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CVEN9840-Lecture1-2020copy.pdf
Structural Health Monitoring
(CVEN9840)
Lecture 1
Dr Mehri Makki Alamdari
School of Civil and Environmental Engineering
Teaching Team and Contacts
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Dr Mehrisadat Makki Alamdari, Lecturer Centre for Infrastructure Engineering and Safety (CIES), UNSW Course Instructor and Course Coordinator
B.Sc., M.Sc., Aerospace Engineering M.Sc., Mechanical Engineering Ph.D., Civil Engineering
Email: [email protected]
Lectures (+ Workshops): Every Friday 12:00-16:00 (Weeks: 1-5,7-10) Offering Period 18/09/2020 - 20/11/2020
Consultation Details: Individual consultation time: Monday, 15:00 17:00 It will be online in MS Teams. You should arrange an appointment in advance.
Class Structure: I will be running entire lecture and workshop. There is no distinct separation between the lecture and workshop. After each hour of class, there will be a 10-minute break.
Workload
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4 hours of class per week Lecture and workshop
+ 4 hours of private study
A minimum of 1 hour of private study for each hour delivered in the class is expected (this will vary from week to week, in some
cases the private study/preparation will be more intensive)
= Minimum of 8 hours per week
Assessment
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1. Assignment (Homework) – 50%
Assignment 1 (10%) Week Assignment 2 (10%) Week Assignment 3 (15%) Week Assignment 4 (15%) Week
2. Final Exam: 50%
Note: A mark of at least 40% in the final examination is required before the assignments mark is included in the final mark.
Beware! An assignment that includes plagiarised material will receive a 0% Fail, and students who plagiarise may fail the course. Students who plagiarise a e a iab e di ci i a ac i , i c di g e c i f e e . P agia i i he e f a he e ideas a if he e e . Whe i i ece a de i ab e e he e e a e ia h d ade a e ac edge whose words or ideas they are and where you found them (giving the complete reference details, including page number(s)). The Learning Centre provides further information on what constitutes Plagiarism at: https://student.unsw.edu.au/plagiarism
A penalty of 10% will apply for each day of late submission.
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Assessment
Subject Outline CVEN9840
Major Topics:
Structural Health Monitoring (SHM): Motivation and Background
Measurement and Sensing
Structural Dynamics
System Identification and Modal Analysis
Vibration-Based SHM and Damage Identification
Signal Processing and Feature Extraction
Statistical Pattern Recognition and Classification Methods
Non-Destructive Testing and Evaluation (NDT/E)
Real World Case Studies
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Subject Outline CVEN9840
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Learning Outcomes
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Structural Health Monitoring: A Machine Learning Perspective By Charles R Farrar, Keith Worden
Written by global leaders and pioneers in the field, this book is a must-have read for researchers andpracticing engineers working in SHM.
Structural Health Monitoring: A Machine Learning Perspectiveis the first comprehensive book on the general problem of structural health monitoring.
The authors, renowned experts in the field, consider structural health monitoring in a new manner by casting the problem in the context of a machine learning/statistical pattern recognition paradigm, first explaining the paradigm in general terms then explaining the process in detail with further insight provided via numerical and experimental studies of laboratory test specimens andin-situstructures. This paradigm provides a comprehensive framework for developing SHM solutions.
The book is suitable for upper-level undergraduates, postgraduate students and practicing engineers.
Recommended Textbook
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Structural Health Monitoring of Large Civil Engineering Structures By Hua-Peng Chen
A critical review of key developments and latest advances in Structural Health Monitoring technologies applied to civil engineering structures, covering all aspects required for practical application.
Presents state-of-the-art SHM technologies allowing asset managers to evaluate structural performance and make rational decisions
Covers all aspects required for the practical application of SHM Includes case studies that show how the techniques can be applied
in practice
Structural Health Monitoring of Large Civil Engineering Structures is an ideal book for practicing civil engineers, academics and postgraduate students studying civil and structural engineering.
Recommended Textbook
Install MATLAB!
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MATLAB® is the high-level language and interactive environment used by millions of engineers and scientists worldwide. It lets you explore and visualize ideas and collaborate across disciplines
NOTE: We will be using MATLAB in this subject quite a lot, and in order to complete your assignments and final exam you should be confident using MATLAB.
Instruction for installing MATLAB on your own computer:
Create a Mathwork account. You will need to use your student UNSW email address for this. (https://au.mathworks.com/) Visit the UNSW MATLAB Portal, then select Sig i ge a ed under the Get MATLAB and Simulink section. Log into your MathWorks account that is associated to your UNSW license. Click the download button for the current release. Choose a supported platform and download the installer. Run the installer. In the installer, select Log in with a MathWorks Account and follow the online instructions. When prompted to do so, select the Academic Total Headcount license labelled Individual. Select the products you want to download and install. After downloading and installing your products, keep the Activate MATLAB checkbox selected and click Next. Follow the prompts to activate MATLAB.
https://www.myit.unsw.edu.au/software-students
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MATLAB Training
MATLAB Online Training Access 100+ hours on online interactive training courses that cover MATLAB and numerous applied mathematics & statistics. These courses are included in our UNSW site wide license and available for all students to access.
https://matlabacademy.mathworks.com/
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Structural Health Monitoring (SHM)
Background and Motivation
Infrastructures
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Modern societies are heavily dependent upon structural and mechanical systems:
Aircraft Bridges Power generation systems Rotating machinery Offshore oil platforms Buildings Defense systems
Because these systems cannot be economically replaced, techniques for damage detection are being developed and implemented so that these systems can continue to be safely used if or when their operation is extended beyond the design basis service life.
