MS17.1 - Structural Health Monitoring

Deterioration of bridge infrastructure is a serious concern to transport and government agencies as it declines serviceability and reliability of bridges and jeopardizes public safety. Bridge infrastructure, in addition to serving the crucial function of connecting highways, is the most vulnerable constituent of the transportation system. This is often attributed to their exposure to harsh environmental settings as well as heavy loads and traffic volumes that bridges need to sustain. Maintenance and rehabilitation need of bridge infrastructure are periodically monitored and assessed, typically, every two years. Traditional or existing inspection techniques, namely visual inspection, have multiple disadvantages such as subjective, time-consuming, and often incomplete. These are laborious and associated with incomplete assessment due to poor accessibility to critical segments of the bridge, cause traffic disruption, and entail subjectivity in evaluation, among others. Sometimes initial inspections find conditions that warrant repeat inspection and hence, repeated periodic visits to the bridge to check on the progression of initial deficiencies, such as cracks or corrosion. This process is time consuming and costly, especially where inspections must be carried out beneath the bridge deck, i.e., where special equipment with a boom would need to be obtained so as to gain visual access for inspection. Moreover, portions of the bridge superstructure are often located at heights that are difficult to reach and potentially hazardous for bridge inspectors to go. Furthermore, often hundreds or thousands of photographs and instrument readings must be scanned and analyzed by bridge inspectors for indications of potential problems that may warrant further analysis. Studies have identified these limitations and explored innovative and promising bridge inspection technologies to tackle these challenges. These emerging technologies include Non-Destructive or Non-contact methods such as ground penetrating radars, photogrammetry, laser scanning technology, infrared thermography, sensors, machine vision, and unmanned aerial vehicles (UAVs). Non-destructive testing (NDT) using Unmanned Aerial Vehicles (UAVs) have been gaining momentum for bridge monitoring in the recent years, particularly due to enhanced accessibility and cost efficiency, deterrence of traffic closure, and improved safety during inspection. They are often deployed in instances where the infrastructure has limited accessibility, characterized by their height and/or location. This study assess unmanned aerial vehicles (UAVs) that are best suited for inspecting damage in structures and pairing them with a damage detection technique that best suits structural health monitoring of a bridge structure. Upon selection of a UAV and detection method, the technique will be put to the test on bridge prototype in a laboratory. This technique will be compared to the various techniques in literature; for which, if successful, future work will include applying the technique to a real bridge structure.

This paper presents a fast and reliable model, for analysing the effect of localised damage on waveguides and obtaining all the range of dispersion curves for wave propagation. The model is based on modelling a finite section of the waveguide using the Finite Element Method (FEM) and applying the Floquet boundary conditions. These boundary conditions ensure the implantation of periodicity in the displacement field. Then, the eigenvalue problems are solved to obtain mode shapes and corresponding natural frequencies for different wavelengths. This method provides a physical based knowledge of how damages interact with wave modes. Although the eigenvectors obtained may be very localised, they will still result in some interaction between the damage and the propagating wave modes. Finally, the phase and group velocity curves have been studied for various composite laminates in pristine conditions as well as those with delamination. The effect of damage on the shape of individual wavefronts has also been studied with the help of slowness curves. The effect of damage on the shape of individual wavefronts has also been studied with the help of slowness curves.

Dynamic responses of steel bolts anchored in concrete subject to cyclic loads are complex: qualitative physical attributes include pinching, displacement ratcheting and force-intercept. In fact, these attributes vary with time, involving uncertainties that can lead to different failure patterns. This study explores higher-order elements (HOEs), an emerging theoretical macro-modeling framework originated from electrical engineering and transferred to engineering mechanics by using physical analogies. This study proposes a systematic procedure for a new model formulation: a model assembly that is made up of three distinct modeling elements, a higher-order spring, higher-order damper, and loading-rate independent damper to capture pinching, displacement ratcheting, and force intercepting, respectively. The two higher-order elements play significant roles to model responses of fatigue loading in two opposite directions each involving loading and unloading, therefore this study offers a case study to reveal a major practical application of HOE theory. To afford more physical insights, the higher-order spring is further formulated into a mem-spring, where new choices of state variables are employed. The proposed model formulation not only benefits both efficient simulation of time-varying dynamical systems and qualitative failure analysis, but also is a new utility of HOE theory. Two major datasets of anchoring blots are used to develop and validate the proposed modeling approach, while three minor datasets are used for a supplementary investigation. This study shows that, both qualitatively and quantitatively under ordered excitation, HOE can be employed with more modeling power and efficiency compared with existing models for progressive damage, and with the flexibility to interface with some existing models when needed.