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Monitoring and Assessment of Built Structures
M o n i t o r i n g a n d A s s e s s m e n t o f B u i l t
S t r u c t u r e s
R e p o r t
R R - 2 5 9
Hui (Henry) Xue, Qi Hao, and Weiming Shen
J u n e 2 0 0 8
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EXECUTIVE SUMMARY
1. INTRODUCTION... 1
2. SENSOR TECHNOLOGIES AND APPLICATIONS ... 1
2.1 Sensor Technology – MEMS and Wireless Sensor Networks ... 1
2.1.1 MEMS ... 2
2.1.2 Wireless sensors... 3
2.1.3 Hardware for wireless sensors ... 3
2.1.4 Embedded software for wireless sensors... 5
2.1.5 RFID sensors for cracks and corrosion detections... 7
2.2 Applications of Sensor Technologies in Structure Monitoring ... 7
2.2.1 Structural Health Monitoring (SHM) ... 7
2.2.2 Performance monitoring and optimization ... 11
3. FACILITY/BRIDGE MANAGEMENT SYSTEMS ... 11
3.1 Bridge Management System (BMS) ... 12
3.1.1 Objective and tasks... 12
3.1.2 Components... 13
3.2 Existing BMSs... 13
3.2.1 Canada... 13
3.2.2 USA... 15
3.3 Issues in Bridge Management ... 15
3.3.1 Deterioration model ... 15
3.3.2 Inspections ... 16
3.3.3 Non-destructive Evaluation (NDE) technology for concrete structures.. 17
4. CONCLUSIONS AND FUTURE RESEARCH OPPORTUNITIES ... 17
EXECUTIVE SUMMARY
Built structures, including bridges, dams, buildings, and pipelines, are complex engineered systems that support our society’s economy. These structures are often subject to severe loading scenarios and harsh environmental conditions that can result in long-term structural deterioration, and, eventually, loss of functionality and structural damages. Monitoring and assessment systems can help owners and operators to estimate the risk at different stages and plan maintenance and rehabilitation activities during the life-cycle of the structures. At present, visual inspection is still the most common means of evaluating the condition of the structures. Wireless monitoring has emerged in recent years as a promising technology that could greatly impact the field of structural monitoring and infrastructure asset management.
In addition to the research on deterioration model and cost strategy for management systems, there is a need to develop new non-destructive evaluation (NDE) techniques and damage/safety assessment models.
Management systems for buildings, roads and other infrastructures have not been as sufficiently developed and widely adopted as bridge management systems (BMSs). Ideally all these elements of infrastructure could be combined to form an all embracing infrastructure management system which would have some potential benefits.
It is anticipated that sensors and wireless networks can be embedded in an integrated management system throughout the entire life-cycle of a built structure (from planning, design, construction to operation) to provide information for scheduling, safety
monitoring, process monitoring, quality monitoring and control, heath and damage monitoring, performance monitoring, decision making and optimization. The information can be beneficial to each party of the project through each phase (designer, owner, contractor, manager and user).
The objective of this short term project focuses on a literature review to get a better understanding of recent R&D on sensor technologies, wireless networks,
non-destructive evaluation (NDE) techniques, bridge/structure management systems as well as assessment models. This report also discusses R&D challenges and identifies future research opportunities so that it can help set a long term goal in developing decision-making support tools for the life-cycle management of built structures.
1. INTRODUCTION
Built structures, including bridges, dams, buildings, and pipelines, are complexly engineered systems that support our society’s economy. These structures are often subject to severe loading scenarios and harsh environmental conditions that can result in long-term structural deterioration and, eventually, loss of functionality and structural damages. Monitoring and assessment systems can help owners and operators to estimate the risk at different stages and plan maintenance and rehabilitation activities during the life-cycle of the structures. At present, visual inspection is still the most common means of evaluating the condition of the structures. Wireless monitoring has emerged in recent years as a promising technology that could greatly impact the field of structural monitoring and infrastructure asset management.
The objective of this short term project focuses on a literature review to get a better understanding of recent R&D on sensor technologies, wireless networks,
non-destructive evaluation technologies, bridge management systems as well as assessment models. This report also discusses R&D challenges and identifies future research
opportunities. It can help set a long term goal in developing decision-making support tools for the life-cycle management of built structures.
The rest of the report is organized as follows: Section 2 reviews sensor technologies and wireless sensor networks, including the state-of-art hardware, software and data
processing algorithms, as well as their applications in structure health monitoring and performance monitoring; Section 3 provides an overview of Bridge Management System (BMS) and its application in Canada and USA; Section 4 summarizes the findings and discusses the challenging issues; Section 5 identifies potential research opportunities for IRC-London.
2. SENSOR TECHNOLOGIES AND APPLICATIONS
2.1 Sensor Technology – MEMS and Wireless Sensor Networks
Sensor technology has attracted much interest from scientists and engineers for several decades. Many types of sensors (including physical, electromagnetic, thermal, radiation, pressure sensors) have been extensively studied and developed for various
applications.
Nowadays, the application of sensors can be found almost anywhere in industry and in our daily lives. With the development of micro-electromechanical system (MEMS) and nanotechnology, new types of sensing mechanisms and technologies have been developed, especially in biomedical and chemical areas. The development of new sensors and technologies has a tremendous impact on human life, such as health, safety, environment and economics.
