detection of the bridge dynamic parameters are investigated. The algorithm is highly sensitive to the condition of the road profile and simulations are carried out for both smooth and rough profiles

Monitoring Bridge Dynamic Behaviour using an Instrumented Two Axle Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing, loading and environmental factors. A rational transport policy must monitor and provide adequate maintenance to this infrastructure to guarantee the required levels of transport service and safety. Increasingly in recent years, bridges are being instrumented and monitored on an ongoing basis due to the implementation of Bridge Management Systems. This is very effective and provides a high level of protection to the public and early warning if the bridge becomes unsafe. However, the process can be expensive and time consuming, requiring the installation of sensors and data acquisition electronics on the bridge. This paper investigates the use of an instrumented 2-axle vehicle fitted with accelerometers to monitor the dynamic behaviour of a bridge network in a simple and cost-effective manner. A simplified half car-beam interaction model is used to simulate the passage of a vehicle over a bridge. This investigation involves the frequency domain analysis of the axle accelerations as the vehicle crosses the bridge. The spectrum of the acceleration record contains noise, vehicle, bridge and road frequency components. Therefore, the bridge dynamic behaviour is monitored in simulations for both smooth and rough road surfaces. The vehicle mass and axle spacing are varied in simulations along with bridge structural damping in order to analyse the sensitivity of the vehicle accelerations to a change in bridge properties. These vehicle accelerations can be obtained for different periods of time and serve as a useful tool to monitor the variation of bridge frequency and damping with time.

Experimental Investigation of the Detection of Bridge Dynamic Parameters using a Moving Vehicle

This paper investigates the feasibility of using an instrumented vehicle to detect bridge dynamic parameters, such as natural frequency and structural damping, in a scaled laboratory experiment. In the experiment, a scaled vehicle model crosses a steel girder which has been adopted as the bridge model. The bridge model also includes a scaled road surface profile. The effects of varying vehicle model mass and speed are investigated. The damping of the girder is also varied. The bridge frequency and changes in damping are detected in the vehicle acceleration response in the presence of a rough road surface profile.

Experimental investigation of drive-by bridge inspection

This study presents a vibration-based health monitoring of short span bridges by
an inspection vehicle. How to screen health condition of short span bridges in terms of the
drive-by bridge inspection is described. Feasibility of the drive-by bridge inspection is
investigated through a scaled laboratory moving vehicle experiment. The feasibility of using an
instrumented vehicle to detect the natural frequency and changes in structural damping of a
model bridge is observed. Observations also demonstrate possibility of diagnosis of bridges by
comparing patterns of identified dynamic parameters of bridges through a periodical
monitoring. It is confirmed that the method for damage identification under a moving vehicle
identifies the damage location and severity well.