Key words

sensors; sensing systems; civil engineering; infrastructure; structural health monitoring; asset management; damage detection

1.1 Introduction

Infrastructure systems including bridges, roads, pipelines, dams, and power grids are vital resources that provide basic services to society. Given the vital role infrastructure plays in providing society with a high quality-of-life, monitoring these assets using sensors is an important step towards verifying design assumptions, tracking structural performance over a structure’s life, and rapidly identifying unsafe structural conditions originating from damage and deterioration. The past half century has witnessed an explosive growth in the availability of powerful new sensors that can be adopted to monitor the behavior, performance, and health of large-scale, operational civil infrastructure systems. This growth has been fueled in part by industry and government. For example, government-funded research programs have been initiated and sustained in academic settings to scientifically advance sensing technologies in the laboratory. Concurrently, industry has made strategic investments aimed at translating promising laboratory-based technologies into reliable, field-ready sensing components and systems. A direct beneficiary of these long-term investments has clearly been the civil engineering field, which today is at the cutting-edge in terms of the advancement and deployment of sensing systems tailored for monitoring complex and massive structural systems. Adoption of sensing systems to monitor operational civil infrastructure systems has undergone three major phases over the past 50 years: (i) sensors were first used in special case studies centered on assessing structures with revolutionary design concepts; (ii) sensors were later used more widely to monitor structures exposed to extreme loads to determine structural performance and the limits of performance under such harsh load conditions; and (iii) today, interest in sensing systems has grown in response to the need for sensed data to assess the structural health of a system monitored.

Early examples of permanent structural monitoring date as far back as the 1940s. An early example of a monitoring system installed on an operational civil engineering structure was the installation of ten Hall accelerometers on the Golden Gate Bridge between 1945 and 1946 to monitor vertical deck accelerations under wind excitation (Vincent, 1958; Abdel-Ghaffar and Scanlan, 1985). Hall accelerometers were large single degree-of-freedom mass systems that wrote their measurements to paper on a rotating drum. The installation of the Hall accelerometers on the Golden Gate Bridge was optimized to illuminate the behavior of the long, slender bridge structure under strong wind loading. Installation of the system was in response to the dramatically catastrophic failure of the Tacoma Narrows Bridge that same year. This monitoring system was kept in continuous operation until 1954 (Abdel-Ghaffar and Scanlan, 1985). The decision to install permanent monitoring systems, like that deployed on the Golden Gate Bridge, were extremely rare and typically reserved for ‘special’ structures with unique designs that required verification of design assumptions.

By the early 1960s, digital data acquisition systems were beginning to emerge with the advent of the personal computer. For example, IBM introduced the IBM 1710 personal computer and IBM 7700 data acquisition system in 1961 and 1963, respectively (Border, 1990). Based on analog interfaces, digital data acquisition systems rapidly matured for both laboratory and field use over the three decades following the introduction of the first IBM data acquisition systems. Specifically, improved functionality (e.g., higher digitalization resolutions, higher sample rates, more channels) and hardware ruggedization all allowed digital data acquisition systems to be deployed to the field for reliable operation in civil infrastructure systems. Due to the availability of digital data acquisition systems, civil engineers began to deploy monitoring systems more widely to assess the response of infrastructure systems during extreme load events (e.g., earthquakes, hurricanes) in order to determine if structures were operating in a manner that placed the public at risk. Unfortunately, the high costs of the systems only justified their use in critical structures operating in extreme load environments. Examples include monitoring systems in bridges (Peeters and DeRoeck, 2001; Wong, 2004; Ko and Ni, 2005; Brownjohn et al., 2010) and buildings (Celebi, 2000; Nayeri et al., 2008) exposed to seismic and wind loads, and offshore oil platforms (Tidewell and Ilfrey, 1969) exposed to extreme sea states and subject to fatigue. The seismic monitoring of civil structures has been a particularly strong driver of monitoring system adoption. For example, in the United States, 60 bridges, 170 buildings, and 20 dams have been instrumented since 1972 through the California Strong Motion Instrumentation Program (CSMIP, 2013). Other strong ground motion instrumentation programs in Japan, Taiwan, China, and Europe have led to similar instrumentation efforts in their respective nations and regions.

