Structural Health Monitoring

Structural Health Monitoring

Md. Abul Kalam Tutul Dept. Of Civil Engineering Mymensingh Engineering College.

Structural Health Monitoring

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 Contents: 1. 2. 3. 4.

Introduction Structural health monitoring (SHM) Statistical pattern recognition Health assessment of engineered structures of bridges, buildings and other related infrastructures 5. Operational evaluation 6. Data acquisition, normalization and cleansing 7. Feature extraction and data Compression 8. Statistical model development 9. Fundamental axioms 10.Components 11.Main functions of structural health monitoring 12.Objective of Structural Health Monitoring 13.Steps of Structural Health Monitoring 14.SHM Involves 15.Structural Monitoring Challenges 16.Some Barriers in SHM up today 17.SHM is the imitation of the human nervous System 18.SHM in aircraft maintenance 19.SHM technology helps in 20.Damage Identification 21.Test Structure 22.SHM by Structural System Identification 23.How to Do SHM in practice? 24.Vibration Based SHM: Sensors 25.Vibration Based SHM: Model-Based Techniques 26.Vibration Based SHM: Uncertainties 27.Wireless sensor role in SHM 28.SHM Current Uses 29.Issues with SHM Implementation 30.Limitation of traditional methods 31.Vision of the Future 32.Examples 33.Other large examples 34.Conclusions 35.References

Abstract The process of implementing a damage identification strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). Here, damage is defined as changes to the material and/or geometric properties of these systems, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. A wide variety of highly effective local nondestructive evaluation tools are available for such monitoring. However, the majority of SHM research conducted over the last 30 years has attempted to identify damage in structures on a more global basis. The past 10 years have seen a rapid increase in the amount of research related to SHM as quantified by the significant escalation in papers published on this subject. The increased interest in SHM and its associated potential for significant life-safety and economic benefits has motivated the need for this theme issue. This introduction begins with a brief history of SHM technology development. Recent research has begun to recognize that the SHM problem is fundamentally one of the statistical pattern recognition (SPR) and a paradigm to address such a problem is described in detail herein as it forms the basis for organization of this theme issue. In the process of providing the historical overview and summarizing the SPR paradigm, the subsequent articles in this theme issue are cited in an effort to show how they fit into this overview of SHM. In conclusion, technical challenges that must be addressed if SHM is to gain wider application are discussed in a general manner.

Structural health monitoring Introduction Qualitative and non-continuous methods have long been used to evaluate structures for their capacity to serve their intended purpose. Since the beginning of the 19th century, railroad wheel-tappers have used the sound of a hammer striking the train wheel to evaluate if damage was present.[2] In rotating machinery, vibration monitoring has been used for decades as a performance evaluation technique.[1] Two techniques in the field of SHM are wave propagation based techniques Raghavan and Cesnik[3] and vibration based techniques.[4][5][6] Broadly the literature for vibration based SHM can be divided into two aspects, the first wherein models are proposed for the damage to determine the dynamic characteristics, also known as the direct problem, for example refer, Unified Framework[7] and the second, wherein the dynamic characteristics are used to determine damage characteristics, also known as the inverse problem, for example refer.[8] In the last ten to fifteen years, SHM technologies have emerged creating an exciting new field within various branches of engineering. Academic conferences and scientific journals have been established during this time that specifically focus on SHM.[2] These technologies are currently becoming increasingly common.

Structural health monitoring (SHM) It refers to the process of implementing a damage detection and characterization strategy for engineering structures. Here damage is defined as changes to the material and/or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which adversely affect the system's performance. The SHM process involves the observation of a system over time using periodically sampled dynamic response measurements from an array of sensors, the extraction of damage sensitive features from these measurements, and the statistical analysis of these features to determine the current state of system health. 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. 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 integrity of the structure.[1]

Statistical pattern recognition The SHM problem can be addressed in the context of a statistical pattern recognition paradigm.[9][10] This paradigm can be broken down into four parts: (1) Operational Evaluation, (2) Data Acquisition and Cleansing, (3) Feature Extraction and Data Compression, and (4) Statistical Model Development for Feature Discrimination. When one attempts to apply this paradigm to data from real world structures, it quickly becomes apparent that the ability to cleanse, compress, normalize and fuse data to account for operational and environmental variability is a key implementation issue when addressing Parts 2-4 of this paradigm. These processes can be implemented through hardware or software and, in general, some combination of these two approaches will be used.

