Karbhari / Ansari Structural Health Monitoring of Civil Infrastructure Systems

1. Auflage 2009
ISBN: 978-1-84569-682-5
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 552 Seiten

Reihe: Woodhead Publishing Series in Civil and Structural Engineering

Structural health monitoring is an extremely important methodology in evaluating the 'health' of a structure by assessing the level of deterioration and remaining service life of civil infrastructure systems. This book reviews key developments in research, technologies and applications in this area of civil engineering. It discusses ways of obtaining and analysing data, sensor technologies and methods of sensing changes in structural performance characteristics. It also discusses data transmission and the application of both individual technologies and entire systems to bridges and buildings.With its distinguished editors and international team of contributors, Structural health monitoring of civil infrastructure systems is a valuable reference for students in civil and structural engineering programs as well as those studying sensors, data analysis and transmission at universities. It will also be an important source for practicing civil engineers and designers, engineers and researchers developing sensors, network systems and methods of data transmission and analysis, policy makers, inspectors and those responsible for the safety and service life of civil infrastructure. - Reviews key developments in research, technologies and applications - Discusses systems used to obtain and analyse data and sensor technologies - Assesses methods of sensing changes in structural performance

Autoren/Hrsg.

Karbhari, Vistasp M.

Ansari, Farhad

Leseproben

Introduction: structural health monitoring – a means to optimal design in the future
V.M. Karbhari, University of Alabama in Huntsville, USA While interest in structural health monitoring (SHM) has increased as related to both research and implementation, its basis and motivation can be traced to the very earliest endeavors of mankind to conceptualize, construct, worry about deterioration, and then attempt to repair (or otherwise prolong the life) of a structure. This is largely in response to the fact that over time all structures deteriorate and it is essential that the owner/operator has a good idea as to the extent of deterioration, its effect of remaining service-life and capacity, and has sufficient information to make a well-informed decision regarding optimality of repair. Thus it represents an attempt at deriving knowledge about the actual condition of a structure, or system, with the aim of not just knowing that its performance may have deteriorated, but rather to be able to assess remaining performance levels and life. This ability will, at some point in the near future, enable those associated with the operation of civil infrastructure systems to handle both the growing inventory of deteriorating and deficient systems and the need for the development of design methods that inherently prescribe risk to a system based on usage and hence differentiate between systems based on frequency of use and type of operating environment. Further such a system would enable decisions related to resource allocation to be made on a real time basis rather than years ahead thereby allowing for maintenance plans to be based on actual state of a structure and need rather than a time-based schedule. This would allow for real-time resource allocation thereby enabling a more optimal approach to maintenance and replacement of structural inventory. In its various forms, over the years, SHM has been represented as the process of conventional inspection, inspection through a combination of data acquisition and damage assessment, and more recently as the embodiment of an approach enabling a combination of non-destructive testing and structural characterization to detect changes in structural response. It has also often been considered as a complementary technology to systems identification and non-destructive damage detection methods. A decade ago Housner et al. (1997) defined it as “the use of in-situ, non-destructive sensing and analysis of structural characteristics, including the structural response, for detecting changes that may indicate damage or degradation.” While this definition provides a basis for the development of a good data management system in that it enables the collection of incremental indicators of change, it falls short of the goal of considering the effect of deterioration on performance and thence on the estimation of remaining service life. These management systems thus focus on processing collected data, but are unable to measure or evaluate the rate of structural deterioration, and more importantly from an owner’s perspective, are unable to predict remaining service life and level of available functionality (e.g. the load levels that the structure can be subjected to within the pre-determined level of reliability and safety). In essence a true system should be capable of determining and evaluating the serviceability of the structure, the reliability of the structure, and the remaining functionality of the structure in terms of durability. This functionality has an analogy to the health management system used for humans wherein the patient undergoes a sequence of periodic physical examination, preventive intervention, surgery, and recovery (Aktan et al., 2000). Thus one would expect that a health monitoring system not only provides an indication of “illness” but also enables an assessment of its cause and extent, as