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Structural health monitoring
Encyclopedia
The process of implementing a damage detection and characterization strategy for engineering structures is referred to as Structural Health Monitoring (SHM). 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.
In the last ten to fifteen years, SHM technologies have emerged creating an exciting new field within various branches of engineering. Academic conference
s and scientific journal
s have been established during this time that specifically focus on SHM. These technologies are currently becoming increasingly common.
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.
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 loosely mounted and, hence, based on the judgment 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. Signal processing techniques such as filtering and re-sampling can also be thought of as data cleansing procedures.
Finally, the data acquisition, normalization, and cleansing portion of SHM process should not be static. Insight gained from the feature selection process and the statistical model development process will provide information regarding changes that can improve the data acquisition process.
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.
An example of this technology is embedding sensor
s in structures like bridge
s 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 centres. With the aid of modern technology, real time control of structures (Active Structural Control) based on the information of sensors is possible
, Ting Kau
, and Kap Shui Mun
bridges that run between Hong Kong
and the Hong Kong Airport.
In order to oversee the integrity, durability and reliability of the bridges, WASHMS has four different levels of operation: sensory system
s, data acquisition systems, local centralised 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 behaviour of the bridges is measured 24 hours a day, seven days a week.
The sensors include accelerometer
s, strain gauge
s, displacement transducers, level sensing stations, anemometer
s, 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 kilometres of cable
s 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 metres per second. In 1997, when Hong Kong had a direct hit from Typhoon Victor, wind speeds of 110 to 120 kilometres 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 metres per second, 284 kilometres 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.
by simultaneous measurement of loads on the bridge and
effects of these loads. It typically includes monitoring of:
Provided with this knowledge, the engineer can:
References are available that provide an introduction to the application of fiber optic sensors to Structural Health Monitoring on bridges.
Origins
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. In rotating machinery, vibration monitoring has been used for decades as a performance evaluation technique.In the last ten to fifteen years, SHM technologies have emerged creating an exciting new field within various branches of engineering. Academic conference
Academic conference
An academic conference or symposium is a conference for researchers to present and discuss their work. Together with academic or scientific journals, conferences provide an important channel for exchange of information between researchers.-Overview:Conferences are usually composed of various...
s and scientific journal
Scientific journal
In academic publishing, a scientific journal is a periodical publication intended to further the progress of science, usually by reporting new research. There are thousands of scientific journals in publication, and many more have been published at various points in the past...
s have been established during this time that specifically focus on SHM. These technologies are currently becoming increasingly common.
Statistical Pattern Recognition Paradigm Approach
The SHM problem can be addressed in the context of a statistical pattern recognition paradigm. 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.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 loosely mounted and, hence, based on the judgment 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. Signal processing techniques such as filtering and re-sampling can also be thought of as data cleansing procedures.
Finally, the data acquisition, normalization, and cleansing portion of SHM process should not be static. Insight gained from the feature selection process and the statistical model development process will provide information regarding changes 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.The Fundamental Axioms of SHM
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. 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 IVa: Sensors cannot measure damage. Feature extraction through signal processing and statistical classification is necessary to convert sensor data into damage information;
- Axiom IVb: 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.
SHM Components
SHM System's elements include:- StructureStructureStructure is a fundamental, tangible or intangible notion referring to the recognition, observation, nature, and permanence of patterns and relationships of entities. This notion may itself be an object, such as a built structure, or an attribute, such as the structure of society...
- SensorSensorA sensor is a device that measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument. For example, a mercury-in-glass thermometer converts the measured temperature into expansion and contraction of a liquid which can be read on a calibrated...
s - Data acquisitionData acquisitionData acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems typically convert analog waveforms into digital values for processing...
systems - Data transfer and storage mechanism
- Data managementData managementData management comprises all the disciplines related to managing data as a valuable resource.- Overview :The official definition provided by DAMA International, the professional organization for those in the data management profession, is: "Data Resource Management is the development and execution...
- Data interpretation and diagnosis:
- 1) System Identification
- 2) Structural model update
- 3) Structural condition assessment
- 4) Prediction of remaining service lifeService lifeA product's service life is its expected lifetime, or the acceptable period of use in service. It is the time that any manufactured item can be expected to be 'serviceable' or supported by its manufacturer....
