Cost Effective Computer Vision Based Structural Health Monitoring using Adaptive LMS Filters

Structural health monitoring (SHM) algorithms based on Adaptive Least Mean Squares (LMS) filtering theory can directly identify time-varying changes in structural stiffness in real time in a computationally efficient fashion. However, the best metrics of seismic structural damage are related to permanent and plastic deformations. The recent work done by the authors uses LMS-based SHM methods with a baseline non-linear Bouc-Wen structural model to directly identify changes in stiffness (modelling or construction error), as well as plastic or permanent deflections, in real-time. The algorithm validated, in silico, on a non-linear sheartype concrete structure using noise-free simulation-derived structural responses. In this paper, efficiency of the proposed SHM algorithm in identifying stiffness changes and plastic/permanent deflections under different ground motions is assessed using a suite of 20 different ground acceleration records. The results show that even with a fixed filter tuning parameters, the proposed LMS SHM algorithm identifies stiffness changes to within 10% of true value in 2.0 seconds. Permanent deflection is identified to within 14% of the actual as-modelled value using noisefree simulation-derived structural responses. Accuracy of the proposed SHM algorithm mainly relies on providing high-speed structural responses. However, due to a variety of practical constraints, direct high frequency measurement of displacement and velocity is not typically possible. This study explores the idea that emerging high speed line scan cameras can offer a robust and high speed displacement measure required for the modified LMS-based SHM algorithm proposed for non-linear yielding structures undergoing seismic excitation, and can be used for more precise estimation of the velocity using measured acceleration and displacement data. The displacement measurement method is tested to capture displacements of a computer-controlled cart under 20 different displacement records. The method is capable of capturing displacements of the cart with less than 2.2% error

Real-time Structural Health Monitoring of Nonlinear Hysteretic Structures

The great social and economic impact of earthquakes has made necessary the development of novel structural health monitoring (SHM) solutions for increasing the level of structural safety and assessment. SHM is the process of comparing the current state of a structure’s condition relative to a healthy baseline state to detect the existence, location, and degree of likely damage during or after a damaging input, such as an earthquake. Many SHM algorithms have been proposed in the literature. However, a large majority of these algorithms cannot be implemented in real time. Therefore, their results would not be available during or immediately after a major event for urgent post-event response and decision making. Further, these off-line techniques are not capable of providing the input information required for structural control systems for damage mitigation. The small number of real-time SHM (RT-SHM) methods proposed in the past, resolve these issues. However, these approaches have significant computational complexity and typically do not manage nonlinear cases directly associated with relevant damage metrics. Finally, many available SHM methods require full structural response measurement, including velocities and displacements, which are typically difficult to measure. All these issues make implementation of many existing SHM algorithms very difficult if not impossible. This thesis proposes simpler, more suitable algorithms utilising a nonlinear Bouc-Wen hysteretic baseline model for RT-SHM of a large class of nonlinear hysteretic structures. The RT-SHM algorithms are devised so that they can accommodate different levels of the availability of design data or measured structural responses, and therefore, are applicable to both existing and new structures. The second focus of the thesis is on developing a high-speed, high-resolution, seismic structural displacement measurement sensor to enable these methods and many other SHM approaches by using line-scan cameras as a low-cost and powerful means of measuring structural displacements at high sampling rates and high resolution. Overall, the results presented are thus significant steps towards developing smart, damage-free structures and providing more reliable information for post-event decision making

Structural Health Monitoring using Adaptive LMS Filters

A structure's level of damage is determined using a real time that comes with significant computational cost and non-linear model-based method utilizing a Bouc-Wen hysteretic complexity. Moreover, like other linear approaches they are model. It employs adaptive least mean squares (LMS) filtering not applicable to the typical non-linearities found in seismic theory in real time to identify changes in stiffness due to modeling error damage, as well as permanent displacements, which are structural responses. critical to determining ongoing safety and use. The structural In contrast, direct identification of changes in stiffness health monitoring (SHM) method is validated on a 4-story shear and/or permanent deflection would offer the post-earthquake structure model undergoing seismic excitation with 10% uniform outputs desired by engineers. The goal is to obtain these noise added. The method identifies stiffness changes within 0.5- stiffness changes in real time in a computationally efficient and 1.0% inside 0.2-1.0 seconds at different sampling frequencies. robust fashion. Model-based methods combined with modern Permanent deflections are identified to within 10% of the true value in 1.0 second, converging further over the remainder of the filterang theory offer that opportunity. record