Health assessment of engineered structures of bridges, buildings and other related infrastructures Commonly known as Structural Health Assessment (SHA) or SHM, this concept is widely applied to various forms of infrastructures, especially as countries all over the world enter into an even greater period of construction of various infrastructures ranging from bridges to skyscrapers. Especially so when damages to structures are concerned, it is important to note that there are stages of increasing difficulty that require the knowledge of previous stages, namely: 1. Detecting the existence of the damage on the structure 2. Locating the damage 3. Identifying the types of damage 4. Quantifying the severity of the damage. It is necessary to employ signal processing and statistical classification to convert sensor data on the infrastructural health status into damage info for assessment.

Operational evaluation Operational evaluation attempts to answer four questions regarding the implementation of a damage identification capability: i) What are the life-safety and/or economic justification for performing the SHM? ii) How is damage defined for the system being investigated and, for multiple damage possibilities, which cases are of the most concern? iii) What are the conditions, both operational and environmental, under which the system to be monitored functions? iv) What are the limitations on acquiring data in the operational environment? Operational evaluation begins to set the limitations on what will be monitored and how the monitoring will be accomplished. This evaluation starts to tailor the damage identification process to features that are unique to the system being monitored and tries to take advantage of unique features of the damage that is to be detected.

Data acquisition, normalization and cleansing The data acquisition portion of the SHM process involves selecting the excitation methods, the sensor types, number and locations, and the data acquisition/storage/transmittal hardware. Again, this process will be application specific. Economic considerations will play a major role in making these decisions. The intervals at which data should be collected is another consideration that must be addressed. Because data can be measured under varying conditions, the ability to normalize the data becomes very important to the damage identification process. As it applies to SHM, data normalization is the process of separating changes in sensor reading caused by damage from those caused by varying operational and environmental conditions. One of the most common procedures is to normalize the measured responses by the measured inputs. When environmental or operational variability is an issue, the need can arise to normalize the data in some temporal fashion to facilitate the comparison of data measured at similar times of an environmental or operational cycle. Sources of variability in the data acquisition process and with the system being monitored need to be identified and minimized to the extent possible. In general, not all sources of variability can be eliminated. Therefore, it is necessary to make the appropriate measurements such that these sources can be statistically quantified. Variability can arise from changing environmental and test conditions, changes in the data reduction process, and unit-to-unit inconsistencies. Data cleansing is the process of selectively choosing data to pass on to or reject from the feature selection process. The data cleansing process is usually based on knowledge gained by individuals directly involved with the data acquisition. As an example, an inspection of the test setup may reveal that a sensor was be eliminated. Therefore, it is necessary to make the appropriate measurements such that these sources can be statistically quantified. Variability can arise from changing environmental and test conditions, changes in the data reduction process, and unit-to-unit inconsistencies. Data cleansing is the process of selectively choosing data to pass on to or reject from the feature selection process. The data cleansing process is usually based on knowledge gained by individuals directly involved with the data acquisition. As an example, an inspection of the test setup may reveal that a sensor was that can improve the data acquisition process.