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Ageing Most of the infrastructures especially bridges, are either reaching their design lives or have passed their service period.
Harsher Environmental Loading Civil structures become increasingly vulnerable to natural and man-made hazards, partly due to the global climatic change leading to stronger hurricanes and faster material deterioration.
Infrastructures
72% of bridges in Australia were constructed before 1976!
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More Frequent and Larger Operational Loading
Infrastructures
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Many innovative large civil structures have been built or are under construction throughout the world. These systems in terms of design often incorporate novel materials whose long-term degradation processes are not well understood.
Tall Buildings
Taipei 101, Taipei, 509 ICC, Hong Kong, 484m. Burj Khalifa, Dubai , 828ms
Long-span Bridges
Large Spatial Structures
Infrastructures
Bridge Collapses
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Catastrophic failure during the evening rush hour on August 1, 2007, killing 13 people and injuring 145.
I-35W Mississippi River bridge, USA, 2007
Additional weight on the bridge at the time and fracture in a gusset plate contributed to the catastrophic failure.
Bridge Collapses
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Morandi bridge, Italy, 2018 Catastrophic collapse during a rainstorm on August 14, 2018, killing 43 people and left 600 homeless.
The collapsed part of the bridge is shown in red
Corrosion Land sliding
Bridge Collapses
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Florida International University Bridge, USA, 2018.
A catastrophic collapse resulting in six deaths, eight injuries, and eight vehicles being crushed underneath.
Load and capacity calculation errors!
Partially constructed concrete truss bridge with faux cable ties. One of two spans erected without faux cable ties or support tower. Faulty design, failure to follow proper procedures, and lack of redundancy all contributed to failure.
Bridge Collapses
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Bridge designed for max. 46 tonne vehicles, Truck overloaded with 160 tons of sand caused it to collapse.
Baihe Bridge in Huairou, China, 2011
Bridge Collapses
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Kutai Kartanegara Bridge, Indonesia, 2011
A catastrophic failure which caused 20 killed, 40 injured.
Human error. Bridge collapsed while workers repaired a cable.
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Tacoma Narrow Bridge, USA, 1940 (Wind load)
Bridge Collapses
Ref: https://www.youtube.com/watch?v=XggxeuFDaDU
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Definition of Damage
Damage can be defined as changes introduced into a system that adversely affects its current or future performance; this includes changes to the material and/or geometric properties of these systems, such as changes to the boundary conditions.
Crack damage Change the stiffness characteristics. No influence on the material characteristics
Scour of bridge pier is the process whereby increased flow rates around a pier erode the surrounding soil Boundary condition change
Loosening of bolted joints Connectivity and dissipation change
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Definition of Damage
1. Damage is a comparison between two different states of the system. 2. Damage identification is a problem in statistical pattern recognition.
The concept of damage is not meaningful without a comparison between two different states of the system, one of which is assumed to represent the initial, and often undamaged, state.
Example: An apparently damaged highway bridge even though a close examination shows that pedestrians are still using this bridge to cross the river.
Your conclusion that this bridge is ‘damaged is based on a mental comparison with the hundreds or thousands of examples of undamaged bridges that you have observed in your daily lives!!!
Damage identification is a process of pattern recognition Pattern recognition provides a fundamental framework for carrying out SHM
Distinguishing between the states ‘healthy and ‘damaged for a structure
This database have been accumulated, trained or learned, over an earlier period of time.
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Damage: Length Scales
All damage begins at the material level and then under operational and environmental loading scenarios progresses to the component and then system level damage.
Damage in micro-structure level
All materials used in engineering systems have some inherent initial imperfections at the grain boundary scale, i.e. interfaces between crystals of the material the grain boundaries influence how materials will fail.
Damage in component level
Under environmental and operational loading material flaws will grow and produce component level damage.
Damage in system level
Further loading causes system- level failure failure is defined in terms of exceeding some strength, stability or deformation-related performance criterion.
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Damage Detection: Length Scales
Material scientists Based on three-dimensional images of the microstructure Defects: voids, inclusions and dislocations.
Damage detection in microscopic level
Local Non-Destructive Testing (NDT) methods They are applied to assess incipient macroscopic damage at the material and component level
Damage detection in component level
Global vibration-based methods They are used to assess damage from the component to full system scale
Damage detection in system level
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Damage can accumulate incrementally over long periods of time such as that associated with fatigue (ageing) or corrosion damage accumulation. Damage can also progress very quickly, as in the case of critical fracture. Damage can also result from discrete extreme events such as flood or earthquake.
Damage: Time Scales
Damage due to extreme event Damage due to cyclic loading
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How Engineers and Scientists Study Damage
What causes damage? Material deterioration (material aging and degradation processes) Engineering analyses (exceeding allowable strength, deformation or stability criteria)
What can be done to prevent damage? Material science (new materials) Engineering design strategies Define operational and environmental limitations
Is damage present? Where is the location? How big is it? How fast damage grow and reach to a critical level? NDT, Structural Health Monitoring
How do we mitigate the effects of damage? Change operational parameters (e.g. speed of operation) Maintenance and repair Self-hea i g c e ( a a e ia )
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Maintenance and Repair
Maintenance
Cyclic activity which is repeated over the life of the structure so that it
continues to perform its function.
It involves the early repair of small, less serious defects which prevents long-term deterioration that would otherwise be more costly to repair.
