Structural Health Monitoring

Abstract:

Typically structure is suited design erected with components such as roofs, slabs, beams, columns and foundation. These structures damage due to exposure conditions like temperature, ill-management during construction and lack of quality of control during construction. Damage to structure may be defined as changes to the material or geometric properties of a structural system, including changes to the boundary conditions and system connectivity, which can adversely affect the system’s performance. In SHM process we observe system over time with the help of periodically sampled dynamic response measurements from an array of sensors, then extract damage-sensitive features, and finally statistical analysis is done to determine the current state of system health. 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.

 

                           

Introduction:

The process of implementing a damage detection and characterization strategy for engineering structures is referred to as Structural Health Monitoring (SHM).

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 conferences and scientific journals have been established during this time that specifically focuses on SHM. These technologies are currently becoming increasingly common.         

Paradigm approach in SHM:

The paradigm approach of an SHM is mainly divided in to four parts namely:

Ø  Operational Evaluation,

Ø  Data Acquisition and Cleansing,

Ø  Feature Extraction and Data Compression, and

Ø  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. 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:

Ø  What are the life-safety and/or economic justification for performing the SHM?

Ø  How is damage defined for the system being investigated and, for multiple damage possibilities, which cases are of the most concern?

Ø  What are the conditions, both operational and environmental, under which the system to be monitored functions?

Ø  What are the limitations on acquiring data in the operational environment?

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.

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.

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.

In total,

Operation evaluation gives the conditions of SHM,

Data Acquisition gives the number and types of sensors to be introduced in buildings,

Feature extraction gives the technical literature to distinguish between damaged and non damaged items of buildings,

Statistical Model Development is used for determining damaged and undamaged structures.

Principles 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 Principles, or general principles, have emerged.

·         Principle I: All materials have inherent laws or defects;

·         Principle II: The assessment of damage requires a comparison between two system states;

·         Principle 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;

·         Principle IV (a): Sensors cannot measure damage. Feature extraction through signal processing and statistical classification is necessary to convert sensor data into damage information;

·         Principle IV (b): Without intelligent feature extraction, the more sensitive a measurement is to damage, the more sensitive it is to changing operational and environmental conditions;

·         Principle V: The length- and time-scales associated with damage initiation and evolutions dictate the required properties of the SHM sensing system;

·         Principle VI: There is a trade-off between the sensitivity to damage of an algorithm and its noise rejection capability;

·         Principle VII: The size of damage that can be detected from changes in system dynamics is inversely proportional to the frequency range of excitation.

So far, we have known about SHM.

Let us know about it in a deep manner something about Components of SHM.

Components of SHM:

v  Structure

v  Sensors

v  Data acquisition systems

v  Data management

v  Data transfer

v  Data interpretation and diagnosis.

Data Interpretation and Diagnosis systems consist of:

1. System Identification,
2. Structural model update,
3. Structural condition assessment,
4. Prediction of remaining service life.

Sensors:

Sensors are a device that measures a physical quantity and converts it in to a signal that can be measured by an instrument or by an observer. A sensor is a device which receives and responds to a signal. A good sensor obeys the following rules:

• Is sensitive to the measured property
• Is insensitive to any other property likely to be encountered in its application
• Does not influence the measured property.

Data Acquisition Systems:

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.

This includes:

• Sensors that convert physical parameters to electrical signals.
• Signal conditioning circuitry to convert sensor signals into a form that can be converted to digital values.
• Analog-to-digital converters, which convert conditioned sensor signals to digital values.

Data acquisition begins with the physical phenomenon or physical property to be measured. Examples of this include temperature, light intensity, gas pressure, fluid flow, and force.

Data management:

Data management comprises all the disciplines related to managing data as a valuable resource. 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 of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise."

Data transfer systems are used to transfer the data to systems which help in predicting the failures of structures.

Structure

Conceptually, an accelerometer behaves as a damped mass on a spring. When the accelerometer experiences acceleration, the mass is displaced to the point that the spring is able to accelerate the mass at the same rate as the casing. The displacement is then measured to give the acceleration.

In commercial devices, piezoelectric, piezoresistive and capacitive components are commonly used to convert the mechanical motion into an electrical signal. Piezoelectric accelerometers rely on piezoceramics (e.g. lead zirconate titanate) or single crystals (e.g. quartz, tourmaline). They are unmatched in terms of their upper frequency range, low packaged weight and high temperature range. Piezoresistive accelerometers are preferred in high shock applications. Capacitive accelerometers typically use a silicon micro-machined sensing element. Their performance is superior in the low frequency range and they can be operated in servo mode to achieve high stability and linearity.

Modern accelerometers are often small micro electro-mechanical systems (MEMS), and are indeed the simplest MEMS devices possible, consisting of little more than a cantilever beam with a proof mass (also known as seismic mass). Damping results from the residual gas sealed in the device. As long as the Q-factor is not too low, damping does not result in a lower sensitivity.

Under the influence of external accelerations the proof mass deflects from its neutral position. This deflection is measured in an analog or digital manner. Most commonly, the capacitance between a set of fixed beams and a set of beams attached to the proof mass is measured. This method is simple, reliable, and inexpensive. Integrating piezoresistors in the springs to detect spring deformation, and thus deflection, is a good alternative, although a few more process steps are needed during the fabrication sequence. For very high sensitivities quantum tunneling is also used; this requires a dedicated process making it very expensive. Optical measurement has been demonstrated on laboratory scale.