Feature extraction and data compression

The area of the SHM process that receives the most attention in the technical literature is the identification of data features that allows one to distinguish between the undamaged and damaged structure. Inherent in this feature selection process is the condensation of the data. The best features for damage identification are, again, application specific. One of the most common feature extraction methods is based on correlating measured system response quantities, such a vibration amplitude or frequency, with the first-hand observations of the degrading system. Another method of developing features for damage identification is to apply engineered flaws, similar to ones expected in actual operating conditions, to systems and develop an initial understanding of the parameters that are sensitive to the expected damage. The flawed system can also be used to validate that the diagnostic measurements are sensitive enough to distinguish between features identified from the undamaged and damaged system. The use of analytical tools such as experimentally-validated finite element models can be a great asset in this process. In many cases the analytical tools are used to perform numerical experiments where the flaws are introduced through computer simulation. Damage accumulation testing, during which significant structural components of the system under study are degraded by subjecting them to realistic loading conditions, can also be used to identify appropriate features. This process may involve induced-damage testing, fatigue testing, corrosion growth, or temperature cycling to accumulate certain types of damage in an accelerated fashion. Insight into the appropriate features can be gained from several types of analytical and experimental studies as described above and is usually the result of information obtained from some combination of these studies. The operational implementation and diagnostic measurement technologies needed to perform SHM produce more data than traditional uses of structural dynamics information. A condensation of the data is advantageous and necessary when comparisons of many feature sets obtained over the lifetime of the structure are envisioned. Also, because data will be acquired from a structure over an extended period of time and in an operational environment, robust data reduction techniques must be developed to retain feature sensitivity to the structural changes of interest in the presence of environmental and operational variability. To further aid in the extraction and recording of quality data needed to perform SHM, the statistical significance of the features should be characterized and used in the condensation process.

Statistical model development The portion of the SHM process that has received the least attention in the technical literature is the development of statistical models for discrimination between features from the undamaged and damaged structures. Statistical model development is concerned with the implementation of the algorithms that operate on the extracted features to quantify the damage state of the structure. The algorithms used in statistical model development usually fall into three categories. When data are available from both the undamaged and damaged structure, the statistical pattern recognition algorithms fall into the general classification referred to as supervised learning. Group classification and regression analysis are categories of supervised learning algorithms. Unsupervised learning refers to algorithms that are applied to data not containing examples from the damaged structure. Outlier or novelty detection is the primary class of algorithms applied in unsupervised learning applications. All of the algorithms analyze statistical distributions of the measured or derived features to enhance the damage identification process.

Fundamental axioms Based on the extensive literature that has developed on SHM over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or general principles, have emerged.[11] The axioms are listed as follows:  Axiom I: All materials have inherent flaws or defects;  Axiom II: The assessment of damage requires a comparison between two system states;  Axiom III: Identifying the existence and location of damage can be done in an unsupervised learning mode, but identifying the type of damage present and the damage severity can generally only be done in a supervised learning mode;  Axiom IV: Sensors cannot measure damage. Feature extraction through signal processing and statistical classification is necessary to convert sensor data into damage information;  Axiom IV: Without intelligent feature extraction, the more sensitive a measurement is to damage, the more sensitive it is to changing operational and environmental conditions;  Axiom V: The length- and time-scales associated with damage initiation and evolution dictate the required properties of the SHM sensing system;  Axiom VI: There is a trade-off between the sensitivity to damage of an algorithm and its noise rejection capability;  Axiom VII: The size of damage that can be detected from changes in system dynamics is inversely proportional to the frequency range of excitation.

Components SHM System's elements include:  Structure  Sensors  Data acquisition systems  Data transfer and storage mechanism  Data management  Data interpretation and diagnosis: 1. System Identification 2. Structural model update 3. Structural condition assessment 4. Prediction of remaining service life An example of this technology is embedding sensors in structures like bridges and aircraft. These sensors provide real time monitoring of various structural changes like stress and strain. In the case of civil engineering structures, the data provided by the sensors is usually transmitted to a remote data acquisition centers. With the aid of modern technology, real time control of structures (Active Structural Control) based on the information of sensors is possible.