Inadequate maintenance can result in more frequent and costly repairs.
Repair
Non-cyclic and infrequent activity.
More complex and costly operation than maintenance
Maintenance and repair are complementary operations and are both essential components of infrastructure management.
Maintenance philosophies have evolved to minimise the potential negative life- safety and economic impacts of unforeseen system failures.
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Maintenance Strategies: Run-to-Break
This is the traditional method where
systems/structures operate until major problems happen.
When a major problem occurs, the consequence
can be catastrophic.
Severe damage may occur: time to repair can
be greatly increased.
The structure/system may be no longer operational: closure of bridge, closure
of production line.
Major economic loss: it can be much greater
than the cost of individual machine.
When failure is unlikely to be catastrophic, this
approach may be adopted.
Initially, run-to-failure approaches to engineering system maintenance were used. With this approach the system is operated until some critical component fails and then that component is replaced. This procedure requires no investment in monitoring systems, but it can be extremely costly as failure can occur without warning.
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Maintenance Strategies: Time-Based
Maintenance is done at regular intervals.
Maintenance can be planned well in
advance.
Catastrophic failure is greatly reduced, but unforeseen failures
can still occur.
Maintenance may be carried out too early
or too late.
A more sophisticated maintenance approach is time-based maintenance. This maintenance approach requires that critical components are serviced or replaced at predefined times regardless of the condition of the component.
A typical example is the recommendation that one changes the oil in their car after it has been driven a certain distance or at some prescribed time interval. This maintenance is done regardless of the condition of the oil.
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Example: Bridge Maintenance Strategies Time-Based
It occurs every six month to one-year cycle depending on a risk assessment. It checks for visible defects which might affect structural safety. It identifies maintenance items that require immediate action, repairs that address a structural issue and/or prevent further deterioration.
Level 1 Inspection
It occurs every 2-year intervals. It assesses the condition of structural components with more detail. It calculates the bridge condition rating. It identifies maintenance items that require immediate action, repairs that address a structural issue and/or prevent further deterioration.
Level 2 Inspection
It is an engineering investigation, conducted when required to assess structural capacity in preparation for possible strengthening.
Level 3 Inspection
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Maintenance Strategies: Time-Based
Rope Access InspectionUnder Bridge Inspection Vehicle
Access and safety issues …
o Time-based maintenance is a more proactive approach than run-to-failure and it has made complex engineering systems such as commercial aircraft extremely safe.
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Maintenance Strategies: Predictive (Condition-Based)
Maintenance is carried out at optimum time.
It requires having access to reliable
condition monitoring techniques.
It should be able to determine the
current condition of the
structure/system.
It should be able to give reasonable predictions of
remaining useful life.
Maintenance costs can be significantly
reduced.
It is the best maintenance strategy in
most cases as it minimises the
frequency and cost of repairs.
SHM is the technology that allows the current time-based maintenance approaches to evolve into condition-based maintenance philosophies.
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Structural Health Monitoring (SHM)
Structural Health Monitoring is the process of implementing a condition-based damage detection strategy for aerospace, civil and mechanical engineering infrastructure.
It enables maintenance to be scheduled based on actual asset condition.
Move from time-based maintenance to condition-based maintenance.
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The concept of condition-based maintenance is that a sensing system on the structure will monitor the system response and notify the operator that damage or degradation has been detected.
Life-safety and economic benefits associated with such a philosophy will only be realised if the monitoring system provides sufficient warning such that corrective action can be taken before the damage or degradation evolves to some critical level.
The trade-off associated with implementing such a philosophy is that it potentially requires more sophisticated monitoring hardware to be deployed on the system and more sophisticated data analysis procedures to interrogate the measured data.
Structural Health Monitoring (SHM)
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SHM is based on a comprehensive sensory system and a sophisticated data processing implemented with advanced information technology and structural analysis algorithms, to provide a continuous (real-time) and automated surveillance on structural health condition.
SHM process involves the observation of a structure or mechanical system over time using periodically spaced dynamic response measurements, the extraction of damage-sensitive features from these measurements and the statistical analysis of these features to determine the current state of system health.
Structural Health Monitoring (SHM)
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Structural Health Monitoring
Depending on time scale on which damage evolves:
Long Term SHM For long term SHM, the output of this process is periodically updated information regarding the ability of the structure to perform its intended function in light of the inevitable aging and degradation resulting from operational environments.
Short Term SHM After extreme events, such as earthquakes or blast loading, SHM is used for rapid condition screening and aims to provide, in near real time, reliable information regarding the performance of the system and the subsequent integrity of the structure.
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Local versus Global Damage Detection D
am ag
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en tif
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io n
Local NDE Based
- Based on localised experimental methods such as acoustic or ultrasonic methods, magnetic field methods, radiography, eddy-current methods and thermal field methods.
- These techniques require that the vicinity of the damage is known a priori and that the portion of the structure being inspected is readily accessible.
- Subject to these limitations, such experimental methods can detect damage on or near the surface of the structure.
Global Vibration Based
- Global damage identification based on changes in the vibration characteristics of the structure.
-The basic premise of vibration-based damage detection is that damage will alter the stiffness, mass or energy dissipation properties of a system, which, in turn, alter the measured global dynamic response properties of the system.
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Fundamentally, there will always be a trade-off between the cost associated with deploying a local sensing system over a large area of the structure and the lack of fidelity associated with more global sensing systems.
The most fundamental challenge is the fact that damage is typically a local phenomenon and may not significantly influence the lower- frequency global response of a structure that is normally measured during vibration tests, particularly those where the response to ambient excitation is measured.