Beginning with the 1970s, the structural engineering field sought to do more with sensors and sensing systems deployed to the field. Specifically, attention was turned to expanding the role of monitoring systems to track the behavior of structural systems over their complete life-cycles so as to assess their long-term performance and health (i.e., structural health monitoring (SHM)). Two technological advances fueled strong interest in SHM. First, new classes of sensors began to emerge that offered new measurement modalities and more convenient means of data acquisition at lower cost. For example, microelectromechanical systems (MEMS) sensors were beginning to be marketed that miniaturized sensor packages while reducing sensor unit costs (Petersen, 1982; Liu, 2011). Other disruptive, new sensing technologies included fiber optic sensors (Ansari, 1993; Glisic and Inaudi, 2007), wireless sensors (Straser and Kiremidjian, 1998; Lynch, 2002; Spencer et al., 2004; Lynch and Loh, 2006), and piezoelectric surface sensors (Raghavan and Cesnik, 2007), among others. Second, data management and health-based data interrogation methods began to emerge that allowed measurement data to be analyzed to infer structural conditions (i.e., assess if a structure is damaged or deteriorated) (Sohn and Oh, 2010). As early as the 1970s, the offshore oil and aerospace industries began to study the use of vibration-based damage assessment methods to assess the health of offshore platforms and aerospace systems, respectively. In the 1980s, the civil infrastructure community started to use vibration-based approaches as monitoring tools for bridges and buildings (Salane et al., 1981; Salawu, 1994; Doebling et al., 1996; Sohn et al., 2004). Modal properties and global quantities derived from these characteristics, such as mode shape curvature and dynamic flexibility, have been the primary features used to identify the existence of damage in structures. To enhance the sensitivity of damage detection methods to identify the location, type and severity of structural damage, nondestructive detection techniques (NDT) such as impact echo, magnetic particles, ultrasonic inspection, acoustic emission, thermography, and ground penetration radar, to name just a few, have been considered for use in SHM systems (Chong et al., 1994, 2003; Chong and Liu, 2003). While data interrogation methods for damage detection are still in their early stages of development, great strides have been made in recent years making automated SHM a potentially viable technology for asset management of civil infrastructure systems.

Today, the civil infrastructure field is fortunate to have deployed a large number of sensing systems to operational civil engineering structures. Prior to 2000, the monitoring systems deployed had largely been commercial monitoring systems installed by owners to either improve their asset management methods (as was the case for offshore structures, where owners sought means of managing fatigue accumulation in their structures) or to be compliant to local regulations (as was the case for building owners in California, who were required to install seismic monitoring systems in large structures by the state building code). Academic researchers also play increasingly greater roles in the deployment of monitoring systems in operational civil engineering structures with many systems now including experimental, non-commercial components. Specifically, many researchers engaged in the technological development of sensors and sensing systems to view full-scale, operational structures as the ultimate test of their technologies. For example, the reliability of wireless monitoring systems must be tested at full-scale in the field because environmental variations in the wireless communication channel cannot be accurately simulated in a laboratory setting. Academia–industry collaborations have also impressively led to prolific adoption of structural monitoring systems on newly constructed critical infrastructure systems including long-span bridges. This is especially true in developing countries, such as China where more than 70 newly constructed bridges have been instrumented with dense networks of sensors and sophisticated data acquisition systems since 2000.

1.2 Introduction to this book

The objective of this book is to introduce and showcase the many sensors, sensing systems (i.e., monitoring systems), and data processing techniques recently proposed and validated in real operational civil infrastructure systems. These systems have as their primary goal the assessment of the performance, operation, and physical health of a monitored structure. Since the authors’ interest and expertise lie in civil engineering, our discussion mainly focuses on infrastructure systems such as high-rise buildings, signature bridges, wind turbines, nuclear power plants, electrical distribution systems, pipelines, dams, roads, communication networks, and railroad systems, among others. We have selected three major areas that will serve as focal points for this two volume book: Volume 1 Sensing hardware and data collection methods for performance assessment, Volume 2/Part I - Sensor data interrogation and decision making, and Volume 2/Part II - Case studies in assessing and monitoring specific structures.