Main functions of structural health monitoring

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Monitor and assess load conditions Examine current design philosophy Verify new analytical methods and computer simulations Assess structural performance and detect damage Facilitate inspection and maintenance works Help authority to make quick and right decision in emergency cases Ultimate goal is to ensure serviceability, safety, and sustainability The Hong Kong Polytechnic University

Objective of Structural Health Monitoring 

Performance enhancement of an existing structure

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Monitoring of structures affected by external factors

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Feedback loop to improve future design based on experience

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Assessment of post-earthquake structural integrity

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Decline in construction and growth in maintenance needs

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The move towards performance-based design philosophy

Steps of Structural Health Monitoring    

Determination of damage existence Determination of damage’s geometric location Quantification of damage severity Prediction of remaining life of the structure

SHM Involves    

Health monitoring Operational Evaluation Data Feature Extraction Statistical Models Development

Structural Monitoring Challenges  Infrastructure is expected to provide:  reliable service for long periods of time,  Undergoing major technology changes,  spanning several generations and experiencing dramatic evolutions  Develop Wireless Sensor Networks

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Reliable Energy aware Smart

 Develop Design-to-service Solutions     

Efficient Monitoring Digital Signal Processing strategies Evaluation Criteria Knowledge bases

 Develop Smart Control Units  

Real-time Feedback Centralized (or not)

Some Barriers in SHM up today     

Conventional cables High installation costs Vulnerable to ambient signal noise corruption Vulnerable to earthquake conditions Size and complexity of large structures require a large number of sensing points to be installed

SHM is the imitation of the human nervous System Sensor Net-work

Diagnosis:

subsystem deals with monitoring of the entire structure under inspection. It has an underlying wired/ wireless network of sensors and a variety of sensing mechanisms need to be adopted for different sections of aircraft.  Periodic measurements are tapped from these in-situ sensors either through wired or wireless media into a centralized analysis station in the SHM system. Prognosis: subsystem takes the periodic inspection data from diagnosis subsystem to analyze and estimate various possible internal and external damages that might have occurred in the structure.  The estimated damage characteristics are used in the damage evolution models to estimate the remaining life of the structure as well as to find a necessity to trigger maintenance. 

Life Extension & Predictive Maintenance:  

The damage evolution models are effectively combined with probability of detection (POD) models for structural integrity assessment and remaining life assessment. Cost-benefit analysis is performed to arrive at a tradeoff between the safety allowance and maintenance costs to be incurred while triggering maintenance in the given conditions

The Three key subsystems of SHM

SHM in aircraft maintenance   

Due to various stress conditions during the flight, aircraft structures develop various kinds of defects which include stress corrosion, cracks, accidental damage, impact damage, delamination’s, deboning’s, water ingress, damage due to loads/strain. A thorough inspection schedule is instructed by the aircraft manufacturer, which include various types of checks as shown in Figure below. The current state of the art in the schedule-based inspection and maintenance is to conduct time-based localized inspection of few selected parts of the structure. Hence, at any given point of time, it is difficult to comprehensively understand the structure’s health in totality.

Aircraft Maintenance checks (Periodic Inspection)

 Schedule based maintenance works well during designed service life. However, over

time, the focus shifts towards life extension i.e. need to use aircraft longer than planned or to use it for different missions than designed.

 As Aging aircrafts continue in service, they result in increased inspection time, increased operations and maintenance costs and decreased availability, due to higher risk of hazard.

 Such high risk of hazard and maintenance costs can be minimized by employing a

continuous online monitoring technique which triggers the maintenance schedule as and when required.

 SHM enables condition based maintenance with a capability to initiate inspection requirements not only based on the scheduled intervals, but also on actual wear indicators exhibited by the equipment at that given point of time.

Issues Related to the Aging Infrastructure

SHM technology helps in

 Increased availability of the aircraft  Effective assessment of actual damage events  Reduced costs of life-cycle and total ownership  Reduced logistics  Increased safety and reliability Moving beyond preventive maintenance into predictive maintenance, in-situ Structural Health Monitoring (SHM) can provide  Long-term cost savings and  Extended fleet life. Thus, SHM will enable new maintenance concepts.