Local versus Global Damage Detection
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Non-Destructive Evaluation Local Monitoring
Example:
Micro-cracks were found in numerous welded connections of steel moment-resisting frame structures after the 1994 Northridge earthquake.
These connections are typically covered by nonstructural architectural material.
Costs associated with inspecting a single joint and then reinstalling the architectural cladding can be on the order of thousands of dollars per joint.
A typical twenty-storey building may have hundreds of such joints.
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Structural Health Monitoring Motivation
Motivation: Economic and life-safety advantage
Aerospace companies, along with government agencies are investigating SHM technology for detection of damage in aerospace structures. Clearly, such damage detection has significant life-safety implications.
A semiconductor manufacturing industry is adopting SHM technology to prevent accidental downtime in the fabrication plants. Such downtime can cost these companies on the order of millions of dollars per hour.
SHM technology may provide a means on whether buildings are safe for reoccupation after a significant earthquake. They can minimize uncertainty associated with current visual post-earthquake damage assessments.
Economic Impact Life- Safety and Economic Impact Life- Safety and
Economic Impact
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Structural Health Monitoring Motivation
Economic Benefits:
Reduced maintenance cycles Reduced warranty obligations Increased manufacturing capacity Increased system availability
Life-Safety Benefits:
Legal actions associated with injuries or fatalities resulting from damage to a structure add a significant economic impact to the human tragedy
Considerable expense associated with the accident investigation, for instance for the space shuttle accidents and most accidents associated with large commercial aircrafts
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Proactively monitor structural performance under operational and environmental variations
Extend the remaining life by reducing failures due to early detection
Optimize inspection budgets with real-time condition data
Reduce unnecessary maintenance and life-cycle costs
Increase confidence in structural integrity and public safety
Avoid closures and downtime for routine inspection
Design verification
On-line load monitoring
Structural Health Monitoring Motivation
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SHM Attempts to Answer the Following Questions D
am ag
e D
ia gn
os is
Level 1: Damage Detection Is damage present in the structure?
Level 2: Damage Localisation What is the geometric location of the damage?
Level 3: Damage assessment What is the type/shape of damage?
Level 4: Damage quantification What is the severity of the damage? D
am ag
e Pr
og no
si s
What is the prediction of the remaining service life/ structural capacity of the structure?
Offline SHM Damage identification method is applied to a large pool of already collected data Online SHM Damage identification method is applied to a real-time stream of live data.
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Structural Health Monitoring Process
Operational evaluation Defines the damage to be
detected and implementation issues.
Measurement and data acquisition
Defines the sensing hardware and the data to be used.
Feature selection & extraction
The process of identifying damage-related information
from measured data.
Probabilistic decision making
Using statistical models to transform features into actual performance-level decisions.
Data Cleansing Data Normalization Data Fusion Information Condensation
It answers questions regarding implementation issues for a structural health monitoring system.
Provide economic and/or life-safety justifications for performing the monitoring. Define system-specific damage including types of damage and expected locations. Define the operational and environmental conditions under which the system functions. Define regulatory and business constraints. Define the limitations on data acquisition in the operational environment.
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Operational evaluation will require input from many different sources (designers, operators, maintenance people, financial analysts, regulatory officials)
Operational Evaluation
1
What is the life-safety and/or economic justification for performing the structural health monitoring? How much will it cost to develop the system? How much will it cost to maintain the system? What are the costs associated with the decisions based on the SHM system performance?
2 How is damage defined for the system being investigated and, for multiple damage possibilities, which cases are of the most concern?
3 What are the conditions, both operational and environmental, under which the system to be monitored?
4 What are the limitations on acquiring data in the operational environment?
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Operational Evaluation Economic and/or life-safety justifications
Commercial airframe and jet engine manufactures will soon lease their products and assume maintenance responsibilities. Reducing maintenance cost increases profits!
Oil companies invest over a billion dollars for deep water offshore platforms, cost of down time is exorbitant for high capital expenditure manufacturing.
Loss of transportation infrastructure has significant impact on entire economy. Life safety is also an issue for most of these examples
Example: Typically, for most high-expenditure aerospace, civil and mechanical systems, the design and construction budget is much larger than the annual maintenance budget. Therefore, the capital expenditures associated with the hardware requirements of the SHM system will not seem as extreme if they are included in the design and construction portion of the budget.
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Operational Evaluation Defining System-Specific Damage
Type of damage to be detected (e.g. crack, excessive deformation, corrosion) Anticipated damage locations Critical level of damage that must be detected Time scale for damage evolution
Example: Type of damage to be detected Fatigue crack Threshold level of damage that must be detected
A 2-mm-long, through-thickness crack The critical level of damage that produces failure or that will no longer allow for a planned safe shut down of the system A 5-cm-long crack Locations where the particular type of damage accumulates in the structure Welded beam- to-column connections
This particular type of damage may manifest itself in terms of changes to the vibration signature caused by a fatigue crack opening and closing under the normal traffic loading. Generation of resonance frequency harmonics is a common feature of a vibration signature associated with cracks opening and closing.
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Operational Evaluation
Operational Constraints
Operational conditions will influence loading that produces the monitored dynamic responses.
Traffic loading on bridges Machinery and fluid storage on offshore platforms Speed of rotating machinery Flight manoeuvres (altitude, speed) and fuel level for aircraft
Environmental Conditions (temperature and humidity)
Environmental conditions can produce changes in dynamic response that must be distinguished from changes cause by damage.