Traditionally a sensor is a device which measures a physical quantity and converts it into a signal. Based on the type of the quantity to be measured, sensors can be classified as
• Mechanical • Thermal
• Electromagnetic • Chemical
• Optical • Biological
The most common types of sensors used in the construction sector include mechanical (positioning, deformation, force, stress, strain, vibration, acceleration, acoustics,
pressure, etc), thermal (temperature, humidity, heat flux), chemical (air quality, oxygen, carbon monoxide/dioxide, corrosion, pH), optical (lumination, radiation) and biological (mold).
2.1.1 MEMS
MEMS is an emerging technology leveraging many technologies from the integrated circuit industry, as well as materials, micromachining and micro mechanical components or structures such as membranes, cantilever beams, gears, springs, and mirrors. This allows developing MEMS sensors small in size, low in cost and high in performance. Inertial sensors (accelerometers and gyroscopes) have been the most successful
examples of MEMS technologies due to the demands of the automotive industry (Du and Chen 2007).
The technologies used to fabricate 3D microstructure from silicon in MEMS sensors include isotropic and anisotropic etching (micro-engineering), various thin film deposition methods, anodic bonding, and the masking and doping techniques employed in IC fabrication (Bogue 2007).
Modern sensors are no longer traditional transducers. MEMS sensors can integrate electronics and sensors into one single silicon chip, which includes a sensing element, a transducer, an amplifier, a processor, and a memory (Bogue 2007).
An example is MEMS accelerometers which are fabricated by advanced surface
micromachining techniques. Many of them can sense in 2 or 3 axes with self-testing and over-range protection as shown in Fig. 1
Fig. 1. Micromachined MEMS accelerometer (Analog Devices, Inc.)
ISFET (Ion Selective Field Effect Transistor) is the emerging technology for gas and fluid sensing. As an application, MEMS gas sensors can be used in indoor air quality
monitoring, such as the detecting of volatile organic compounds and toxic organic
vapours. Other MEMS-based gas sensors include micro-hotplates and integrated sensor arrays (electronic nose chips) based on metal oxides, coated resonators and acoustic wave devices. Some commercial progress has been made in this area. For example, AppliedSensor has launched a family of IAQ sensors based on metal oxides and micromachined silicon substrates which can detect a range of oxidising and reducing gases and vapours. However, there are no inexpensive sensors that can selectively detect compounds such as benzene, toluene and butadiene (Bogue 2007).
2.1.2 Wireless sensors
In recent years, wireless sensors and sensor networks have been considered as substitutes for traditional tethered monitoring systems in the structural engineering field (Lynch et al 2006). Since extensive wiring is no longer required between the sensors and the data acquisition system, wireless structural monitoring systems cost much less and can be used in hazardous environments where wiring is impossible (Sammarco et al 2007). Wireless sensors can play greater roles in the processing of structural response data which can be utilized to screen data for signs of structural damage.
A wireless sensor itself can be a platform that is composed of autonomous data acquisition units with built-in traditional structural sensors (e.g. strain gages,
accelerometers, linear voltage displacement transducers, and inclinometers), mobile computing and wireless communication elements. With computational power coupled with the sensor, wireless sensors are capable of autonomous operations including data collection, processing and storage, interrogation of measurement data, and deciding when and what to communicate with other wireless sensors in the wireless sensor network (Lynch et al 2006).
2.1.3 Hardware for wireless sensors
As for the hardware part, wireless sensors include three or four subsystems: sensing interface, computational core, wireless transceiver and, for some, an actuator interface. A sensing interface to which the sensing transducer can be connected is responsible for converting an analog signal to a digital output (ADC). The computation core, represented by microcontrollers and memory, takes care of the data to be stored, processed and communicated. A radio transceiver is used for both transmission and reception of data. Three factors should be considered when designing wireless sensors for built structure monitoring: functionality, power consumption and cost. The higher resolution and sample rates of ADC provide more useful and reliable information for signal analysis. Longer range radio enhances the reliability of transmission and the capacity for monitoring larger structures. Dynamic monitoring often requires a faster processor/controller and more memory. However, all of these functional requirements require more power consumption and result in a higher cost. A trade-off between functionality, power consumption and cost is a case-based decision to be made when designing wireless sensors and wireless sensor networks.
Since Straser and Kiremidjian (1998) proposed the design of a low-cost wireless modular monitoring system (WiMMS) for civil structures using off-the-shelf components (New Micros board NMIT-0022, Motorola Microprocessor 68HC11, Proxim Proxlink wireless modem MSU2 and 240 Hz Harris sigma-delta ADC H17188IP), researchers have proposed sensing unit prototypes to address the different problems.