An example of this technology is embedding sensor
Sensor
A sensor is a device that measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument. For example, a mercury-in-glass thermometer converts the measured temperature into expansion and contraction of a liquid which can be read on a calibrated...
s in structures like bridge
Bridge
A bridge is a structure built to span physical obstacles such as a body of water, valley, or road, for the purpose of providing passage over the obstacle...
s and aircraft
Aircraft
An aircraft is a vehicle that is able to fly by gaining support from the air, or, in general, the atmosphere of a planet. An aircraft counters the force of gravity by using either static lift or by using the dynamic lift of an airfoil, or in a few cases the downward thrust from jet engines.Although...
. These sensors provide real time monitoring of various structural changes like stress
Stress (physics)
In continuum mechanics, stress is a measure of the internal forces acting within a deformable body. Quantitatively, it is a measure of the average force per unit area of a surface within the body on which internal forces act. These internal forces are a reaction to external forces applied on the body...
and strain
Strain (materials science)
In continuum mechanics, the infinitesimal strain theory, sometimes called small deformation theory, small displacement theory, or small displacement-gradient theory, deals with infinitesimal deformations of a continuum body...
. In the case of civil engineering structures, the data provided by the sensors is usually transmitted to a remote data acquisition centres. With the aid of modern technology, real time control of structures (Active Structural Control) based on the information of sensors is possible
Wind and Structural Health Monitoring System for 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 MaTsing Ma Bridge
The Tsing Ma Bridge is a bridge in Hong Kong. It is the world's seventh-longest span suspension bridge, and was the second longest at time of completion. The bridge was named after two of the islands at its ends, namely Tsing Yi and Ma Wan . It has two decks and carries both road and rail...
, Ting Kau
Ting Kau Bridge
Ting Kau Bridge is a 1,177-metre long cable-stayed bridge in Hong Kong that spans from the northwest of Tsing Yi Island and Tuen Mun Road. It is adjacent to Tsing Ma Bridge which also serves as major connector between the Hong Kong International Airport on Lantau Island and the rest of Hong Kong....
, and Kap Shui Mun
Kap Shui Mun Bridge
The Kap Shui Mun Bridge in Hong Kong is one of the longest cable-stayed bridges in the world that transports both road and railway traffic, with the upper deck for motor vehicles, and the lower deck for both vehicles and the MTR. It has a main span of 430 metres and an overall length of 750 metres...
bridges that run between Hong Kong
Hong Kong
Hong Kong is one of two Special Administrative Regions of the People's Republic of China , the other being Macau. A city-state situated on China's south coast and enclosed by the Pearl River Delta and South China Sea, it is renowned for its expansive skyline and deep natural harbour...
and the Hong Kong Airport.
In order to oversee the integrity, durability and reliability of the bridges, WASHMS has four different levels of operation: sensory system
Sensory system
A sensory system is a part of the nervous system responsible for processing sensory information. A sensory system consists of sensory receptors, neural pathways, and parts of the brain involved in sensory perception. Commonly recognized sensory systems are those for vision, hearing, somatic...
s, data acquisition systems, local centralised 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 behaviour of the bridges is measured 24 hours a day, seven days a week.
The sensors include accelerometer
Accelerometer
An accelerometer is a device that measures proper acceleration, also called the four-acceleration. This is not necessarily the same as the coordinate acceleration , but is rather the type of acceleration associated with the phenomenon of weight experienced by a test mass that resides in the frame...
s, strain gauge
Strain gauge
A strain gauge is a device used to measure the strain of an object. Invented by Edward E. Simmons and Arthur C. Ruge in 1938, the most common type of strain gauge consists of an insulating flexible backing which supports a metallic foil pattern. The gauge is attached to the object by a suitable...
s, displacement transducers, level sensing stations, anemometer
Anemometer
An anemometer is a device for measuring wind speed, and is a common weather station instrument. The term is derived from the Greek word anemos, meaning wind, and is used to describe any airspeed measurement instrument used in meteorology or aerodynamics...
s, temperature sensors and dynamic weight-in-motion sensors. They measure everything from tarmac
Tarmac
Tarmac is a type of road surface. Tarmac refers to a material patented by Edgar Purnell Hooley in 1901...