Temperature changes on bridges Sea states for offshore platforms Air turbulence for aerospace structures
Changing operational and environmental conditions will produce changes in the measured system response and it is vital that these changes are not interpreted as indications of damage.
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Operational Evaluation Data Acquisition Constraints
Cost
Accessibility Access to the foundation of an offshore oil platform is extremely difficult once the structure is put in place.
For aerospace structures weight restrictions pose significant limitations.
RF interference poses challenges for wireless telemetry.
Many portions of a structure will not be easily accessible for instrumentation (bridge deck, below-water-line portions of oil platforms).
Hostile Environnements (e.g. radiation, temperature, moisture) Certain types of sensors will not perform well in extreme thermal or radiation environments.
Adverse effects of the sensors on the system operation (spark due to a sensor being very close to fuel tank).
Non-technical issues Owner does not want the public to see the sensing hardware for fear they will lose confidence in the safety of the system being monitored.
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Operational Evaluation Wind Turbine Example
Motivation: purely economic.
An initial investment is about $1 -1.5 million/megawatt. Overhaul might cost 15-20% of the initial investment O&M costs are $100-150K/year. What is the cost of deploying a SHM system?
Damage to be detected: Delamination of composite turbine blades
Need to define minimum area of delamination that must be detected Expected delamination growth rates Critical delamination areas
Damage to gear box Turns at 1000 rpm
Environmental and operation constraints on the SHM System:
rotating device, wind, rain, lightning, temperature electromagnetic fields, offshore
There is no widely accepted procedure to demonstrate rate of return on investment in an SHM system
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Operational Evaluation off-Shore Platform Example
Motivation: Life-safety and economic As an example, deep water platforms can represent over a billion US dollar capital investment before any revenues are generated from the platform. In addition, these structures can have many people working and living on the platform at any time.
Damage to be detected: - Ship impact on the structural elements - Corrosion damage - Fatigue damage accumulation
Environmental and operation constraints on the SHM System:
Corrosive saltwater environment Instruments must withstand environmental challenges Numerous sources of variability
Varying mass marine growth, fluid storage and equipment The inputs used are often nonstationary There are usually multiple input sources occurring simultaneously (e.g. wave input simultaneously occurring with drilling machinery in operation).
The SHM system must offer financial advantages over the use of divers.
Ambient (sea and wind) excitation must be used to extract the resonance frequencies.
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Operational Evaluation
Summary
Need to define the justification, goals for, and the limitations of the SHM system as quantifiable manner as possible.
Operational evaluation should integrate as much a priori information as possible to inform the SHM system design process.
Such information can come from a wide variety of sources.
Quantified operational evaluation will impact the development and of all other portions of the SHM process and, in turn, the final system performance.
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Ob ai i g acc a e ea e f he e e e i e e ia c a hea h monitoring. It involves selecting the excitation methods, the sensor types, number and locations, and the data acquisition/storage/transmittal hardware. This process is application-specific.
Economic considerations will play a major role in making decisions regarding the data acquisition hardware to be used for the SHM system.
The interval at which data should be collected is another consideration that must be addressed. For instance, if fatigue crack growth is the failure mode of concern, it may be necessary to collect data almost continuously once some critical crack has been identified.
Data Acquisition
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Excitation Excitation is the process of applying a time-varying input to the system in order to generate a response.
Selection of the proper excitation methods will be influenced by many factors including: The size of the structure. Required frequency range and amplitude of inputs. Operational constraints associated with the system. Power availability. Cost.
Data Acquisition
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Measured/controllable: usually imposed deterministic waveforms
Excitation
Modal hammer used to deliver an impact force to the bridge deck
Data Acquisition
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Measured/Controllable Excitation Examples:
Data Acquisition
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Unmeasured/uncontrollable: usually stochastic ambient input sources (Ambient excitation wind, waves, traffic, etc.)
Excitation
Often the only method that can be used for large structures, or if the structure is not to be taken out of service, or if regulations prohibit introducing energy into the structure. Parameter identification is more challenging without a measured input.
Data Acquisition
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Unmeasured/Uncontrollable Excitation Examples:
Data Acquisition
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THERE IS NO SENSOR THAT MEASURES DAMAGE!
(and there never will be!!)
However, we can t do SHM without sensing
Data Acquisition
Sensing and Measurement
Structural health monitoring is a detection and tracking problem.
Detection Sensor reading should be directly correlated, and as sensitive to, damage as possible.
Tracking Sensor readings and associated damage-sensitive features extracted from these data should change in a monotonic fashion with increasing damage levels.
Insensitive to other variability except damage Sensors should be as independent as possible from all possible sources of environmental and operational variability.
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Data Acquisition
Sensing systems for SHM consist of some or all of the following components:
1. Transducers that convert changes in the field variable of interest (e.g. acceleration, strain, temperature) to changes in an electrical signal (e.g. voltage, impedance, resistance). 2. Actuators that can be used to apply a prescribed input to the system (e.g. a piezoelectric transducer bonded to the surface of a structure). 3. A/D converters that transform the analogue electrical signal into a digital signal that can subsequently be processed on digital hardware. 4. Signal conditioning. 5. Power. 6. Telemetry. 7. Processing. 8. Memory for data storage.