In Volume 1, sensing hardware (i.e., sensors, data acquisition systems) is the primary focus. The sensors presented in this book have been used for performance assessment of civil infrastructure systems ranging from commonly used sensors (e.g., accelerometers, strain gages) to new wireless, noncontact, robotic sensors. Furthermore, sensing data acquisition systems and energy harvesting issues are also discussed, given their important relationship to sensors installed in operational infrastructure systems. Readers will find this section particularly useful, since several intriguing sensing techniques specific to civil engineering systems are introduced. In Part I of Volume 2, the book explores how data acquired from various sensors can be processed and transformed into valuable information that owners and asset managers can utilize to understand the characteristics of a structure, evaluate its condition, identify damage, and predict future performance. Key data interrogation and decision-making methods are presented, including statistical inference, data fusion methods, and decision analysis techniques. In Part II of Volume 2, case studies of the technologies presented in the earlier chapters are highlighted. This section includes successful examples of structural monitoring systems deployed in various civil engineering systems ranging from conventional buildings, bridges, tunnels, and dams to lifeline systems, roads, and offshore structures.

1.3 Overview of sensors and sensing system hardware

The fundamental building block of structural monitoring systems is the sensing transducer. The quality and completeness of the data set collected for a given structure largely depends upon the capabilities and quality of the transducers used to record structural responses. To date, a plethora of sensor types are available for installation in civil infrastructure systems. Traditional transducers widely used in bridge and building monitoring were largely macroscopic sensors: accelerometers (e.g., force-balance, piezoelectric, piezoresistive, and capacitive), linear variable displacement transducers, strain gages (e.g., metal foil, semiconductor), tilt meters, ground positioning system (GPS), anemometers, and geophones. Many of these sensing transducers have enjoyed decades of successful field deployments and have proven valuable for measuring static and dynamic structural responses to loading. The objective of Volume 1 is to review currently available sensing and data acquisition technologies that are being used to assess current structural conditions.

First, the basic operational principles of data acquisition system architectures used to collect measurement data are presented in Chapter 2 of Volume 1 by Todd. This chapter presents an overview of important concepts related to the process of getting (i.e., data acquisition) and processing of data (i.e., data reduction). It includes many potent subjects, such as time/ frequency domain signal analysis, probability, uncertainty analysis, analogto-digital (A/D) and digital-to-analog (D/A) conversions, sampling theory, multiplexing, filtering, and aspects of hardware design. With the basics of data acquisition introduced, Chapter 3 of Volume 1 by Yoder and Adams reviews and describes some commonly used traditional sensors (which are mature and often inexpensive sensors) for long-term monitoring of infrastructure systems. The measurements made using these sensors are well correlated to the physical characteristics of a structure or to its operating condition. This chapter also identifies commonly used algorithms for processing the data originating from these sensors.

In general, sensors designed for structural monitoring are ‘passive’ components; in other words, such sensors record the response of the system without acting directly on the structure itself. In contrast, ‘active’ sensors described in Chapter 4 of Volume 1 by An et al. introduce controlled excitations into the structure and measure the corresponding structural response. The advantage of using active sensors for SHM is that they can be used to introduce controllable and repeatable excitations. This chapter deals with the basic operational principles of piezoelectric transducers and the characteristics of piezoelectric materials. Several active sensing techniques used for defect detection such as guided waves, impedance spectroscopy, and acoustic emission are introduced.

In recent years, fiber-optic-based strain sensors, especially long-gage strain sensors and displacement sensors, have received much attention because of their high sensitivity and immunity to environmental factors such as electromagnetic interference and resistance to chemical attack. Chapter 5 of Volume 1 by Peters and Inaudi describes many different fiber optic sensor technologies, defines their ranges of performance, and presents their suitability for monitoring civil infrastructure systems.

Chapter 6 of Volume 1 by Meo provides an overview of acoustic emission techniques for fatigue detection in civil infrastructure systems. Basic principles are followed by techniques for acoustic source localization and defect quantification. Field application is introduced to validate its effectiveness. Chapter 7 by the same author goes further and presents nonlinear acoustic and ultrasonic techniques.

Chapter 8 of Volume 1 by Huston and Busuioc presents a comprehensive overview of radar technology to detect the subsurface condition of a structure. It includes probing subsurface conditions in concrete, detecting cracks and corrosion in concrete decks, assessing asphalt pavement depth along with its overall layer properties, and detecting delamination of reinforcement inside concrete and debonding between concrete and asphalt layers. An important feature of radar sensing is that disturbances in the magneto-elastic (EM) field serves as both a probe for measurement and a signal transmitter.

