Introduction of Structural Health Monitoring to Civil Engineering Education

This paper describes the development of a Structural Health Monitoring (SHM) Education Unit; its initial implementation and assessment at Louisiana State University (LSU) and the University of Louisiana- Lafayette (UL-Lafayette) during the 2016-17 Academic Year; and its subsequent re- implementation and assessment during the 2017-18 Academic Year at these institutions plus its initial implementation at four partner institutions Case Western Reserve University, Tuskegee University, University of North Florida and Virginia Tech. The SHM Education Unit encompasses the Fundamentals Education Subunit and the Applications Education Subunit. The Fundamentals Education Subunit consists of an introductory and four content online modules whereas the Applications Education Subunit consists of two content online modules, a SHM system design/evaluation module and a SHM instrumentation model demonstration. Using a pedagogical model developed during the project, the former Subunit is implemented in two classes of a structural analysis course whereas the latter Subunit is implemented in two classes of a reinforced concrete design course. The results of readiness tests and student assessments demonstrate the effectiveness of the content and the pedagogical model to engage students and teach SHM fundamentals and practices.Cockrell School of Engineerin

Structural Health Monitoring of Composite Airframe

Seminario impartido por el Profesor Aliabadi para describir los últimos logros obtenidos en el seno de los grupos de investigación que dirige en el Imperial College de Londres.Design and maintenance of future airframe composite structures is mainly influenced by the requirement to cope with accidental impact damage. The impact detection and identification strategy for existing structures is of primary importance both in structural health monitoring (SHM) and in non-destructive evaluation (NDE) techniques. Accurately detecting and characterizing an impact event based on sensor data leads us towards condition-based monitoring (CBM), where the subsequent damage can then be detected through active sensing strategies. In this talk, SHM techniques based ultrasonic guided wave will be presented for both passive and active SHM system. Application of these methods to complex stiffened panel will be shown through both experimental measurements and finite element simulations.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A method for vibration-based structural interrogation and health monitoring based on signal cross-correlation

Vibration-based structural interrogation and health monitoring is a field which is concerned with the estimation of the current state of a structure or a component from its vibration response with regards to its ability to perform its intended function appropriately. One way to approach this problem is through damage features extracted from the measured structural vibration response. This paper suggests to use a new concept for the purposes of vibration-based health monitoring. The correlation between two signals, an input and an output, measured on the structure is used to develop a damage indicator. The paper investigates the applicability of the signal cross-correlation and a nonlinear alternative, the average mutual information between the two signals, for the purposes of structural health monitoring and damage assessment. The suggested methodology is applied and demonstrated for delamination detection in a composite beam

Vibration-based methods for structural and machinery fault diagnosis based on nonlinear dynamics tools

This study explains and demonstrates the utilisation of different nonlinear-dynamics-based procedures for the purposes of structural health monitoring as well as for monitoring of robot joints

Open-source digital technologies for low-cost monitoring of historical constructions

This paper shows new possibilities of using novel, open-source, low-cost platforms for the structural health monitoring of heritage structures. The objective of the study is to present an assessment of increasingly available open-source digital modeling and fabrication technologies in order to identify the suitable counterparts of the typical components of a continuous static monitoring system for a historical construction. The results of the research include a simple case-study, which is presented with low-cost, open-source, calibrated components, as well as an assessment of different alternatives for deploying basic structural health monitoring arrangements. The results of the research show the great potential of these existing technologies that may help to promote a widespread and cost-efficient monitoring of the built cultural heritage. Such scenario may contribute to the onset of commonplace digital records of historical constructions in an open-source, versatile and reliable fashion.Peer ReviewedPostprint (author's final draft

A simple method for enhanced vibration-based structural health monitoring

This study suggests a novel method for structural vibration-based health monitoring for beams which only utilises the first natural frequency of the beam in order to detect and localise a defect. The method is based on the application of a static force in different positions along the beam. It is shown that the application of a static force on a damaged beam induces stresses at the defect which in turn cause changes in the structural natural frequencies. A very simple procedure for damage detection is suggested which uses a static force applied in just one point, in the middle of the beam. Localisation is made using two additional application points of the static force. Damage is modelled as a small notch through the whole width of the beam. The method is demonstrated and validated numerically, using a finite element model of the beam, and experimentally for a simply supported beam. Our results show that the frequency variation with the change of the force application point can be used to detect and in the same time localize very precisely even a very small defect. The method can be extended for health monitoring of other more complicated structures

Structural health monitoring and bridge condition assessment

Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work

Synergistic combination of systems for structural health monitoring and earthquake early warning for structural health prognosis and diagnosis

Earthquake early warning (EEW) systems are currently operating nationwide in Japan and are in beta-testing in California. Such a system detects an earthquake initiation using online signals from a seismic sensor network and broadcasts a warning of the predicted location and magnitude a few seconds to a minute or so before an earthquake hits a site. Such a system can be used synergistically with installed structural health monitoring (SHM) systems to enhance pre-event prognosis and post-event diagnosis of structural health. For pre-event prognosis, the EEW system information can be used to make probabilistic predictions of the anticipated damage to a structure using seismic loss estimation methodologies from performance-based earthquake engineering. These predictions can support decision-making regarding the activation of appropriate mitigation systems, such as stopping traffic from entering a bridge that has a predicted high probability of damage. Since the time between warning and arrival of the strong shaking is very short, probabilistic predictions must be rapidly calculated and the decision making automated for the mitigation actions. For post-event diagnosis, the SHM sensor data can be used in Bayesian updating of the probabilistic damage predictions with the EEW predictions as a prior. Appropriate Bayesian methods for SHM have been published. In this paper, we use pre-trained surrogate models (or emulators) based on machine learning methods to make fast damage and loss predictions that are then used in a cost-benefit decision framework for activation of a mitigation measure. A simple illustrative example of an infrastructure application is presented