temperature and strains in structural members to wind speed and the deflection
Deflection (physics)
In physics deflection is the event where an object collides and bounces against a plane surface.In such collisions involving a sphere and a plane, the collision angle formed with the surface normal must equal the bounce angle , \alpha = \beta.Magnetic deflection refers to Lorentz forces acting...
and rotation
Rotation
A rotation is a circular movement of an object around a center of rotation. A three-dimensional object rotates always around an imaginary line called a rotation axis. If the axis is within the body, and passes through its center of mass the body is said to rotate upon itself, or spin. A rotation...
of the kilometres of cable
Cable
A cable is two or more wires running side by side and bonded, twisted or braided together to form a single assembly. In mechanics cables, otherwise known as wire ropes, are used for lifting, hauling and towing or conveying force through tension. In electrical engineering cables are used to carry...
s 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 metres per second. In 1997, when Hong Kong had a direct hit from Typhoon Victor, wind speeds of 110 to 120 kilometres 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 metres per second, 284 kilometres per hour.
The information from these hundreds of different sensors is transmitted to the data acquisition
Data acquisition
Data acquisition is the process of sampling signals that measure real world physical conditions and converting the resulting samples into digital numeric values that can be manipulated by a computer. Data acquisition systems typically convert analog waveforms into digital values for processing...
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
Tsing Yi
Tsing Yi , or Tsing Yi Island is an island in the urban area of Hong Kong, to the northwest of Hong Kong Island and south of Tsuen Wan. With an area of 10.69 km², the island has extended drastically by reclamation along almost all its natural shore and the annexation of Nga Ying Chau and Chau...
. The local central computer system provides data collection control, post-processing
Post-processing
Post-processing may refer to:* Differential GPS post-processing* Video post-processing, methods used in video processing and 3D graphics* Finite element model data 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.
Other large examples
The following project 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 ViaducMillau ViaductThe Millau Viaduct is a cable-stayed road-bridge that spans the valley of the river Tarn near Millau in southern France. Designed by the British architect Norman Foster and French structural engineer Michel Virlogeux, it is the tallest bridge in the world, with one mast's summit at . It is the...
, France: has one of the largest systems with fiber optics in the world which is considered state of the art. - The Huey P Long bridgeHuey P. Long Bridge (Jefferson Parish)The Huey P. Long Bridge in Jefferson Parish, Louisiana, is a cantilevered steel through truss bridge that carries a two-track railroad line over the Mississippi River at mile 106.1 with two lanes of US 90 on each side of the central tracks....
, 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.
Structural Health Monitoring for bridges
Health monitoring of large bridges shall be performedby 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
- Forecast the probable evolution of the bridge
References are available that provide an introduction to the application of fiber optic sensors to Structural Health Monitoring on bridges.
See also
- Deformation MonitoringDeformation monitoringDeformation monitoring is the systematic measurement and tracking of the alteration in the shape or dimensions of an object as a result of stresses induced by applied loads...
- Automatic deformation monitoring systemAutomatic Deformation Monitoring SystemAn automatic deformation monitoring system is a group of interacting, interrelated, or interdependent software and hardware elements forming a complex whole for deformation monitoring that, once set up, does not require human input to function. Automatic deformation monitoring systems provide a...
- CivionicsCivionicsCivionics is the combination of civil engineering with electronics engineering, in a manner similar to avionics and mechatronics...
- SAA - ShapeAccelArray
Further reading
- B. Glisic and D. Inaudi (2008). Fibre Optic Methods for Structural Health Monitoring. Wiley. ISBN 978-0-470-06142-8
- Daniel Balageas,Claus-Peter Fritzen,Alfredo Güemes. Structural Health Monitoring. ISBN 1-905209-01-0. Link
- M. Mucciarelli, M. Bianca, R. Ditommaso, M.R. Gallipoli, A. Masi, C Milkereit, S. Parolai, M. Picozzi, M. Vona (2011). FAR FIELD DAMAGE ON RC BUILDINGS: THE CASE STUDY OF NAVELLI DURING THE L’AQUILA (ITALY) SEISMIC SEQUENCE, 2009. Bulletin of Earthquake Engineering. DOI: 10.1007/s10518-010-9201-y.