Despite the increasing popularity of cable-stayed bridges, accurate yet simple methods are still needed to directly measure stresses (forces) in stay cables. The measurement of cable force is important for monitoring excessive wind or traffic loadings, gaging the redistribution of forces present after seismic events and other natural disasters, and for detecting corrosion via loss of the cable cross section. Wang and Wang in Chapter 9 of Volume 1 present a state-of-the-art technique using the magneto-elastic (EM) sensor to measure stress or force in high-strength steel strands and cables. EM techniques are used to determine forces in all sizes of a prestressed steel cable or tendon. Applications include stress measurement of strands in prestressed concrete members during or after construction, and stress measurement in cable-stayed bridges and in hanger cables and anchorage strands for suspension bridges. Additional applications include monitoring of cable anchors for retaining walls and tunnels, as well as in monitoring cable-based support systems of dome structures.

Chapter 10 of Volume 1 by Ozevin discusses advances in MEMS as a sensing tool for monitoring of civil infrastructure systems. Sensors can now be miniaturized using the same operational principles as their traditional counterparts yet have circuitry for signal processing and computation included on the same silicon die. To date, the greatest success of MEMS has been in the design and fabrication of accelerometers (e.g. Analog Devices, MEMSIC, STMicro, VTI). MEMS-based transducers also have been produced to sense such parameters as relative humidity, temperature, pressure of all types, magnetic field (compass), tilt, twist (gyroscope), strain, corrosion, and gaseous compounds such as CO, NO. . methane, and sarin. Closely related to MEMS are nano-engineered systems. In Chapter 11 of Volume 1 by Loh and Ryu, multifunctional materials and nanocomposites are presented for use in sensing strain of structural elements. Multifunctional materials are designed to perform multiple functions such as sensing, actuation, energy harvesting, mechanical reinforcement, and energy dissipation. Technological breakthroughs allow a plethora of new materials to be created through controlled assembly of different molecular components.

Laser-based sensing for displacement field measurement is discussed in Chapter 12 of Volume 1 by Yu. This chapter provides an overview of current laser-based sensing techniques for engineering applications. It includes descriptions of laser interferometry or electronic speckle pattern interferometry (ESPI), digital shearography, scanning photogrammetry, and laser Doppler vibrometry (LDV).

Corrosion sensing is discussed in Chapter 13 of Volume 1 by Poursaee. Numerous sensor technologies exist for monitoring corrosion effects on steel. These technologies can be categorized as electrochemical or mechanical methods. Since corrosion of steel reinforcement is an electrochemical process, such sensors either measure electrical fields at the concrete surface or measure main corrosion factors such as chloride content and pH of pore fluids. In recent years, a number of researchers have begun to explore means of integrating wireless read-out mechanisms for corrosion sensors to make their use more attractive to bridge managers. Physical approaches include fiber optic corrosion sensors and magneto-elastic corrosion sensors. Additional technologies include the use of resistance probes, guided waves, among others.

Ji and Chang in Chapter 14 of Volume 1 describe vision-based measurement techniques for deformation monitoring. Results from the laboratory as well as from the field are obtained to demonstrate the accuracy and the potential for applications to infrastructure systems. Deformation measurement of large-scale infrastructure, including bridges, has always been a demanding task. However, deformation data can be used for design validation, performance monitoring, as well as for structural safety and integrity assessment.

Myung et al. in Chapter 15 of Volume 1 use robotic technology to remotely inspect and monitor structural systems. A prototype robotic system carrying various sensing technologies such as a camera, optical sensors, laser sensors, and wireless sensors, is used to inspect a structure and to detect defects remotely. There is a growing concern with the disruption of regular traffic due to human inspection processes; remote and robotic sensing can offer an alternative to this growing problem.

Comprehensive discussion on wireless sensing platforms for monitoring civil engineering systems is presented in Chapters 16 and 17 of Volume 1 by Kane et al. and Peckens et al, respectively. Traditionally, SHM is performed at a global level, with a limited number of sensors distributed over a relatively large area of a structure. Such sensing systems, with gross spatial resolution, can only detect major damage conditions. Wireless sensor networks offer engineers the opportunity to deploy dense networks of sensors at reduced cost. As discussed in Chapter 17 of Volume 1, wireless sensors have made an especially major impact in the monitoring of operational bridge structures.

Chapter 18 of Volume 1 by Scruggs gives an overview of some of the fundamental design issues arising in vibrating energy harvesting systems for sensing applications. It focuses primarily on piezoelectric and electromagnetic transducers. A number of basic electronic circuits for extracting power from harvesters are discussed, including the standard diode bridge rectifier, DC/DC converters, and synchronized switch harvesting on inductor (SSHI) circuits.

