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

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

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

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

Cable-Climbing Robots for Power Line Inspection

Power transmission line inspection is of utmost importance for power companies towards having sustainable electricity supply to vast number of customers in major industries as well as households in a city. Inspection provides valuable data from status of the line, thus helps line engineers to plan for necessary repair or replacement works before any major damages which may result in outage. Constant energy supply to the customers requires performing all the inspection tasks without de-energizing the line, so live line inspection methods are of the most interest to power companies. These companies perform patrol inspection mainly using helicopters equipped with infrared and corona cameras to detect observable physical damages as well as some internal deterioration to the line and line equipment. However, aerial inspection is costly and always there is a risk of contact with live lines and loss of life. Moreover, there are some critical specifications of the line such as internal corrosion of steel reinforced aluminium conductors that should be inspected precisely from close distances to the line that are not accessible by a mobile platform such as a helicopter or even an unmanned aerial vehicle (UAV). Hence, power companies have endeavored to make especial cable-climbing robots to accomplish inspection tasks from close distances to the hot line. Thanks to technological advances, utilizing robots as reliable substitutes for human beings in hazardous environments such as live lines has become possible. For many tasks requiring high precision over a long period of time, robots even do their job better than human operators. However, power companies have mainly focused on automating inspection tasks more willingly than making autonomous systems to perform repair works on the live line due to the fact that repair works are often complex to be accomplished by a robot

A simple approach to real-time fault detection and diagnosis in base-isolated structures

In recent years, base-isolation has become an increasingly applied structural design technique in highly seismic areas. The state-of-the-art practice is to use active or passive magneto-rheological (MR) dampers to limit the base displacement. The crucial effect of likely faults in the base-isolation system on the top superstructure requires that the resulting nonlinear hysteretic system to be monitored in real-time for possible changes in the two most important structural parameters: stiffness and damping. This paper develops a simple fault detection and diagnosis technique based on comparing the internal dynamics of the base-isolation system with those of a healthy baseline model to detect faults. Three different cases of stiffness, damping, and combined stiffness and damping faults are studied, in silico, on a realistic base-isolated structure subjected to the Loma Prieta earthquake with a passive MR damper. The simulation results show that the proposed fault detection and diagnosis algorithm is well capable of detecting the existence, determining the type, and quantifying the severity of faults in the system in real-time as the faults occur

Health Hazards Associated with Consumption of Roof-Collected Rainwater in Urban Areas in Emergency Situations

The greater Wellington region, New Zealand, is highly vulnerable to large earthquakes because it is cut by active faults. Bulk water supply pipelines cross the Wellington Fault at several different locations, and there is considerable concern about severe disruption of the provision of reticulated water supplies to households and businesses in the aftermath of a large earthquake. A number of policy initiatives have been launched encouraging householders to install rainwater tanks to increase post-disaster resilience. However, little attention has been paid to potential health hazards associated with consumption of these supplies. To assess health hazards for householders in emergency situations, six 200-litre emergency water tanks were installed at properties across the Wellington region, with five tanks being allowed to fill with roof-collected rainwater and one tank being filled with municipal tapwater as a control. Such tanks are predominantly set aside for water storage and, once filled, feature limited drawdown and recharge. Sampling from these tanks was carried out fortnightly for one year, and samples were analysed for E. coli, pH, conductivity, a range of major and trace elements, and organic compounds, enabling an assessment of the evolution of water chemistry in water storage tanks over time. Key findings were that the overall rate of E. coli detections in the rain-fed tanks was 17.7%, which is low in relation to other studies. We propose that low incidences of may be due to biocidal effects of high zinc concentrations in tanks, originating from unpainted galvanised steel roof cladding. Lead concentrations were high compared to other studies, with 69% of rain-fed tank samples exceeding the World Health Organisation’s health-based guideline of 0.01 mg/L. Further work is required to determine risks of short-term consumption of this water in emergency situations