- M. Picozzi, S. Parolai, M. Mucciarelli, C. Milkereit, D. Bindi, R. Ditommaso, M. Vona, M.R. Gallipoli, and J. Zschau (2011). Interferometric Analysis of Strong Ground Motion for Structural Health Monitoring: The Example of the L’Aquila, Italy, Seismic Sequence of 2009. Bulletin of the Seismological Society of America, Vol. 101, No. 2, pp. 635–651, April 2011, DOI:10.1785/0120100070.
- M. Picozzi, S. Parolai, M. Mucciarelli, C. Milkereit, D. Bindi, R. Ditommaso, M. Vona, M.R. Gallipoli, and J. Zschau (2011). Interferometric analysis of strong ground motion for structural health monitoring: the example of the L’Aquila (Italy) seismic sequence, 2009. Bulletin of the Seismological Society of America, Vol. 101, No. 2, pp. –, April 2011, DOI: 10.1785/0120100070.
- 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/s10518-010-9194-6. Volume 8, Number 6, Pages 1421-1434.
- Ditommaso, R., Parolai, S., Mucciarelli, M., Eggert, S., Sobiesiak, M. and Zschau, J. (2010). Monitoring the response and the back-radiated energy of a building subjected to ambient vibration and impulsive action: the Falkenhof Tower (Potsdam, Germany). Bulletin of Earthquake Engineering. Volume 8, Number 3. DOI: 10.1007/s10518-009-9151-4.
- M. Picozzi, C. Milkereit, C. Zulfikar, K. Fleming, R. Ditommaso, M. Erdik, J. Zschau, J. Fischer, E. Safak, O. Özel, N. Apaydin (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.
- Rocco Ditommaso, Marco Vona, Marco Mucciarelli, Angelo Masi (2010). Identification of building rotational modes using an ambient vibration technique. 14th European Conference on Earthquake Engineering. Proceedings Volume. Ohrid, Republic of Macedonia. August 30 – September 03, 2010.
- Rocco Ditommaso, Marco Mucciarelli, Felice C. Ponzo (2010). S-Transform based filter applied to the analysis of non-linear dynamic behaviour of soil and buildings. 14th European Conference on Earthquake Engineering. Proceedings Volume. Ohrid, Republic of Macedonia. August 30 – September 03, 2010. (http://roccoditommaso.xoom.it).
- F.C. Ponzo, G. Auletta, R. Ditommaso & A. Mossucca (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 03, 2010.
- Mohanty, S., Chattopadhyay, A., Wei, J and Peralta, P., "Real time Damage State Estimation and Condition Based Residual Useful Life Estimation of a Metallic Specimen under Biaxial Loading", 2009, Structural Durability & Health Monitoring Journal, vol.5, no.1, pp.33-55.
- Mohanty, S., Chattopadhyay, A., Wei, J and Peralta, P., "Unsupervised Time-Series Damage State Estimation of Complex Structure Using Ultrasound Broadband Based Active Sensing", 2010, Structural Durability & Health Monitoring Journal, vol.130, no.1, pp.101-124
- Liu, Y., Mohanty, S., Chattopadhyay, A., "Condition Based Structural Health Monitoring and Prognosis of Composite Structures under Uniaxial and Biaxial Loading, 2010, Journal of Nondestructive Evaluation, Volume 29, Number 3, 181-188
External links
- Nano-Engineering and Smart Structures Technologies (NESST) Laboratory, University of California, Davis
- Metis Design Corporation, Digital Structural Health Monitoring Solutions
- Acellent Technologies, Inc, a Structural Health Monitoring Company
- University of Siegen Germany
- Critical Materials - Materials with Intelligence
- Laboratory for Intelligent Structural Technology, University of Michigan
- Engineering Institute, Los Alamos National Laboratory
- Centre for Non-Destructive Evaluation IIT Madras,India
- CIMSS at Virginia Tech
- Catching Crumbling Infrastructure: Sensor Technology Provides New Opportunity
- MatLab tools for Earthquake Engineering Applications
- 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
Journals of Structural Health Monitoring
- 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.)
- Smart Materials and Structures (IOP)
- Smart Materials Bulletin (science direct)