Sensing and Measurement
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What features will be extracted from data for SHM assessment? May determine the type(s) of primary data to be acquired May determine the periodicity of primary data collection
The data types fall into four general categories of: 1. Dynamic input and response quantities (e.g. input force, strain or acceleration,
displacement, tilt) Sensors Strain gauges, displacement transducers such as linear variable
differential transducers (LVDTs) and piezoelectric accelerometers
2. Other damage-sensitive physical quantities (e.g. electromagnetic fields)
Sensors Acoustic emission associated with damage initiation and progression, Measurement of changes in electric impedance across a piezoelectric sensor/actuator
3. Environmental quantities (e.g. temperature, humidity, or wind speed) Sensors Temperature, pressure, rain, wind and moisture sensors
4. Operational quantities (e.g. traffic volume or vehicle speed)
Sensors Weigh-in-motion sensors
Sensing and Measurement – System Level Considerations
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Sensing and Measurement Active or passive sensing?
Determines need for active actuation (i.e., not ambient) Strongly influences power, networking, and bandwidth demands
What other factors may need to be considered for accurate assessment?
Operational (e.g., loading conditions during primary data collection) Environmental (e.g., ambient conditions during primary data collection) Identification of sources of error
Active sensing Actuators are incorporated with the sensing system to provide a known input to the structure that is designed to enhance the damage detection process. Passive sensing No actuator is involved.
Overall imposed limitations/constraints? Economic (e.g., fixed budgets) P ca (g c a f e ce ) Environmental
Sensing and Measurement – System Level Considerations
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Sensitivity and bandwidth What is the response of the sensor to inputs?
Sampling frequency What is the frequency response range of interest?
Resolution What is the minimum detectable value of the
intended input or the minimum achievable output?
Cross-axis sensitivity How much does the sensor respond to inputs
not aligned with the primary sensing direction?
Multiple resonances Does the sensor have multiple nonlinear
(resonant) areas that affect sensitivity and bandwidth?
Sensing and Measurement – Sensor Level Considerations
Sensitivity to extraneous measurands Does the sensor (actuator) respond to unintended inputs (commands)? (Does an accelerometer,
f e a e, a e d ai e e a e i ie di g fa e ig a ?)
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Sensing and Measurement
Challenges for SHM Sensing Systems
Number of sensors Instrumenting large structures with thousands of sensors still represents a sparsely instrumented system! Large sensor systems pose many challenges for reliability and data management
Ruggedness of sensors Sensing systems must last for many years with minimal maintenance Harsh environments (thermal, mechanical, moisture, radiation, corrosion) Need sensor diagnostic capability
There is no accepted sensor design methodology Optimal sensor placement (need models) Optimal waveform design for active sensing (need models)
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Data Acquisition
Data Transmission
Most conventional data acquisition systems transmit data and power to or from the sensor over a direct wired connection from the transducer to the central data analysis facility.
In some cases the central data analysis facility is then connected to the Internet such that the processed information can be monitored at a remote location.
A wired sensor network connected to a central data acquisition system
Wired Systems
A significant reduction in power consumption can be achieved by processing the data locally and only transmitting the results.
A sensor network directly connected to the central processing hardware.
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Wired Systems
Central Processing
Disadvantages: They are difficult to deploy in a retrofit mode because they usually require AC power, which is not always available.
The deployment of such a system can be challenging, with potentially over 75% of the setup time attributed to the installation of system wires and cables for larger- scale structures such as those used for long-span bridges.
The wires can be costly to maintain because of general environmental degradation and damage caused by agents external to the system, like rodents and vandals.
Advantages: A wide variety of commercially available off-the-shelf systems can be used. A wide variety of transducers can typically be interfaced with such a system. Recordings from multiple channels are more easily time-synchronised. These systems have been used in both a passive and active sensing manner.
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Data Acquisition
Wireless systems Rece ad a ced e ech gie ha e e ab ed SHM i g i e e a e e (WSSN ).
It is a promising alternative to the traditional wired SHM approaches.
The smart sensors are typically small, inexpensive, and capable of wireless communication and onboard computation, addressing many of the concerns regarding wired monitoring.
Wireless communication can remedy the cabling problem of the traditional monitoring system and significantly reduce the maintenance cost.
These attractive features enable the development of scalable monitoring systems and dense sensor networks.
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Data Acquisition
Decentralised Processing
Data collision is an issue with this approach.
Data collision is a phenomenon that results from a network device receiving several simultaneous requests to store or retrieve data from other devices on the network. With increasing numbers of sensors, a sensor node located close to the base station will experience more data transmission, possibly resulting in a significant bottleneck. Because the workload of each sensor node cannot be evenly distributed, the chances of data collision increase with expansion of the sensing networks.
Time synchronisation of sensors at different nodes is more difficult than for the wired system.
Not good for an active-sensing system deployment. Because active sensors can serve as actuators as well as sensors, the time synchronisation between multiple sensor/actuator units is again a challenging task.
Wireless systems
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Data Acquisition
The hybrid connection network advantageously combines the desirable characteristics of the previous two networks.
Hybrid systems
At the first level, several sensors are connected to a relay-based piece of hardware, which can serve as both a multiplexer and general purpose signal router (black box).
This device will manage the distributed sensing network, control the modes of sensing and actuation, and multiplex the measured signals.
At the next level, replicates of this hardware are linked to a de-centralised data control and processing station (red box). This control station is equipped with data acquisition boards, onboard computer processors and wireless telemetry, which is similar to the architecture of wireless system.
At the highest level, multiple data processing stations are linked to a central base station that delivers a damage report back to the user.