Damage Identification  Type of damages – Ductile Higher deformations Better suited for SHCE applications Baseline data essential – Brittle Sudden and little or no deformation until failure Not suitable for SHCE application – Progressive

Higher deformations SHCE may be useful Higher probability of system failure

 Types of damage Fatigue Corrosion Wear and Tear Large Deformation (impact, delamination, etc…)

 Structural Identification Understand structural behavior Dynamic, static, or both (depends on loading) Variety of methods (common sense to FEM/BEM) Use measurements Validate/calibrate structural model Understand differences between actual and designed/modeled behavior

 Translate measurements parameters/states  Modeling aspects depend on

to

meaningful

structural

Loading type Measurements Damage identification methods Coarseness of the results Use of model after the initial decision-making

 Complicated or fine models does not mean they are better  Loading interactions and boundary conditions are very important

Test Structure  Rte 2 over Hudson River Bridge – Built in 1969 – Eight span steel stringer bridge – 430-m long and 23-m wide – Columns deteriorated (non-structural) – Leaking joints – Repaired (Patched) in 1991 and 1992 – Conventional repairs failed quickly. These were also expensive and require considerable time, personnel, and money

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Repaired in July-August 1999 FRP Wrapping in September 1999 Done by maintenance personnel Instrumentation for monitoring – Corrosion rate

– Humidity – Temperature – Three locations for column based potential data

Results

SHM by Structural System Identification

Courtesy of Prof. E. Chatzi, ETH

How to Do SHM in practice? 

Visual Inspection  Fully experience-based  Subjective/Non-quantitative

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Non-Destructive Evaluation(NDE) o Various technologies for different purposes o Demands a high degree of expertise o Time consuming and costly o Usually requires a priori knowledge of the potentially damaged region o Works only in accessible regions of the structure o Interruption and downtime o Labour intensive and risky

Fig: Visual Inspection

Dye Penetration Test

Magnetic Particle Testing

Thermal Infrared Test

Eddy Current Test

Ultrasonic Test

Acoustic Emission Test

Thermal Infrared Test Fig: NDT

 Static-Based SHM Based on the premise that damage will alter the static properties of the structure. –e.g. displacements, rotations

 Drawback

Considerable static deflection requires large amount of static force

 Vibration-Based SHM  Based on the premise that damage will alter the dynamic properties of the structure. –e.g. structural response, frequencies, mode shapes, damping or modal strain energy change  By measuring the structural response by means of sensors strategically placed on the structure, and intelligently analyzing these measured responses, it is possible to identify damage occurrence.  It can be done either in modal domain or physical domain

Vibration Based SHM: Sensors  Different forms of dynamic structural response   

Displacement, Velocity, Acceleration, Strain. Which ones to measure depends on monitoring conditions and objectives. Sensing technology: an ever emerging field of study

 Based on what to measure, different sensors available o o o o o

Laser Displacement Sensors(LDS) Velocity Transducers Seismometers Piezoelectric Accelerometers Strain Gauges

 Most of these sensors can be wirelessly connected

Collection of Sensory Information

Load Cell (Force)

LVDT (Displacement)

Accelerometer Strain Gauge (Strain)

Pros and cons of various types of sensors:  Bandwidth  displacement sensors capture low frequency modes  acceleration sensors capture high frequency modes  Global vs. Local 

strain gauges capture local dynamics better

(Acceleration)

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accelerometers/displacement sensors measure Global dynamics

Vibration Based Techniques  

SHM:

Model-Based

Based on a model (e.g. F.E.) of the monitored structure. Optimization based methods: 

An initial model is updated using measured structural response. Also called FE model updating 

Optimization algorithms are run by iteratively changing the values of some structural properties (e.g. Young’s modulus), so that the FEM parameters match measured parameters.

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Measured parameters: Measured responses or some parameters obtained from measured responses (e.g. modal properties). 

Usually require repeatedly solving the forward problem.

 Alternatively, inverse problem solution approach:   

Identify modal parameters using some system identification method. Use identified modal parameters to obtain physical parameter (mass, damping, stiffness) matrices. Does not require repeatedly solving the forward problem, but is more complicated.