In conclusion, traditional sensors to measure strain, stress, acceleration, velocity, temperature, and displacement are reviewed in Volume 1. These sensors offer tremendous possibilities for the monitoring of various infrastructure system types subjected to external load conditions. Additional new technologies, such as fiber optic sensors and MEMS sensors, offer various possibilities of sensing and defect detection under severe environmental constraints. Acoustic and ultrasound sensors in the form of piezoelectric elements are also introduced to more aggressively assess current structural conditions. Electromagnetic sensors are introduced to measure the forces in steel cables and to detect the breakage of cables. Ground penetrating radar (GPR) is used to locate and determine the extent of corrosion of reinforcement steel or prestressing tendons buried inside concrete elements or covered by asphalt pavements. Robotic sensing, remote sensing, and wireless sensing platforms including energy harvesting are emerging as powerful new additions to the sensing arsenal of the civil engineer for long-term health monitoring of infrastructure.

1.4 Overview of sensor data interrogation and decision making

Structural monitoring and SHM systems deploy sensors to collect measurements of structural responses, assess the current state of a structure, and assist decision-makers with making informed and ideally optimal decisions. The second major section of the book (Volume 2/Part I) focuses on sensor data interrogation and decision-making technologies and is intended to serve as a linkage between the data collected from sensors (covered primarily in Volume 1) to decision making so that the information inferred from the measurement data can be utilized for structural health assessments, life-cycle assessment and management, and long-term resource allocation for infrastructure repair, rehabilitation and replacement, just to name a few. The objective of this section of the book is to cover, in detail, the current state-of-the-art in the management of sensor data including the interrogation of sensor data for decision making.

The first part of Volume 2 of this book (Chapters 1 through 6) is reserved for presenting the current data management methods associated with structural monitoring. Specifically, Chapter 1 of Volume 2 by Law et al. summarizes key data management issues including data collection and storage at the site, data communication, and transfer of data to off-site facilities for storage. In particular, the discussion focuses on: (i) data processing and management at wireless sensor nodes (sensor-level) with special attention on energy consumption reduction; (ii) in-network communication issues such as wireless communication range, robust communication protocol design; and (iii) system- and database-level persistent data management and retrieval by means of a wind turbine monitoring system as an illustrative example.

Chapter 2 of Volume 2 by Zonta presents Bayesian logic as a mathematical framework to formulate the inference problem, in attempting to gain information on the target structure based on sensor readings, particularly accounting for data and model uncertainties. Next, since monitoring systems often produce a variety of data from distributed sensors, common data reduction techniques including principal component analysis (PCA), probabilistic PCA, multidimensional scaling, and kernel PCA are introduced to reduce computational efforts and to enhance inference performance. Furthermore, sensorand temporal-level data fusion techniques are discussed based on Bayesian and alternative non-probabilistic models.

Chapter 3 of Volume 2 by Bernal builds on Chapter 2 by further exploring statistical inference and PCA for vibration-based damage detection and localization as one step in infrastructure decision making. Novelty detection, which is one class of statistical inference, is used for damage identification, particularly under a situation where environmental variations are not measured. Examples of damage detection using statistical subspace system identification, Kalman filtering, and cumulative sum charts are presented in sufficient detail to allow implementation by users.

Other types of vibration-based damage detection techniques are introduced in Chapter 4 of Volume 2 by Nagarajaiah et al. focusing on modal parameter identification using output-only signals. These output-only system identification techniques are particularly attractive for civil infrastructure systems because large-scale civil systems are often subjected to temporally and spatially distributed unknown system inputs. Furthermore, time varying characteristics of systems are captured using time-frequency analysis techniques such as the short-time Fourier transform, empirical mode decomposition, wavelet transforms, and blind source separation techniques.

In Chapter 5 of Volume 2 by Frangopal and Kim, the prediction of structural performance based on monitoring data using the Bayesian approaches described in Chapter 2 of Volume 2 are presented. However, the chapter goes much further, in showing how the data can also be utilized for cost-effective SHM planning, life-cycle performance analysis, and cost analysis. In particular, probability- and statistics-based decision-making approaches are presented to effectively treat epistemic uncertainty for rational performance assessment and prediction. Furthermore, measured data are used to reduce uncertainties in prognosis. Finally, the effects of SHM on life-cycle assessment and approaches for efficient integration of SHM into life-cycle analysis of civil infrastructure systems are discussed.