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Example: Architecture of a Bridge SHM System
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Data acquisition and transmission system (DATS)
Data acquisition units (DAU): Signal conditioners
Memory and data storage unit
Microcontroller
Communication device
Uninterruptible power supply
Fan/air conditioner
Lightning conductor
GPS time synchronizer
Example: Architecture of a Bridge SHM System
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Data processing and control system (DPCS)
Data Acquisition Control Signal Pre-processing Data with Abnormal Magnitude
Da a h S g f ca F c a
Da a h Va a
Signal Post-processing, Analysis and Visualisation Data Mining
Da a F
F e e c D a A a
T e-frequency Domain Analysis
Example: Architecture of a Bridge SHM System
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Data management system (DMS) Device database Measurement data database Structural analysis data database Health evaluation data database User data database
Structural evaluation system (SES)
On-line structural condition evaluation system P e a a i he c a c di i
Off-line structural health and safety assessment system L adi g ide ifica i M da ide ifica i a d da i g Da age diag i a d g i Pe f a ce a i g
Example: Architecture of a Bridge SHM System
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Data Normalisation
Example 1: An aircraft will change its mass during flight. A continuous change is caused by the burning of fuel. This is an operational issue. If an in-flight SHM system is based on resonance frequencies, one would not infer damage occurrence.
System response data will often be measured under varying operational and environmental conditions. The ability to normalise the data becomes very important to the damage detection process; without this, changes in the measured response caused by changing operational and environmental conditions may be mistaken as an effect of damage.
Environmental variability is an issue for space structures where the required operating e e a e a ge ca a f 250 C to 120 C
Example 2:
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Example 3: The stiffness properties of a bridge can and do change with temperature. This variation can be quite complex. Bridges are also susceptible to operational changes like variations in traffic loading. You can clearly see weekdays vibration vs. weekend from acceleration measurements from a cable-stayed bridge.
Data Normalisation
Often sensors will have to be added to monitor the changing operational and environmental conditions in an effort to develop a procedure that normalises the data to remove trends caused by these effects.
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Data Cleansing Data cleansing is the process of selectively choosing data to pass on to or reject from the feature selection process.
Signal processing techniques such as filtering can also be thought of as data cleansing procedures.
Example: As an example, an inspection of the test setup may reveal that a sensor was loosely mounted and, hence, based on the judgement of the individuals performing the measurement; this set of data or the data from that particular sensor may be selectively deleted from the feature selection process.
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Data Compression
Data Compression is the process of reducing the dimension of the measured data.
Dimensionality reduction is important to reduces the size of large amount of data produced by SHM system and to extract the most informative features from raw data.
Dimensionality Reduction Example:
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Data Fusion Data fusion is the process of combining information from multiple sources in an effort to enhance the fidelity of the damage detection process.
With data fusion, we can identify the progress of damage.
Example:
Without data fusion, we cannot identify the progress of damage.
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Feature Extraction A damage sensitive feature is some quantity extracted from the measured system response data that indicates the presence (or not) of damage in a structure. An ideal is a low-dimensional feature set that is highly sensitive to the condition of the structure.
Example: If one wished to monitor the condition of a gearbox, one might start by attaching an accelerometer to the outer casing. This sensor would yield a stream of acceleration time data. To reduce the dimension of the data without compromising the information content, one might use the time series to compute a spectrum. Once the spectrum is available, one can then extract only those spectral lines centred around the harmonics, as these are known to carry information about the health of the gears.
Vibration-Based SHM
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A great deal of this course will be concerned with vibration-based approaches to SHM.
This is amply justified by the fact that, in most practical scenarios, changes to a structural system caused by damage manifest themselves as changes to the mass, stiffness and energy dissipation characteristics of the system.
Damage can also manifest itself as changes to the boundary conditions of a structure ha e ea he e e a cha ge he c e dynamic response characteristics.
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Beam/Plate Like Structures
Vibration-Based Damage Identification
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Vibration-Based Damage Identification
The basic premise is that damage will alter the stiffness, mass, or energy dissipation properties of a system, which in turn alter the measured dynamic response of the system.
It is a global approach since it is attributed to monitoring the whole structural performance.
The development of vibration-based damage detection has been driven by the rotating machinery, aerospace and offshore oil platform applications.
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Vibration-Based Damage Identification
Developments in vibration-based damage detection are closely coupled with the evolution, miniaturization and cost reductions in sensors, data acquisition systems and digital computing hardware.
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Rotating Machinery Applications To date, the most successful applications of vibration-based damage detection has been for condition monitoring of rotating machinery.
Significant economic benefits have driven the development of machine condition monitoring. Maintenance is typically accounts for 15-40% of production costs. Well-controlled environments. Typically, there are a tractable number of well-defined damage scenarios to be monitored (example: bearing failure or shaft misalignment). The possible locations of the damage are known a priori. The operational limitation on acquiring data is that the machine will typically be in operation and performing its normal function or will be in a transient start-up or shut- down mode.
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Offshore Structures
Many practical problems compared to rotary machinery: Damage location is unknown Majority of the structure is not readily accessible for measurement Machine noise Non-uniform inputs Hostile environment for instrumentation Changes in foundation with time Inability of wave motion to excite higher vibration modes
Off h e I d e i i f d a d i g he 70 a d 80 i a eff a ch ac ica damage detection and health monitoring of offshore platforms.
