Abdollah Malekjafarian

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.

Pavement Condition Measurement at High Velocity using a TSD

27th annual European Safety and Reliability Conference (ESREL 2017), Portoroz, Slovenia, June, 2017

The Feasibility of Using Laser Doppler Vibrometer Measurements from a Passing Vehicle for Bridge Damage Detection

This paper investigates the feasibility of detecting local damage in a bridge using Laser Doppler Vibrometer (LDV) measurements taken from a vehicle as it passes over the bridge. Six LDVs are simulated numerically on a moving vehicle, collecting relative velocity data between the vehicle and the bridge. It is shown that Instantaneous Curvature (IC) at a moving reference, which is the curvature of the bridge at an instant in time, is sensitive to local damage. The vehicle measures Rate of Instantaneous Curvature (RIC), defined as the first derivative of IC with respect to time. A moving average filter is found to reduce the effects of noise on the RIC data. A comparison of filtered RIC measurements in healthy and damaged bridges shows that local damage can be detected well with noise-free measurements and can still be detected in the presence of noise.

Detection of vehicle wheels from images using a pseudo-wavelet filter for analysis of congested traffic

There is potential for significant savings if the safety of existing bridges can be more accurately assessed. For long-span bridges, congestion is the governing traffic load condition. The current methods of simulating congestion make assumptions about the axle-to-axle gaps maintained between vehicles. There is potential for improvement in congestion models if accurate data on axle-to-axle gaps can be obtained. In this study, the use of a camera to collect this information is put forward. A new image processing technique is proposed to detect wheels in variable light conditions. The method is based on a pseudo-wavelet filter that amplifies circles, in conjunction with an algorithm that weights features in the image according to their circularity. This new approach is compared with the Hough transform, template matching and the deformable part-based model (DPM) methods previously developed. In a sample set of 80 images, 96.9% of wheels are detected, considerably more than with the Hough transform and template matching methods. It also provides the same level of accuracy as DPM without requiring a training process.

On the estimation of bridge mode shapes from drive-by measurements

6th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Rhodes Island, Greece, June, 2017

Application of Laser Measurement to the Drive-by Inspection of Bridges

5th International Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Crete Island, Greece, 25 - 27 May, 2015