Another, far less common, type of MEMS-based accelerometer contains a small heater at the bottom of a very small dome, which heats the air inside the dome to cause it to rise. A thermocouple on the dome determines where the heated air reaches the dome and the deflection off the center is a measure of the acceleration applied to the sensor.

Most micromechanical accelerometers operate in-plane, that is, they are designed to be sensitive only to a direction in the plane of the die. By integrating two devices perpendicularly on a single die a two-axis accelerometer can be made. By adding an additional out-of-plane device three axes can be measured. Such a combination always has a much lower misalignment error than three discrete models combined after packaging.

Micromechanical accelerometers are available in a wide variety of measuring ranges, reaching up to thousands of g's. The designer must make a compromise between sensitivity and the maximum acceleration that can be measured.

Building and structural monitoring

Accelerometers are used to measure the motion and vibration of a structure that is exposed to dynamic loads.^[22] Dynamic loads originate from a variety of sources including:

• Human activities - walking, running, dancing or skipping
• Working machines - inside a building or in the surrounding area
• Construction work - driving piles, demolition, drilling and excavating
• Moving loads on bridges
• Vehicle collisions
• Impact loads - falling debris
• Concussion loads - internal and external explosions
• Collapse of structural elements
• Wind loads and wind gusts
• Air blast pressure
• Loss of support because of ground failure
• Earthquakes and aftershocks

Measuring and recording how a structure responds to these inputs is critical for assessing the safety and viability of a structure. This type of monitoring is called Dynamic Monitoring.

WIRELESS MONITORING TECHNIQUES BASED ON MEMS

Existing monitoring systems use traditional wired sensor technologies and several other devices that are time consuming to install and relatively expensive (compared to the value of the structure). They are using large number of sensors (i. e. more than ten) are expensive and will therefore be installed only on a few bridges. A wireless monitoring system with MEMS (Micro-Electro-Mechanical-Systems) sensors could reduce these costs significantly. MEMS are small integrated devices or systems that combine electrical and mechanical components that could be produced for less than 50 € each. The principle of such a system is shown in the scheme given below.

Currently, a wireless sensor node with such a MEMS sensor could be fabricated at a price varying from 100 to about 400 € and future developments show the potential for prices of only a few Euro. Monitoring systems equipped with MEMS sensors and wireless communication can reduce the costs to a small percentage of a conventional monitoring system and therefore will increase its application not only in monitoring bridges. Due to the detailed information of the structural behavior of bridges obtained from the monitoring system, maintenance costs could also be reduced, since inspection methods (addressed i.e. in the following chapter) can be applied more efficiently. Only after certain changes in the structural behavior have been identified, an inspection (either by means of non-destructive testing or visual methods) is necessary and proper repair could be done right after the occurrence of the defect. This reduces the risk of further damage.

 The analysis of measured data and the knowledge of continuous changes of structural behavior will also improve the life time prognosis of civil structures reducing the overall maintenance costs of buildings and transport networks. Data has to be continuously transmitted (e.g. using the internet) to the supervisor. Each sensor device (mote), which is itself a complete, small measurement and communication system, has to be power and cost optimized. Using multi-hop techniques, the data of the sensor network has to be transmitted over short distances of some 10 m to a base station on site. There the data items are collected and stored in a data base for subsequent analysis. This data can then be accessed by a remote user. If the central unit detects a hazardous condition by analyzing the data, it has to raise an alarm message. The central unit also allows for wireless administration, calibration and reprogramming of the sensor nodes in order to keep the whole system flexible. Each mote is composed of one or more sensors, a data acquisition and processing unit, a wireless transceiver and a battery as power supply (Fig. 2, right) [3, 4]. The acquisition and processing unit usually is equipped with a low power microcontroller offering an integrated analogue to digital converter (ADC) and sufficient data memory (RAM) to store the measurements. This unit also incorporates signal conditioning circuitry interfacing the sensors to the ADC. In the following sections, some components are mentioned, but a more detailed description is given elsewhere.

A typical example of hybrid sensor system for wireless MEMS and DMS sensor data.

A diagram showing sensors in structures.

An example of Micro machined Silicon sensor.

An example showing monitoring of dams.

An example showing sensors in beams.

It is a typical example showing electrical generator and a sensor for health monitoring of systems.

           An example of sensor based health monitoring of structures.

                      A type of forest based sensor for trees.

                         An example of dam’s health in China.

        A perfect Silicon Sensor for Structural Health Monitoring.

Conclusion:

The inspection of building structures and especially of bridges is mainly done visually nowadays. Therefore, the condition of the structure is examined from the surface and the interpretation and assessment is based on the level of experience of the engineers. An approach to continuous structural health monitoring techniques based on wireless sensor networks were presented, which provide data from the inside of a structure to better understand its structural performance and to predict its durability and remaining life time. Using this technique, monitoring of large structures in civil engineering becomes very efficient. . Essential is that the new system provides a more reliable impact generation.