Lynch et al. (2004) have proposed a wireless sensor prototype that emphasizes the design of a powerful dual-processor computational core to reduce the power
consumption. The sensor uses a low-power 8-bit Atmel AVR AT90S8515 microcontroller for overall unit operation and real-time data acquisition, and a 32-bit 20 MHz Motorola MPC555 PowerPC for intensive data processing algorithms, such as embedded damage detection. Mitchell et al. (2002) proposed a wireless sensor network using a two-tier SHM architecture - wireless sensors and wireless data servers (called wireless cluster nodes). Data collected by the wireless sensors is transferred to cluster nodes to store and process. A key element of this wireless SHM system is its seamless interface to the Internet using http protocol which allows users to remotely access structural response data, as well as analysis results via web servers on the cluster nodes. Aoki et al. (2003) proposed a wireless sensing unit prototype called the Remote Intelligent Monitoring System (RIMS) for bridge and infrastructure health monitoring. The wireless sensing unit includes a three-axis MEMS accelerometer (Microstone MA3-04) and an additional 2 MB externally DRAM for storage of historical data and for improving the efficiency of local computations by minimizing the amount of data to be transmitted on the wireless network. The core component within each wireless sensor is the Realtek RTL-8019AS Ethernet controller with an embedded HTTP manager servlet that allows users to interact with sensors and perform tasks remotely through the Internet.
Recognizing the limitations of batteries in wireless sensors, many researchers have proposed different designs to emphasize the power efficiency of the components (Basheer et al., 2003; Ou et al., 2004; Sazonov et al., 2004; Chung et al. 2004). A good summary of these can be found in Lynch’s paper (2006).
Recently, commercial wireless sensor nodes and networks are increasingly available and will also continue to be an area for research (Culler et al. 2004). Sensor nodes can be purchased as part of complete wireless sensing systems with the advantages of low costs, immediate out-of-the-box operation and support tools for development and prototyping. A wireless sensor platform, called Mote, initially developed at the University of California-Berkeley, has been commercialized by Crossbow, such as Rene, MICA, MICA2 and MICAz (Zhao and Guibas 2004). MICAz meets the IEEE802.15.4 standard, with 1kHz 10-bit ADC, 7.4 MHz 8-bit Process 128 kB ROM and 512kB RAM, 38.4 kbps data rate at 2.4 GHz frequency. A major reason for the Motes’ popularity is the open design in both its hardware and software (TinyOS).
Cogent Computer Systems Inc. is another major supplier of off-the-shelf motes running SOS based on the XYZ Sensor Node wireless sensing platform developed at Embedded Networks and Applications Laboratory, Yale University (eng.yale.edu/enalab).
Intel developed a wireless sensor unit called iMode (Kling, 2003). At the core is a 32-bit, 12 MHz microcontroller with 64 kb RAM and 512 kB ROM for OS. A 2.4 GHz Bluetooth radio is integrated for high bandwidth (720 kbps) and high reliability. The assembled size of iMode is only 3.5 x 3.5 x2.5 cm3.
A number of other companies have also developed commercial wireless sensor platforms (Sammarco 2007), such as:
• Microstrain X-link
• Ambient Systems μNode
• Caiser from Cambridge Silicon Radio • Ember motes
• Lynx Technologies motes
In contrast to the Motes, these wireless sensor platforms are not open source (Lynch et al, 2006).
Sensor motes and sensors are both rapidly developing in terms of size and cost
reduction, and increasing functionality. Figure 2 shows the next-generation MICAz mote which will be offered in a much smaller physical package only about the size of a
postage stamp. Fig. 2 demonstrates the small size of a dual-axes accelerometer and a wireless node.
Fig. 2. MICAz
2.1.4 Embedded software for wireless sensors
A major advantage of wireless sensor platforms is the integration of a mobile computer within the sensors. There are two types of embedded software: the operating system (OS) for autonomous operations, and engineering analysis software, such as damage detection algorithms and FFT model analysis.
TinyOS developed by the researchers at the University of California-Berkeley, in collaboration with the Intel Research Berkeley Laboratory, is an operating system (OS) for various Crossbow and Intel Motes. A distinct advantage of TinyOS is that it is an open-source OS available to the public for free use and modification. Researchers (Tanner et al. 2003, Glaser 2004, and Kurata et al. 2004, Pakzad et al 2008) have used TinyOS to perform structural monitoring experiments in the laboratory. TinyOS (Hill 2000) is intended to maximize the potential of the limited resources, and render wireless
sensors both scalable and energy-efficient. TinyOS is written in the high-level C based language and designed to occupy only 256 bytes of RAM and 4 kB of ROM.
Some researchers have also developed operating systems for their own prototype sensors to meet specific needs or add more features (Lynch et al. 2004, Wang et al. 2005).
With a robust OS in place, engineers can set their focus on the development of the interrogation algorithms. As for structure health monitoring, Straser and Kiremidjian (1998) were the first to utilize algorithms for determining the health of a civil structure using wireless sensors. The algorithms use the normalized Arias intensity, i.e. summing the square of the structural acceleration measured over the duration of an earthquake to detect the formation of damage. Lynch et al (2003) have successfully embedded an FFT algorithm in a wireless sensing unit to provide the frequency response functions of structures.
A damage detection methodology based upon a pattern recognition framework is proposed by Sohn and Farrar (2001). The method begins with the stationary response time history of the structure at a single measurement location. Using the data,
autoregressive (AR) and exogenous input autoregressive (ARX) time series models are created. These AR–ARX time series models form a library of baseline models describing the structure in its undamaged state. When the coefficients of AR–ARX from the new measured structural response do not match the baseline models, then the structure is identified as damaged. One of the attractive features of the method is that it is
decentralized by determining damage from a single measurement location.