well as the effect of that illness. Intrinsically owners and operators of modern civil engineering systems need the knowledge of the integrity and reliability of the network, structural system, and/or components in real time such that they cannot only evaluate the state of the structure but also assess when preventive actions are needed to be taken. This would allow them to take timely decisions on whether functionality has been impaired to a point as a result of an event (or series of events) where the structure had to be shut down to prevent accidents, or whether it could remain open with a pre-specified level of reliability. Thus, what is needed is an efficient method to collect data from a structure in-service and process the data to evaluate key performance measures such as serviceability, reliability and durability. In the context of civil structures, the definition by Housner et al. (1997) is modified and structural health monitoring is defined as “the use of in-situ, nondestructive sensing and analysis of structural characteristics, including the structural response, for the purpose of estimating the severity of damage/deterioration and evaluating the consequences thereof on the structure in terms of response, capacity, and service-life” (Karbhari, 2005). Essentially, a SHM system then must have the ability to collect, validate, and make accessible, operational data on the basis of which decisions related to service-life management can be made. While this task may not have been possible a decade ago, the recent progress in sensor technology, methods of damage identification and characterization, computationally efficient methods of analysis, and data communication, analysis and interrogation, have made it possible for one to consider SHM as a tool not just for operation and maintenance but also for the eventual development of a true reliability and risk based methodology for design. The advancements in forms of energy harvesting and storage, in addition to the integration of types of distributed sensor networks have also enabled rapid progress in this area, moving the concept from research to field implementation. SHM intrinsically includes the four operations of acquisition, validation, analysis, prognosis, and management and thus a SHM system inherently consists of the five aspects of: 1. sensors and sensing technology, 2. diagnostic signal generation, 3. signal transmission and processing, 4. event identification and interpretation, and 5. integration into an operative system for systems life management. It must be emphasized that although a number of non-destructive evaluation (NDE) techniques are incorporated in the overall methodology of SHM of structures there is a distinct difference between NDE and SHM. NDE is dependent on the measurement of specific characteristics and provides an assessment of state at a single point in time without necessarily enabling the assessment of the effect or extent of deterioration, whereas SHM requires the diagnosis and prognosis (interpretation) of events and sequences of events with respect to parameters such as capacity and remaining service-life. It is this aspect that also differentiates SHM from mere monitoring of use (usage monitoring). Usage monitoring is now fairly common and consists of the acquisition of data from a system related to response to external and internal excitations. SHM, in contrast, also includes the interrogation of this data to quantify the change in state of the system and thence the prognosis of aspects such as capacity and remaining service-life. The former is hence a necessary, but not sufficient, part of the latter. Also, the concept of monitoring prescribes that it be an ongoing, preferably autonomous, process rather than one that is used at preset intervals of time through human intervention. Thus SHM is essentially the basis for condition-based, rather than time-based, monitoring and the system should be integrated into the use of real time data on ageing and degradation into the assessment of structural integrity and reliability. Unfortunately, typical systems do not use an integrated approach to the design, implementation, and operation, of the SHM system, resulting in the benefits of the system often not being realized. Too often, a disproportionate emphasis is placed on the collection of data rather than on the management of this data and the use of decision-making tools that would support the ultimate aim of using the collected data to effect better management of the infrastructure system. Typically, systems collect data on a continuous or periodic basis and transmit the data to a common point. The data is then compared with results from a numerical model that simulates the original structure. The weakness is that most systems do not attempt to update the model to reflect ageing and deterioration, or changes made through routine maintenance or even rehabilitation. Thus while it is easy to note that a change has taken place and even that a predetermined performance threshold has been reached, the approach does not enable prediction of future response, nor the identification of “hot spots” that may need to be further monitored. While some systems incorporate a systems identification or non-destructive damage evaluation algorithm to rapidly process the data, these are the exception, not the rule. Even here, there is a gap between the management of this data, and its use towards the ultimate goals of estimating capacity and service life (Sikorsky and Karbhari, 2003). A review of a large number of SHM projects has enabled...