Finally, Chapter 6 of Volume 2 by Birken et al. focuses on a detailed case study to highlight the vexing challenges associated with large-scale data management in the field. Based on the Versatile Onboard Traffic-Embedded Roaming Sensors (VOTERS) project, a roaming sensing system is introduced where a group of sensors are mounted onto a survey vehicle to inspect the conditions of roads’ surfaces and bridge decks. A group of heterogeneous sensors are employed to capture the full and coherent picture of surface and subsurface defects over large spatial areas. Challenges and solutions associated with data uploading and storage, correlating spatially and temporally distributed data, and fusion and rapid processing of large amounts of bulk data are discussed in detail.

1.5 Overview of application of sensing systems to operational infrastructure

The application of sensors and sensing systems to specific infrastructure system types is presented in Chapters 7 through 21 of Volume 2. Each chapter focuses on only one infrastructure system type, in order to ensure a detailed and complete summary is presented to the readers. These application-specific chapters are designed to build upon the content contained in Volume 1 (i.e., sensing hardware and data collection methods) and Chapters 1 through 6 (i.e., sensor data interrogation and decision making) of Volume 2, with minimal overlap presented. Each application chapter begins with an introduction that provides the reader with the motivation for sensing in the defined field of application, followed by a brief summary of prior approaches (if any) to monitoring and assessing system performance and health. A summary of the sensors previously used in monitoring each application is provided with a description of how their operational attributes fit the application. Where appropriate, laboratory validations performed prior to deployment in the field will be described. Each chapter devotes a significant portion of its focus to specific case studies that provide a representative picture of how sensors and sensing systems are deployed and how sensing data are used to aid structure owners and managers in their decision-making processes. Finally, each chapter concludes with a summary of the lessons learned in each case study, with key findings generalized for future technology development and field deployment.

The following infrastructure systems have been selected for coverage in this book: bridges, seismically-excited buildings, super-tall towers, dams, tunnels, roads, wind turbines, pipelines, underwater systems offshore structures, railroad tracks, nuclear power plants, subsurface utilities, power systems, and levees. In Chapter 7 of Volume 2, Brownjohn et al. describe the current state of art in the deployment of long-term monitoring systems in bridges. The chapter both describes the sensors successfully used in this application and presents an extensive case study in which the sensors have been used to monitor the performance of the Tamar Bridge in Saltash, England. Equally impressive are the various monitoring systems deployed to monitor buildings. In particular, buildings in seismic areas have garnered the most instrumentation, given the extreme load that buildings are exposed to during earthquakes. Chapter 8 of Volume 2 by Mita highlights the deployment of sensing systems for seismic monitoring of high-rise structures. This chapter provides a broad overview before describing various case studies in Japan. Chapter 9 of Volume 2 by Ni describes the importance of structural monitoring and health monitoring for super-tall towers such as marquee TV towers located outside seismic areas.

Large earth structures have benefited from long-term deployments of sensors and sensing systems as presented in Chapters 10 through 13 of Volume 2. For example, Chapter 10 by Loh describes the deployment of a permanent monitoring system to the Fei-Tsui arch dam located in Taiwan. A major contribution of this chapter is an extensive description of data processing of the dam response data for system identification. Chapter 11 by Hoult and Soga introduce sensors commonly used for monitoring existing tunnels and those under construction. Their chapter covers the application of long-term monitoring of tunnels in both soft ground and rock, which come with very different sensing system requirements. Chapter 12 by Birken and Oristaglio presents a detailed overview of the sensing methods required to explore subgrade structures including buried pipes. The chapter has a very specific focus on the use of electromagnetic geophysical sensing arrays that are applied on a mobile platform. Chapter 13 by Inaudi explores the application of fiber optic sensors as a distributed sensing platform for monitoring the stability of levees, sinkholes, and landslides. In a similar manner, Chapter 14 by Glisic applies similar fiber optic sensors to monitor buried pipeline systems.