Lynch et al (2004) have presented damage index models based on the peak response of individual structural elements (e.g. columns, beams, joints) in the time domain.
Another type of algorithm used for Structural Health Monitoring (SHM) is system identification technique that originated two decades ago for mechanical and aerospace structures. In civil engineering, the ability to ascertain modal information (modal
frequencies, mode shapes, and damping ratios) from sensor data has paved the way for the assessment of structural performance and the calibration of analytical design models (Alampalli 2000). Due to the difficulty of exciting a large civil structure in a controlled manner with measurable input excitation forces, output-only dynamic data are commonly used for modal parameter estimation within the civil engineering field (Cunha and
Caetano 2006). Zimmerman et al (2008) presented their research results using three output-only modal identification techniques within a wireless sensing network (peak picking method, the random decrement method, and the frequency domain
decomposition method) by parallel processing.
Caffrey et al (2004) proposed a decentralized method of detecting damage by calculating the Fourier spectra of structural acceleration time histories so that modal frequencies and the signal energy contributions can be determined at each sensor location. The parameters then are wirelessly transferred to a centralized data repository and used to diagnose the damage.
Ruiz-Sandoval (2004) has proposed an agent-based framework embedded software for wireless sensors. Ruiz-Sandoval considered the advantages of the agent-based
reduced demand for communication because of embedded data processing; (3) excellent scalability due to role decomposition and functionality encapsulation. Ruiz-Sandoval also identified three distinct agent models within the wireless SHM system: each wireless sensor is a “node” agent; the system data repository is modeled as a “base station” agent, and the human user interacting with the system is defined as the “user” agent. To validate the proposed agent-based wireless SHM system, Ruiz-Sandoval implemented a 10-story shear building in Simulink. The complete agent system architecture is proven robust and effective in identifying damages.
2.1.5 RFID sensors for cracks and corrosion detections
RFID sensor is a passive radio technology, which captures radio energy emanated from a remote reader so that it can communicate its measurement back. This is specifically useful for the sensors installed in a structure like concrete. Mita and Takahira (2003) and Novak et al. (2003) have proposed RFID-based sensors that are designed to memorize the peak strain or peak displacement of an instrumented structural element. The sensor utilizes an inductor-capacitor (LC) circuit for the remote reader to read the frequency, and can be used to identify the cracking in welded steel connections and corrosion in reinforced concrete elements.
Watters et al. (2003) have also proposed wireless sensors based on RFID technology called Smart Pebble, for monitoring the amount of chloride ingress in concrete bridge decks. The sensor makes use of an electrolytic cell to correlate the concentration of chloride ions to a readable voltage signal. With an aggregate-like size, it can be fully embedded in concrete bridge decks during construction.
Carkhuff and Cain (2003) have proposed a passive RFID-based wireless sensor prototype to monitor concrete bridge decks for corrosion called Smart Aggregate (SA). Unlike other RFID-based wireless sensors, the prototype adopts two inductive coils - a 1 MHz coil picking up power from a remote interrogator, another 10.5 MHz coil for radio communication.
Saafi and Romine (2004) have also proposed a novel design of a passive RFID-based corrosion sensor to be embedded into concrete during construction. An innovative element of the sensor is the use of MEMS technology to create high-sensitivity sensing transducers within the sensor platform. The MEMS sensor is capable of monitoring the pH, relative humidity, and the concentration of chloride ions and CO2 of environmental parameters within concrete bridge decks.
The advantage of the passive RFID-based wireless sensors is the power-free feature, but the short coming is that they can only be embedded a few centimeters below the surface of the concrete.
2.2 Applications of Sensor Technologies in Structure Monitoring
2.2.1 Structural Health Monitoring (SHM)
Structural Health Monitoring (SHM) is estimating the state of structural health, or detecting a change in the structure that affects its performance (Kim 2006). Two major factors in SHM are the time-scale of the change (how quickly the change occurs) and the severity of the change. These factors present two major categories of SHM, disaster response (earthquake, explosion, etc.) and continuous health monitoring (ambient
vibrations, wind, etc.). There are two SHM approaches: direct damage detection (visual inspection, x-ray, etc.) and indirect damage detection (detecting changes in structural properties/behavior).
Structures, including bridges, buildings, dams and pipelines are often subjected to severe loading scenarios and harsh environmental conditions that are not anticipated during design. This might result in damage and failures during natural catastrophes, such as an earthquake or tornado, and in long-term structural deterioration.
Structural health monitoring offers an automated method for tracking the health of a structure by combining damage detection algorithms with structural monitoring systems. Damage detection methods can generally be classified as two types: local-based and global-based (Lynch and Loh, 2006).
Local-based damage detection methods identify damage based on monitoring structures at their components or subcomponents. Many non-destructive evaluation (NDE)
technologies, including ultrasonic inspection, can be used for local-based damage detection. Autoregressive (Sohn and Farrar 2001) and damage index (Lynch et al, 2004) mentioned previously belong to local-based damage detection which can be embedded into the wireless sensor platforms for autonomous execution.