Modal parameters as damage-sensitive feature
Motivations: Gather insights in dynamic behaviour in offshore conditions input for new designs, optimization of structures Minimize O&M costs Identify the current state of the offshore structure (i.e. after a storm the scour protection can be damaged) Extend lifetime of offshore structure
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Aerospace Structures
Damage types of interest to the aerospace industry: Corrosion and cracking in metallic components Delamination, debonding, fibre breakage and matrix microcracking in composite components
The aerospace industry began to study the use of damage detection technology during the late 1970s and early 1980s for a variety of civilian and defense applications.
The development of SHM for aerospace applications has been driven by both life-safety and economic concerns.
Weight minimization for the sensing system The need for sensors that do not pose a spark hazard when monitoring near fuel Varying operational and environmental conditions Lack of accessibility to critical structural components Influence of nonstructural components such as cables and insulation on the dynamic response of the structure
Constraints on performing the damage assessment:
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Aerospace Structures
Development of modal identification and finite element model updating approaches lead design of approaches to detect, locate and quantify damage.
Operational variability: Changing mass associated with fuel consumption and varying payloads Changing aerodynamic loading caused by various in-flight manoeuvres Changes in air speed and turbulence
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Highway Bridge Structures
Develop SHM techniques to augment mandated visual inspections. Driven by several catastrophic bridge failures over last decades. Desire to reduce lifecycle costs
The civil engineering community has studied vibration-based damage assessment of bridge structures since the early 1980s.
Bridges are generally rated and monitored during biennial inspections, largely with the use of visual inspection techniques. This procedure is slow, not quantifiable and portions of the bridge are inaccessible for such visual inspection.
The first bridge was deployed with a SHM system was I-40 Bridge by Los Alamos Dynamics Lab in New Mexico.
Typical damage concerns: Corrosion, fatigue cracks, loss of prestressing forces and scour at the bridge pier.
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Highway Bridge Structures
Tsing Ma Bridge in Hong Kong (approx. $20 million for 1000+ channels of data acquisition)
Operational and environmental constraints: Traffic flow can vary on a 24-hour cycle and a weekly cycle. Environmental variability can occur over a 24-hour cycle, over a seasonal cycle and
intermittent cycles caused by varying rainfall conditions.
Measurements Limitations: Access issues Traffic usually prevents the topside of the deck from being instrumented. For large bridges many of the structural elements are difficult to instrument because it is impractical to
access them in a safe and economic manner. Harsh environment The sensors and associated data transmission hardware are subjected to harsh environments that make
equipment reliability a serious issue. Size
The physical size of these structures presents many practical challenges for SHM.
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SHM The Sydney Harbour Bridge
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63 Sensors 600Hz 25GB data/day
SHM A short span cable-stayed bridge
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Recent Development for Bridge Health Monitoring Indirect Structural Health Monitoring Drive by bridge inspection using vehicle mounted sensor
No sensor to be installed on the bridge Higher spatial resolution
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Vibration-Based Damage Identification Challenges
1. Damage is typically a local phenomenon and may not significantly influence the lower frequency global response of a structure that is normally measured during vibration tests.
2. Environmental and operational variations, such as varying temperature, moisture, and loading conditions affecting the dynamic response of the structures cannot be overlooked either. In fact, these changes can often mask subtler structural changes caused by damage.
3. Damage detection must be performed in an unsupervised learning mode. Here, the term unsupervised learning implies that data from damaged systems are not available.
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Assignment 1 (10% of Final Mark)
Literature Review 1. Identify a large-scale real-world operating structure (bridge, tower, dam, mining structure,
wind turbine, rail- a c e, ae ace/ echa ica c e, ) i h a de ed SHM system. You can choose one of the bridges shown in the next slides (if you want).
2. Describe the structure and the justification behind implementation of SHM system. 3. Describe the operational evaluation process discussed in this lecture. 4. Explain the deployed SHM system in terms of measurement set up, i.e. number of sensors,
type of sensors, place of sensors, data acquisition and data transmission set up. 5. What are the sensor characteristics? (you can complete this after lecture 2) 6. Explain operational and environmental conditions of the structure. 7. Explain damage scenarios of interest and any corresponding results in terms of identification
of those damage scenarios.
The report should be minimum three A4 pages, single column, 12-point Times New Roman font. Reference page is not counted in three pages.
Due at 5pm 2nd October 2020. (A penalty of 10% will apply for each day of late submission for assignments.)
Online submission portal will be created on Moodle.
SHM System on Shenzhen western corridor and Tsing Ma bridges
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Layout of the sensory system and the data acquisition system on Tsing Ma Bridge.
Layout of the sensory system and the data acquisition system on Hong Kong – Shenzhen western corridor bridge.
SHM System on Ting Kau and Kap Shui Mun Bridges
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Layout of the sensory system and the data acquisition system on Kap Shui Mun Bridge.
Layout of the sensory system and the data acquisition system on Ting Kau Bridge.
SHM System on Stonecutters Bridge
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Layout of the sensory system and the data acquisition system on Stonecutters Bridge.
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Comms & Power
800 joints monitored Remote Database Server
SHM System on the Sydney Harbor Bridge
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References
[1] Randall, Robert Bond. Vibration-based condition monitoring: industrial, aerospace and
automotive applications. John Wiley & Sons, 2011.
[2] Bridge Maintenance and Repair Manual, VICROADS ASSET SERVICES, STRUCTURAL
TECHNOLOGY AND ASSETS.
[3] Charles R. Farrar, Introduction to Structural Health Monitoring, Los Alamos Dynamics
Structural Dynamics and Mechanical Vibration Consultants.
[4] Farrar, C.R. and Worden, K., 2012. Structural health monitoring: a machine learning
perspective. John Wiley & Sons.