Pros 

Allow damage detection, as well as damage location and extent estimation. May even be used to assess the damage type and to estimate the structure’s remaining life, though research is still at its onset in this regard

Cons    

Require high user expertise Affected by modelling assumptions (e.g. boundary conditions, number of DOFs, material properties, etc.) Often too many unknowns Usually computationally expensive

Vibration Based SHM: Uncertainties Many sources of uncertainty in the different stages of SHM: During data acquisition:    

Measurement noise, Environmental effects (different temperature, humidity levels), Unknown and non-stationary inputs (traffic, wind, earthquake; may excite different frequency regions), Missing data (not every point on the structure observed).

During feature extraction/modeling/identification:   

Modeling assumptions, Errors associated with any numerical method, Non-unique identification (many models may fit the measured data equally well).

Wireless sensor role in SHM 

The SHM system based on Wireless Sensor Networks (WSN) has shown considerable promise.  It has several advantages over most traditional SHM systems: 1. Low production and maintenance cost. 2. Fast installation 3. Reprogrammable software and convenient reconfiguration.  Using WSN, a dense deployment of measurement points in a SHM system is possible, which helps to refine the damage detection results

1. 2. 3. 4. 5.

On-board microprocessor Sensing capability Wireless communication Battery powered low cost

Prototype by Lynch (2002)

Berkeley Mote

BTnode rev3 (2004)

Fig: Wireless Sensors

U3 (2002)

iMote2 (2004)

Despite the potentiality offered by WS, some hardware limitations needs to be addressed when pursuing real SHM implementations using wireless sensors. Some of these hardware limitations are associated to:  Wireless communication  Time synchronization among sensors  Reduced processing and memory capacity  Power management

SHM Current Uses  Verification/validation of – Advanced technologies – Innovative materials – Analytical methods – Guidelines and design procedures

 Augment bridge inspections by making inspection more quantitative  Improve load ratings and remove postings

Issues with SHM Implementation  Most current methods meant for local NDT  Increased Cost – Initial, maintenance, analysis

 Structural environment changes – Slow: Corrosion, general degradation, etc. – Sudden: Impact, blast, etc. – Overloads etc., due to change in use

 Long break-even period  Project vs. network

Limitation of traditional methods   

Dense arrays of sensor are required to effectively monitor structures Wired monitoring systems are expensive, with much of the cost derived from cabling and installation Centralized data collection is not challenging for monitoring large civil infrastructure

Vision of the Future “relying on and leveraging real-time access to living databases, sensors, diagnostic tools, and other advanced technologic to ensure informed decision are made’’

Wireless Smart Sensors will act as the fundamental building block to realize this vision of the future  

Low costs allow for dense deployment as needed Modularity provides inherent flexibility for use in both permanent and temporary applications

Fig: Future of SHM

Examples Bridges in Hong Kong The Wind and Structural Health Monitoring System (WASHMS) is a sophisticated bridge monitoring system, costing US$1.3 million, used by the Hong Kong Highways Department to ensure road user comfort and safety of the Tsing Ma, Ting Kau, Kap Shui Mun and Stonecutters bridges.[12] In order to oversee the integrity, durability and reliability of the bridges, WASHMS has four different levels of operation: sensory systems, data acquisition systems, local centralized computer systems and global central computer system. The sensory system consists of approximately 900 sensors and their relevant interfacing units. With more than 350 sensors on the Tsing Ma Bridge, 350 on Ting Kau and 200 on Kap Shui Mun, the structural behavior of the bridges is measured 24 hours a day, seven days a week. The sensors include accelerometers, strain gauges, displacement transducers level sensing stations, anemometers, temperature sensors and dynamic weight in- motion sensors. They measure everything from tarmac temperature and strains in structural members to wind speed and the deflection and rotation of the kilometers of cables and any movement of the bridge decks and towers. These sensors are the early warning system for the bridges, providing the essential information that help the Highways Department to accurately monitor the general health conditions of the bridges. The structures have been built to withstand up to a one-minute mean wind speed of 95 meters per second. In 1997, when Hong Kong had a direct hit from Typhoon Victor, wind speeds of 110 to 120 kilometers per hour were recorded. However, the highest wind speed on record