Global-based damage detection refers to numerical methods that consider the global vibration characteristics (e.g. mode shapes, natural frequencies) of a structure to identify damage. A large number of sensors are required to implement global-based damage detection, particularly for structures exposed to widely varying environmental and operational loadings, such as bridges, buildings and dams. Inexpensive wireless sensor network technology provides the ability to economically realize SHM.
Health monitoring of Golden Gate Bridge using wireless sensor networks
The Researchers at the University of California, Berkeley, designed, implemented, deployed a Mote network and tested it on the Golden Gate Bridge (GGB) (Kim et al, 2006, Pakzad 2008). The goal was to determine the response of the 70-year-old structure to ambient and extreme conditions, compare actual behavior to design prediction, and test the scalability and performance of the WSN. This will result in greater public safety as well as improving the state of knowledge of the structural engineering profession.
A 64 node wireless sensor network was deployed for the main span (1280 m) and south tower (210 m) of the GGB to identify the vibration characteristics. Fig. 3 shows the instrumentation plan (Pakzad, 2008). The nodes on the west side were placed 30.5m apart, except at places where an obstacle obstructs a clear line of sight (15.25 m). A complete cycle of sampling and data collection for the full network produces 20 MB of data and takes about 9 hours. 13 sets of data were collected with the first set of batteries (174 total runs during the deployment including runs where the network was being installed and tested).
The sensor node consists of a 16-bit ADC MicaZ Mote with an 8-bit, 8 MHz controller and 512 kB flash memory, and a 2.4 GHz radio transceiver with a bi-directional antenna. To capture the dynamic characteristics of the bridge, acceleration is the most
straight-forward physical quantity to measure. In this application, two models of accelerometers were used: a large range accelerometer (±2 g with sensitive of 1 mg at 25 Hz) for earthquakes, and a more sensitive low range as of 10 μg accelerometer for measuring ambient vibrations due to wind and traffic. A 1k Hz Sampling rate was used.
The system software is based on the TinyOS operating system (TinyOS 2007), which is an open source framework for programming Mica motes (Hill et al, 2000). TinyOS is multilevel component-oriented software that supports a wide variety of applications for wireless sensor networks. Low-level components perform basic tasks, and higher level components use sequences of low-level components to achieve more complex
functionality while maintaining efficiency and simplicity of coding (Woo et al. 2003). The application layer was developed for scalable structural health monitoring (Kim et al, 2006). In this application, the embedded software in the node acts as a digital filter to reduce the Gaussian noise level and the amount of data sent to the base station by averaging a number of samples.
With the ambient vibration data (accelerations in transverse and vertical for main span, transverse and longitudinal for tower), a stochastic (output-only) multivariate
autoregressive model (ARX) was used off-line to estimate modal properties of the bridge. The researchers were interested in frequencies below 5 Hz which are the more important vibration modes of the main span.
Fig. 3. Instrumentation plan
A Historic Theatre in Detroit
A historic theatre located in metropolitan Detroit (one of the largest in the United States) was selected for a dynamic evaluation using a wireless sensing network (Zimmerman et al 2008). The main balcony, approximately 50 m wide, located at the fifth floor of the building, as shown in Fig. 4, is structurally supported only at the rear and sides of the auditorium. As a result of its long unsupported span, the theatre’s balcony is known to suffer from human-perceptible vibrations. The front section of the main balcony of the
theatre (specifically the first five rows within a 3m band of the balcony edge) was instrumented with 21 wireless accelerometers (16-bit data collection on four
simultaneous sensor channels, up to 300m on the 900 MHz radio band) in a 7x3 grid to monitor the vertical acceleration. A low-power 8-bit microcontroller is included in the wireless sensor for local data processing, and a rich library of data interrogation algorithms has been included in the operating system (Lynch 2007).
Three output-only modal identification techniques were used for analysis: 1) peak picking (PP) method to determine modal frequencies of a structure by picking a certain number of largest peaks in Frequency Response Function (FRF) obtained by an embedded FFT algorithm. 2) Frequency domain decomposition (FDD) method to determine mode shapes by decomposing the spectral density matrix into a set of single degree of freedom systems. 3) Random decrement (RD) method to determine damping ratios by transforming time history response at each node into a SDOF free decay response function for a specific mode.
All three algorithms were embedded in wireless sensors for distributed and parallel data processing, which minimizes the need for wireless communication. This parallel
approach allows a wireless sensing network to employ typical offline modal analysis techniques to autonomously extract spatial modal information from a large network of sensors without the need for a central data repository.
2.2.2 Performance monitoring and optimization
There has been an increasing interest in the adoption of emerging sensing technologies for building performance monitoring and optimization. The performance of a building can be evaluated by some physical factors that serve as principle indicators. For condition monitoring and indoor environment, temperature and humidity are key indicators. For life-cycle management, maintenance and energy consumption are the main cost components. All of these factors can be objectively evaluated from data collected by sensors.
Indoor temperature and humidity are the key physical factors for evaluating the performance of a building. Water and moisture can also cause structural damage, reduce the thermal resistance of building materials, change the physical properties of materials, and deform materials. Hygrothermal models are useful tools in assessing and optimizing the heat and moisture performance of building envelopes, but they require accurate indoor and outdoor climatic data (Glaser, 2008). Standardized methodology for dynamic moisture design and hygrothermal loads does not yet exist. Kalamees et al (2006) measured and analyzed the indoor humidity loads in 101 light-weight timber frame detached houses during a 2 year period. Temperature, relative humidity and ventilation rate were measured within 100 bedrooms and 79 living rooms. They studied the different moisture supply levels and their dependence on outdoor temperature, and determined the moisture supply design curve and the room temperature design curve for houses with low occupancy.
