Horizon 2020 Marie Skłodowska-Curie Innovative Training Network

Daniel Martínez Otero

BE
Home/Daniel Martínez Otero
Daniel Martínez Otero 2017-07-03T11:07:30+00:00

Early Stage Researcher
University College Dublin (Ireland)

Project 12: Bridge damage detection using an instrumented vehicle

Click on the icons for Daniel’s email, blog and LinkedIn profile

Research Interests:

Structural dynamics; Health monitoring; Bridge assessment; Traffic loading

Biography:

In 2008, he came finalist in Math’s Olimpiad of Asturias (Spain), which took place in Gijon, and in 2010, was selected to participate in the Asturias Chemistry Olympiad. He obtained an average mark of 9.258 out of 10 in his A-level and 9.375 out of 10 in the exam to enter University. In 2015, he has completed the double mention of the Civil Engineering degree in Mieres, University of Oviedo (Spain), including a 3rd year carried out in English language in the Czech Technical University of Prague (Czech Republic). His academic record average is 7.95 out of 10. He is fluent both in Spanish and English, and he has basics of French and Czech languages.

He is enrolled in UCD (Ireland) from September 2015 thanks to the Marie Sklodowska-Curie program, where he is investigating the use of an instrumented vehicle for detecting damage from the vibration when crossing a bridge.

Research Outputs:

Publications in TRUSS

 The aim of this paper is to present the latest developments in the use of an instrumented vehicle called the Traffic Speed Deflectometer (TSD). A large axle load is applied to the pavement under the TSD. The deflection caused by this axle load is measured using several Doppler lasers. In the first step, the velocity of the deflection of the pavement is measured which can be shown to be proportional to the slope of the deformed profile. The pavement deflection is calculated in the second step using an integration model. A Winkler model is used to simulate the pavement behaviour under the axle load and the TSD is represented as a half-car model. The TSD is shown to be an effective tool for pavement damage detection. 

The aim of this paper is to present the latest developments in the use of an instrumented vehicle called the Traffic Speed Deflectometer (TSD). A large axle load is applied to the pavement under the TSD. The deflection caused by this axle load is measured using several Doppler lasers. In the first step, the velocity of the deflection of the pavement is measured which can be shown to be proportional to the slope of the deformed profile. The pavement deflection is calculated in the second step using an integration model. A Winkler model is used to simulate the pavement behaviour under the axle load and the TSD is represented as a half-car model. The TSD is shown to be an effective tool for pavement damage detection. -> Link to full text in repository

Among all the Structural Health Monitoring (SHM) recent methods found in literature, drive by monitoring has demonstrated to be promising for damage detection purposes, particularly in bridges. As curvatures can be derived from displacement measurements taken by this method, they can also be used for damage detection, which has already been successfully demonstrated. This paper describes the use of Instantaneous Curvature (IC) for that purpose. Once the absolute displacements of the bridge are measured, damage location and quantification can be obtained through IC when having a moving reference over a bridge. In this paper, a bridge is represented by a finite element model of a Euler-Bernoulli beam. A Half-Car model of a vehicle is used to represent a Traffic Speed Deflectometer (TSD), a drive-by monitoring vehicle. Damage is represented as a loss of stiffness in different parts of the bridge and 1 % measurement noise is added. A generic road profile is also considered. Healthy and damaged states of the bridge are compared in order to validate the method. -> Link to full text in repository
The Traffic Speed Deflectometer (TSD) is a vehicle incorporating a set of laser Doppler vibrometers on a straight beam to measure the relative velocity between the beam and the pavement surface. This paper describes a numerical study to see if a TSD could be used to detect damage in a bridge. From this measured velocity it is possible to obtain the curvature of the bridge, from whose analysis, it will be demonstrate that information on damage can be extracted. In this paper a Finite Element model is used to simulate the vehicle crossing a single span bridge, for which deflections and curvatures are calculated. From these numerical simulations, it is possible to predict the change in the curvature signal when the bridge is damaged. The method looks promising and it suggests that this drive-by approach is more sensitive to damage than sensors installed on the bridge itself. -> Link to full text in repository
Considerable effort has been dedicated in recent years to the development of bridge damage detection techniques. Recently, drive-by monitoring has become popular as it allows the bridge to be monitored without installing sensors on it. In this work, the Traffic Speed Deflectometer (TSD), which incorporates a set of laser Doppler sensors on a straight beam to obtain the relative velocity between the vehicle and the pavement surface, is modelled to obtain deflections on the bridge as the vehicle drives. From these deflections it is possible to obtain the curvature of the bridge, from which inferences on damage can be made. However, most of the time, the measurements taken by drive-by sensors are subject to a set of uncertainties or noise that can lead the damage detection procedure to either give false positives or to miss damage. For that reason, an analysis is needed in order to determine if these methods can work properly in uncertain or noisy environments. Moreover, as the road surface roughness affects the dynamic interaction between the vehicle and the bridge, this may also have an effect on the damage predictions. Hence, the goal of this paper is to study the sensitivity of curvature measurements to both the presence of environmental noise and the effect of the road surface roughness. -> Link to full text in repository
Drive-by monitoring has received increasing attention in recent years, as it has great potential useful for Structural Health Monitoring (SHM) applications. Although direct instrumentation of civil infrastructures has been demonstrated to be a way of detecting damage, it is also a very expensive method as it requires data acquisition, storage and transmission facilities on each bridge. Drive-by constitutes an alternative that allows the monitoring of a bridge without the necessity of installing sensors on it. In this numerical study, the vertical displacements of the bridge are used for damage detection purposes. The goal of this paper is to describe a model that can reproduce the vertical displacements of the bridge when a simulated vehicle is driving through and show how these displacements change with damage. Vertical displacements are calculated before and after damage, so that the sensitivity of the data to bridge damage can be determined.

A finite element (FE) model of a simply supported beam interacting with a moving half car is used in this study. Damage is represented as a loss of stiffness in several parts of the bridge. Vertical displacements are generated at a moving reference for healthy and damaged states, corresponding to vehicle location on the bridge. Two options are explored, the first axle and the second one, as the locations to fix the simulated sensor on the vehicle. -> Link to full text in repository