occurred during Typhoon Wanda in 1962 when a 3-second gust wind speed was recorded at 78.8 meters per second, 284 kilometers per hour. The information from these hundreds of different sensors is transmitted to the data acquisition outstation units. There are three data acquisition outstation units on Tsing Ma Bridge, three on Ting Kau and two on the Kap Shui Mun. The computing powerhouse for these systems is in the administrative building used by the Highways Department in Tsing Yi. The local central computer system provides data collection control, post processing, transmission and storage. The global system is used for data acquisition and analysis, assessing the physical conditions and structural functions of the bridges and for integration and manipulation of the data acquisition, analysis and assessing processes.  Monitoring Hong Kong's Bridges Real-Time Kinematic Spans The Gap

Other large examples The following projects are currently known as some of the biggest on-going bridge monitoring       

The Rio–Antirrio bridge, Greece: has more than 100 sensors monitoring the structure and the traffic in real time. Millau Viaduc, France: has one of the largest systems with fiber optics in the world which is considered state of the art. The Huey P Long bridge, USA: has over 800 static and dynamic strain gauges designed to measure axial and bending load effects. The Fatih Sultan Mehmet Bridge, Turkey: also known as the Second Bosphorus Bridge. It has been monitored using an innovative wireless sensor network with normal traffic condition. Masjid al-Haram#Current expansion project, Mecca, Saudi Arabia : has more than 600 sensors ( Concrete pressure cell, Embedment type strain gauge, Sister bar strain gauge, etc.) installed at foundation and concrete columns. This project is under construction. The Sydney Harbour Bridge in Australia is currently implementing a monitoring system involving over 2,400 sensors. Asset managers and bridge inspectors have mobile and web browser decision support tools based on analysis of sensor data. The Queensferry Crossing, currently under construction across the Firth of Forth, will have a monitoring system including more than 2,000 sensors upon its completion. Asset managers will have access to data for all sensors from a web based data management interface, including automated data analysis.

For bridges Health monitoring of large bridges can be performed by simultaneous measurement of loads on the bridge and effects of these loads. It typically includes monitoring of:  Wind and weather  Traffic  Prestressing and stay cables  Deck

 Pylons  Ground Provided with this knowledge, the engineer can:  Estimate the loads and their effects  Estimate the state of fatigue or other limit state  Forecast the probable evolution of the bridge's health The state of Oregon in the United States, Department of Transportation Bridge Engineering Department has developed and implemented a Structural Health Monitoring (SHM) program as referenced in this technical paper by Steven Lovejoy, Senior Engineer. [13] References are available that provide an introduction to the application of fiber optic sensors to Structural Health Monitoring on bridges. [14]

CONCLUSIONS Health monitoring of structures is becoming more and more important: its ultimate target is the ability to monitor the structure throughout its working life in order to reduce maintenance requirements and subsequent downtime. Currently, visual inspection is the standard method used for health assessment of structures, along with non-destructive evaluation techniques. However, most of these techniques require a lot of manual work and a significant downtime. Thus, currently an increasing interest in SHM is rising, because it can provide cost savings by reducing the number of manual inspections (Achenbach, 2007). MEMS and wireless sensing are becoming desirable features in SHM systems and there has been a large development of new sensors during the last years. However, optimized and autonomous SHM systems are still not so spread. In this paper, after a review of some sample cases worldwide, some aspects related to the implementation of an integrated SHM system covering several structures on a wide territory has been analyzed. An effective Structural Health Monitoring system has been designed based on integration of several sensors and hardware components in a modular architecture. Even if the advances in the field of Information Technology and communications assure data transmission also in critical conditions, it is worth noting that availability of procedures able to reduce the transmission data volumes is a key aspect for reliability and sustainability of infrastructure, in particular when several constructions are monitored at the same time by a single master node. The distributed structure of the system, based on local and master nodes, and the availability of automated modal parameters identification and tracking procedures, will ensure a significant reduction of the volume of data to be transmitted, so increasing the performance and the reliability of the system. It will be based on integration of several procedures in a home-made software developed in Lab View environment and will be an interest benchmark also for early warning applications.