Many other researchers have been using sensors for different purposes, such as field testing, new material and design, construction monitoring and control, intelligent building, and green building (Henze 2001, El-Salakawy 2005, Sargand 2005, Malkawi 2005, Tsai 2008, Bagheri-Zadeh 2007, Brownjohn 2008).
3. FACILITY/BRIDGE MANAGEMENT SYSTEMS
In 2002, the Canadian Public Sector Accounting Board (PSAB) released a research report - Accounting for Infrastructure in the Public Sector. A key recommendation of this report is that municipalities should record and report their capital assets in their financial statements, including information on the condition of those assets. A new requirement for the recognition of capital assets - Tangible Capital Assets (TCA) will be applied in 2009. TCA is a significant economic resource managed by governments and a key component in the delivery of many government programs. It includes items such as roads, buildings, vehicles, equipment, land, bridges and other utility systems [15]. At present, Quebec has already a TCA policy. Nova Scotia, Saskatchewan, Alberta and British Columbia have been compliant with PSAB [12].
Infrastructure systems comprise a wide range of facilities that provide the public with vital services. With the aging of its infrastructure, Canada, like other developed countries, is facing a critical problem to how best to deal with the complex and
fragmental issues existing in current infrastructure management. Highway bridges are considered to be more critical as they are vital links in any road network. Bridge
management, as an important part of the infrastructure management, is attracting more and more attention (Hammad 2007).
Research emphasis in recent years has been shifting from the design and construction of new bridges to the inspection and maintenance of existing ones in order to upgrade the existing networks and to keep them in a safe and operable condition. These activities demand a tremendous amount of data collection and funds. Therefore, Bridge
Management Systems (BMSs) have been developed to provide decision supports to improve the quality of inspection and the allocation of limited funds.
3.1 Bridge Management System (BMS)
3.1.1 Objective and tasks
Bridge life-cycle management aims to perform the management functionalities related to bridges from the conceptual stage to the end of their useful life, through the design, construction, operation and maintenance stages (Hammad 2005).
The major tasks in bridge management are: (1) collection of inventory data, (2) inspection, (3) assessment of condition and strength, (4) repair, strengthening or replacement, and (5) prioritizing the allocation of funds.
BMSs are means of managing bridge information to support decision-making that ensures their long-term health and to formulate maintenance programs in line with budget constraints and funding limitations.
The main bridge management activities such as inspections, assessments, testing, maintenance, prioritization and replacement, can be combined to produce a framework for a computerized bridge management system that will provide both project and network-level information. The types of project-level tasks include:
• measures the condition of each structural element and component of a bridge • the load carrying capacity of a bridge and its most structurally vulnerable parts • the rate of deterioration of elements and components of a bridge enabling their
future condition to be predicted
• predictions of when a bridge will become substandard in terms of the load carrying capacity
• identification of the maintenance requirements of a bridge • guidance on effective maintenance strategies and methods
• programs of maintenance work indicating the timing of specified maintenance methods needed in order to minimize the whole life cost of a bridge.
The types of network-level tasks include:
• prioritized programs of maintenance when the optimization of the program is constrained by factors such as a maintenance budget that is insufficient to enable all the work in the optimal program to be carried out
• values of policy target parameters such as (a) the number of bridges with load restrictions at a given date, (b) the number of bridge replacements each year and (c) the average condition of bridges in the asset inventory at a given date
• degree of compliance of measured policy target parameters with corresponding benchmark values
3.1.2 Components
A bridge management system includes four basic components: data storage, cost and deterioration models, optimization and analysis models, and updating functions (Czepiel 1996, Ryall 2001).
The core part of a BMS is the database which is built up of information obtained from the regular inspection and maintenance activities. Bridge database management includes the collection, updating, integration, and archiving of the following information: (1) bridge general information (location, name, type, load capacity, etc.), (2) design information and physical properties of the elements, (3) regular inspection records, (4) condition and strength assessment reports, (5) repair and maintenance records, and (6) cost records (Hammad, 2005).
Information stored in the database is used as input into the modeling. The models are used to predict future conditions for each element and to perform a "what if" analysis under different budget constraints to determine the impacts of carrying out different projects. The three primary types of models are deterioration, cost and optimization models.
Deterioration models predict the condition of bridge elements at any given point in the future.
A bridge management system typically estimates two types of costs: improvement and user costs. Improvement costs are estimated to determine the cost of a maintenance action (repair and rehabilitation) to improve the condition of an element. The expected user cost for safety and serviceability improvements should also be estimated.
Based on the results of the cost and deterioration models, the optimization models determine the least-cost maintenance, repair and rehabilitation strategies for bridge elements using life-cycle cost analysis or some comparable procedures. The BMS can also perform multi-year, network-level analysis.
The BMS generates summaries and reports for planning and programming processes, and updates the prediction and cost models.