Transportation systems are also very important systems to monitor because of their vast spatial distribution. These systems often require mobile sensing strategies where sensors are based on a movable platform that can be moved through the system to collect data. Both road and rail transportation systems are considered in Volume 2. Chapter 15 by Wang and Birken explores the application of sensing technologies and nondestructive evaluation (NDE) methods to monitor the integrity of road systems including asphalt pavements and concrete bridge decks. Similarly, Chapter 16 by Rizzo explores the development of sensing systems for monitoring extensive lengths of railroad tracks. Eddy current, thermography, and ultrasonic methods are just a few of the key methods presented for rail road health assessment.

Chapters 17 and 18 of Volume 2 focus on sensing systems designed for the monitoring of offshore structures. Chapter 17 by Rizzo provides a broad overview of the sensing systems available for structures situated underwater, which is an inherently harsh environment for sensors. Chapter 18 by Kim and Lee goes into greater depth in the application of sensing systems for the hull monitoring of ships and offshore oil platforms.

Finally, Chapters 19 through 21 of Volume 2 are devoted to the application of sensing and monitoring systems to monitor the operation and performance of energy systems. Specifically, Chapter 19 by Rolfes et al. describes the application of monitoring systems to monitor the structural integrity of large wind turbine systems sited onand offshore. Chapter 20 by Sohn et al. focuses on the demanding sensing requirements of the nuclear industry to monitor the structural performance of heavy nuclear containment structures. Finally, Chapter 21 by Hiskens is an overview of the monitoring systems employed by the power industry to monitor the operation of national energy grids.

1.6 Future trends

The application of sensors and sensing systems to operational civil infrastructure systems has blossomed in recent years. Permanent structural monitoring systems have been successfully deployed on operational bridges, dams, pipelines, and offshore structures, among other structures. These permanent monitoring systems have been deployed to collect data related to the performance of these structures under normal load conditions as well as during extreme load events. It is clear that bridges and buildings have been the greatest beneficiaries of structural monitoring systems, especially those located in seismically active areas. For example, most of the large buildings and bridges in the western United States, as well as in Japan, have been instrumented with a minimal number of accelerometers through various regional strong ground motion programs. Wireless communications is currently ushering in a major paradigm-shift in the structural monitoring field. Wireless sensors offer infrastructure owners a lower-cost option for instrumentation thereby allowing more structures to be economically monitored. In addition, the ease of installation of wireless sensors is also driving higher sensor densities in a single structure, which in turn offers more quantitative data on structural performance and health. The trend of adoption of wireless telemetry in structural monitoring applications will continue unabated into the future; this is a promising trend that will lead to a proliferation of monitoring systems for civil infrastructure.

Other key sensing technologies that are equally paradigm-shifting include fiber optic sensors for distributed sensing and ultrasonic measurements. Fiber optic sensors have had a particularly high impact in the realm of large, continuous structures whose dimensions span miles, including pipelines, levees, and geotechnical systems (e.g., landfills, sink holes). Ultrasonic sensors and associated NDE methods used to be limited to manual operation when needed. However, technological developments are moving these powerful sensors into the realm of continuous, permanent monitoring. In addition to permanent monitoring systems, classical NDE methods have undergone a revolution in technology development as well as in deployment. As previously mentioned, NDE sensors are being developed for both permanent installation in structures as well as for more automated (and less manual) use in the field. For example, the use of NDE methods such as GPR has proven to be a powerful means of assessing continuous pavement systems found in roads and tarmacs (Saarenketo and Scullion, 2000). In this application domain, research and development have primarily focused on how to transition these powerful sensing techniques into platforms that allow them to sense large swaths of infrastructure systems as quickly and as cost-effectively as possible.

While sensors and sensing systems have made great strides toward offering structure owners valuable information that they can use for the assessment of their structures, there are a number of technological challenges that must be resolved to ensure that the full benefit of structural monitoring is realized for those structure owners who elect to procure such systems. The remainder of this section explores some of the more urgent and vexing challenges that remain ahead for the field.

1.7 Conclusion

Unquestionably, the future for sensors and sensing systems for the assessment of civil infrastructure systems has a very bright future. With the adoption of sensors in the infrastructure management field accelerating, future infrastructure will be better designed, easier to manage cost-effectively, and safer for the public to use. This book seeks to provide a single reference for both the novice and expert reader alike, with a detailed review of sensors, sensing systems, data management approaches, and field applications. While every attempt has been made to produce a book with great breadth, interested readers may also consult a number of other references that would give them deeper knowledge. The following list provides a number of references external to this book that may aid in the cultivation of deeper knowledge in sensors and their application to civil infrastructure systems. Enjoy!