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Ooijevaar T.H., Rogge M.D., Loendersloot R., Warnet L., Akkerman R., Tinga T., Vibro-acoustic modulationbased damage identification in a composite skin-stiffener structure, Structural Health Monitoring, 2016. Ooijevaar T.H., Rogge M.D., Loendersloot R., Warnet L.L., Akkerman R., Tinga T., Nonlinear dynamic behavior of an impact damaged composite skin-stiffener structure, Journal of Sound and Vibration, 353:243–258, 2015. Ooijevaar T.H., Warnet L.L., Loendersloot R., Akkerman R., Tinga T., Impact damage identification in composite skin-stiffener structures based on modal curvatures, Structural Control and Health Monitoring, 2015. Ooijevaar T.H., Loendersloot R., Warnet L.L., de Boer A., Akkerman R., Vibration based structural health monitoring of a composite T-beam, Composite Structures, 92(9):2007–2015, 2010. Ponzo F. C., Ditommaso R., Auletta G., Mossucca A. (2010). A Fast Method for Structural Health Monitoring of Italian Strategic Reinforced Concrete Buildings. Bulletin of Earthquake Engineering. doi:10.1007/s10518-0109194-6. Volume 8, Number 6, Pages 1421-1434. Picozzi M., Milkereit C., Zulfikar C., Fleming K., Ditommaso R., Erdik M., Zschau J., Fischer J., Safak E., Özel O. and Apaydin N. (2010). Wireless technologies for the monitoring of strategic civil infrastructures: an ambient vibration test on the Fatih Sultan Mehmet Suspension Bridge in Istanbul, Turkey. Bulletin of Earthquake Engineering. Volume 8, Number 3. doi:10.1007/s10518-009-9132-7. Ponzo F.C., Auletta G., Ditommaso R. & Mossucca A. (2010). A Simplified Method for a Fast Structural Health Monitoring: methodology and preliminary numerical results. 14th European Conference on Earthquake Engineering. Proceedings Volume. Ohrid, Republic of Macedonia. August 30 – September 3, 2010. Menafro F.,(2015) Method for Prognostics of an Aircraft Structure Based on Structural Testing Eftekhar Azam S. (2014). Online Damage Detection in Structural Systems. Springer. doi:10.1007/978-3-31902559-9. https://link.springer.com/book/10.1007%2F978-3-319-02559-9] External links NDT.net Open Access Database contains EWSHM proceedings and much more SHM articles Engineering Institute, Los Alamos National Laboratory Nano-Engineering and Smart Structures Technologies (NESST) Laboratory, University of California, Davis University of Siegen Germany Laboratory for Intelligent Structural Technology, University of Michigan Centre for Non-Destructive Evaluation IIT Madras,India CIMSS at Virginia Tech Catching Crumbling Infrastructure: Sensor Technology Provides New Opportunity Adaptive Intelligent Materials and Systems (AIMS) Center, Arizona State University, Tempe, USA Drexel Institute for Sustainable Infrastructures, Drexel University PRODDIA - Structural Systems Health Management tool SURFLAND Systemy Komputerowe SA - Structural Health Monitoring Systems, Poland, lang. PL Osmos integrated safety for structures. Iran Society for Structural Health Monitoring of Intelligent Infrastructure (IRAN - SHMII) International Society for Structural Health Monitoring of Intelligent Infrastructure (ISHMII) Laboratory for the Concrete Technology and Structural Behaviour (LABEST) Journals

SHM Proceedings (NDT.net) Journal of Structural Health Monitoring (sagepub) Journal of Intelligent Material Systems & Structures (sagepub) Structural Durability & Health Monitoring (techscience) Structural Control and Health Monitoring (John Wiley & Sons, Ltd.) Journal of Civil Structural Health Monitoring (Springer) Smart Materials and Structures (IOP) Smart Materials Bulletin (science direct)