3.2 Existing BMSs
3.2.1 Canada
More than 40% of the bridges currently in use in Canada were built over 50 years ago (Bisby, etc, 2004), and a significant number of these structures need strengthening, rehabilitation, or replacement, using limited maintenance budgets. In Canada there is no predominant BMS that is used by all provinces. One of the major issues in Canada's bridge management is the lack of unified specifications for the inspection, maintenance, and rehabilitation. Unlike the USA, Canada has no federal specification for the bridge inventory. Each province has its own specifications (Hammad 2007). Furthermore, the available BMSs in different provinces vary in terms of their architecture, functionalities, and interfaces. Table 1 lists the comparison of the BMSs of different provinces and territories in Canada (Hammad 2007).
Ontario Bridge Management System (OBMS) is a typical representative of BMSs in Canada. Quebec Bridge Management System (QBMS) and Nova Scotia Bridge Management System (NSBMS) are very similar to OBMS. Another BMS, the Bridge Expert Analysis and Decision Support (BEADS), in Alberta, interacts with the corporate data storage and the other components of Transportation Infrastructure Management System (TIMS). The following provides more details about OBMS and BEADS. Ontario Bridge Management System (Thompson etc, 1999, 2003, Hammad 2007) Ontario is one of the earliest provinces to develop a BMS in Canada. In order to manage the 3,000 bridges, the Ministry of Transportation of Ontario (MTO) engaged ITX Stanley, Ltd. to develop the Ontario Bridge Management System (OBMS) in 1998.
There are three main models in the OBMS: Deterioration Model, Knowledge Model, and Cost Model. OBMS, like other BMSs, uses the Markovian deterioration model to predict the deterioration of bridges. The Knowledge Model is to suggest a proper rehabilitation method when there are alternatives to choose from. Decision trees and decision tables are based on the Ministry’s Structure Rehabilitation Manual and Structural Steel Coating Manual. In the Cost Model, the cost estimates for alternatives are based on unit costs of tender items. The MTO has a comprehensive cost database, called Project Value System (PVS), at the project-level. The decision making process includes monitoring, needs identification, policy development, priority setting, and budgeting and funding allocation. MTO uses the Bridge Condition Index (BCI) for assessment of the bridge conditions based on the remaining economic value of bridges. Project-level analysis and network-level analysis results are consistent because the network-level analysis is based on project-level models.
OBMS offers a powerful, intuitive user interface and includes linkages to the Ministry’s Bridge Document Image Management System, GIS mapping system, and tender item unit cost database.
BEADS (Alberta) (Loo etc, 2003)
Alberta has a much more comprehensive transportation management system named TIMS, which consists of the Roadway Maintenance and Rehabilitation Application (RoMaRa), the Network Expansion Support System (NESS) and the BEADS system. To cooperate with other systems in the TIMS, the Bridge Expert Analysis and Decision Support system (BEADS), therefore, has different architecture from other BMSs. The BEADS system consists of a series of individual modules including Substructure, Superstructure, Paint, Strength, Bridge Width, Bridge Rail, Vertical Clearance,
Replacement and Culvert modules. Based on existing and predicted condition and functionality states, the modules identify potential work activities, including their timing and cost, throughout the economic life cycle. On the top of these models there is a Strategy Builder Module, which then assembles and groups the identified work activities into feasible life-cycle strategies. Once the project-level analysis results have been determined, a network-level analysis may be performed to facilitate short-term programming, analyze long-range budget scenarios, evaluate network status, and assess the impact of policy decisions.
SLAB-D – A Bridge Deck Management System developed at NRC-IRC
The Researchers at NRC-IRC have developed a decision support tool for life-cycle management of bridge decks, called SLAB-D (Service Life Analysis of Bridge Decks) based on innovative approaches in models for predicting deterioration, assessing risk and optimization (Lounis, 2001, 2006, 2007a, 2007b, 2007c, Morsouc etc 2006, Gaigle etc 2004, Zhang etc 2006, Kyle 2002).
3.2.2 USA
Pontis
Pontis is one of the two main BMSs used in the USA. Pontis includes many innovative features. The condition data included in the system is more detailed than the
requirement of the National Bridge Inventory (NBI 2006). For condition assessment, the bridge is divided into individual elements or sections. The condition of each element is reported according to a condition state, which is a quantitative measure of deterioration. Pontis also views bridge deterioration as probabilistic, recognizing the uncertainty in predicting deterioration rates. The system models deterioration of the bridge elements as a Markov process. Pontis automatically updates the deterioration rates after historical inspection data is gathered. Pontis has the ability to estimate accident costs and user costs resulting from detours and travel time costs. This information is used in the optimization models to examine trade-offs between options. In the optimization routine, maintenance, repair and rehabilitation actions are separated from improvement actions. Pontis employs a top-down analytical approach by optimizing over the network before determining individual bridge projects (Czepiel, E. 1995).