Structural Health Monitoring using Adaptive LMS Filters

A structure’s level of damage is determined using a non-linear model-based method utilising a Bouc-Wen hysteretic model. It employs adaptive Least Mean Squares (LMS) filtering theory in real time to identify changes in stiffness due to modelling error damage, as well as plastic and permanent displacements, which are critical to determining ongoing safety and use. The Structural Health Monitoring (SHM) method is validated on a four-story shear structure model undergoing seismic excitation. For the simulated structure, the algorithm identifies stiffness changes to within 10% of the true value in 0.20 s, and permanent deflection is identified to within 5% of the actual as-modelled value using noise-free simulation-derived structural responses

LMS-based approach to structural health monitoring of nonlinear hysteretic structures

July 2011Structural health monitoring (SHM) algorithms based on adaptive Least Mean Squares (LMS) filtering theory can directly identify time-varying changes in structural stiffness in realtime in a computationally efficient fashion. However, better metrics of seismic structural damage and future utility after an event are related to permanent and total plastic deformations. This paper presents a modified LMS-based SHM method and a novel two-step structural identification technique using a baseline nonlinear Bouc-Wen structural model to directly identify changes in stiffness due to damage, as well as plastic or permanent deflections. The algorithm is designed to be computationally efficient; therefore it can work in real-time. An in silico single-degree-of-freedom (SDOF) nonlinear shear-type structure is used to prove the concept. The efficiency of the proposed SHM algorithm in identifying stiffness changes and plastic/permanent deflections is assessed under different ground motions using a suite of 20 different ground acceleration records. The results show that in a realistic scenario with fixed filter tuning parameters, the proposed LMS-based SHM algorithm identifies stiffness changes to within 10% of true values within 2.0 seconds. Permanent deflection is identified to within 14% of the actual as-modelled value using noise-free simulation-derived structural responses. This latter value provides important post-event information on the future serviceability, safety, and repair cost

A Novel Wall Climbing Robot Based on Bernoulli Effect

It is a challenge for mobile robots to climb a vertical wall primarily due to requirements for reliable locomotion, high manoeuvrability, and robust and efficient attachment and detachment. Such robots have immense potential to automate tasks which are currently accomplished manually, offering an extra degree of human safety in a cost effective manner. In contrast to vacuum suction, magnetic adhesion, and dry techniques used existing wall climbing robots, Canterbury’s research effort focuses on a novel approach which achieves attachment and detachment based on Bernoulli Effect. The adhesion force is achieved on a variety of surfaces, independent on the material of the wall and surface conditions. Such ubiquitous mobility with a force / weight ratio as high as 5 is nearly impossible to be achieved by other adhesion methods

Permanent Deflection Identification of Non-linear Structures Undergoing Seismic Excitation Using Adaptive LMS Filters

Structural Health Monitoring (SHM) algorithms based on Adaptive Least Mean Square (LMS) filtering theory can directly identify time-varying changes in structural stiffness in real time, are robust to noise, and computationally efficient. Common modal or wavelet methods are less robust to noise and small levels of damage. However, the best metrics of seismic structural damage are related to permanent and plastic deformations, which no reported methods identify. This research uses LMS-based SHM methods with a baseline non-linear Bouc-Wen structural model to directly identify permanent deflection and changes in stiffness (modelling or construction error), in realtime. The algorithm is validated, in silico, on an equivalent single degree of freedom of a non-linear 5-storey shear-type concrete structure using MATLAB®. The Cape Mendocino ground motion is scaled to a level that causes permanent deflection to show the algorithm’s capability. For the simulated structure, the algorithm identifies stiffness changes to within 10% of true value in 2.0 seconds, and permanent deflection is identified to within 0.5% of the actual as-modelled value