Bridgit
BRIDGIT is another main BMS in the USA. BRIDGIT is very similar to Pontis in terms of its modeling capabilities. The system requires data at an element level and reports the condition of the elements in terms of condition states. Deterioration is modeled as a Markov process. Cost models are addressed in a similar fashion. The primary difference is in the optimization model. Bridgit adopts the bottom-up approach in optimization. It can perform multi-year analysis and consider delaying actions on a particular bridge to a later date. Pontis only has this capability at the network level. This bottom-up approach provides better results for smaller bridge populations than top-down programming. Its disadvantage is that the system is slower than Pontis for larger bridge populations. The main uses of Bridgit include scheduling and tracking of maintenance, repair and
rehabilitation activities (MR&R), keeping history, estimating the cost, and creating and maintaining a list of MR&R actions.
3.3 Issues in Bridge Management
3.3.1 Deterioration model
The main causes of deterioration of construction materials and components are corrosion, freeze-thaw effects, alkali silica reaction and sulphate attack.
Deterioration models can be deterministic or probabilistic in nature. A deterministic model predicts that a bridge will deteriorate at a known, given rate. A probabilistic model takes into consideration that the actual deterioration rate is unknown, and includes a
probability that the bridge will actually deteriorate at a certain rate. In addition, most deterioration models are patterned as a Markov process. This type of model predicts deterioration in a probabilistic fashion and based on the assumption that future
deterioration depends only on the current condition state, not historical conditions of the element.
3.3.2 Inspections
Both of the two deterioration models need the condition assessment of the bridge which is based on the inspection information. There are two types of approach:
• Visual observations and simple tests to subjectively assess the condition on an arbitrary scale ranging
• Measurement of physical/chemical parameters such as concrete strength, thickness of
steel sections, concrete resistivity and chloride content using more sophisticated tests Both approaches have disadvantages.
The major limitation of visual observations is the subjectivity of the condition
assessment. It relies on the judgment of engineers which can render the results biased and unreliable (Phares 2001). Moreover, it might not detect latent defects or the early stages of deterioration. Some defects that occur on bridges provide no visual indications. Most defects only become visible when they have developed significantly.
In contrast, the main advantage of the field measurement is that it can provide more reliable quantitative data, and enable latent defects to be detected and diagnosed in most circumstances. However, this method has not been adopted by BMSs at present. Further research into developing new non-destructive techniques or sensors is also needed.
Currently, in Canada and the USA, the most common means of evaluating a bridge is to simply assess the condition visually. By the AASHTO’s Manual for the Condition
Evaluation of Bridges, there are 5 different types of bridge inspections:
Initial inspection is the first inspection completed on a bridge. The goal of this inspection is to establish baseline structural conditions and to identify potential or known problem areas.
Routine inspection is the most common type of inspection. The goal of a routine inspection is to assess the physical and functional condition of a bridge and to ensure that the bridge continues to satisfy all applicable serviceability
requirements.
A damage inspection is an unscheduled inspection to assess structural damages resulting from environmental factors or human actions.
An in-depth inspection is a close-up and hands-on inspection of one or more members to identify any deficiencies not readily detectable using routine inspection procedures.
Special inspections are scheduled inspections completed solely to monitor a known or suspected deficiency.
The Ontario Structure Inspection Manual is the guide for bridge inspection in Ontario. Based on the manual, all existing bridges shall be inspected once every two years. The Bridge Condition Index (BCI) is used to assess the bridge condition by incorporating information from the condition of the various bridge elements (deck, superstructure,
substructure and barrier). The Bridge Sufficiency Index (BSI) ranks the bridge using the results of the BCI.
3.3.3 Non-destructive Evaluation (NDE) technology for concrete structures
Some NDE technologies used in bridge inspection are listed as follows (Rens 2005, 1998):
Electrical Methods: Half-cell potential measurements, one of the common electrical methods used in field inspection can yield information about corrosion activity. The application of the method is limited to uncoated reinforcing steel. This method gives no information about the rate of corrosion. Since delamination usually occurs as a consequence of reinforcing steel corrosion activity, this method may be applicable to assess delamination causes (Qian, 2004, Rens 2005).
Impact-Echo: The impact-echo method is based on the analysis of the longitudinal stress waves generated by the impact of ball bearings on the concrete surface. This method can be used to detect delaminations in concrete slabs, characterization of surface-opening cracks, and analysis of interfacial bond quality in concrete and bridges.
Radar: Radar technology is a very efficient method to detect delamination in bridge decks as well as the thickness of the cover. The use of dual frequency radar allows more accurate characterization of the defects. It has the potential to provide an assessment of bridge decks at traffic speed combined with automated signal processing and imaging (Chase 1998).
Ultrasonic: This method measures the time required for the ultrasonic pulses to travel through concrete members (Rens and Greimann 1997; Mohamad and Rens 2001). The measurements of the pulse speed can be used to determine the quality of concrete compressive strength (Krautkramer 1990) and to detect cracks and voids. The presence of reinforcement influences the application of the method. Some literature reports that the decrease in the pulse amplitude
(ultrasonic attenuation) is a more sensitive and reliable parameter to determine distributed cracking in the concrete than pulse velocity (Suaris and Fernando 1987, Selleck et al. 1998).
4. CONCLUSIONS AND FUTURE RESEARCH OPPORTUNITIES
• Sensors and wireless network
Leveraging the MEMS and nano-technologies, wireless sensors can be produced in a small size, with high performance and a low price. Wireless sensor networks are becoming realistic for the monitoring of large scale structures. However, there still exist some challenging issues:
Due to the harsh environment, such as temperature extremes, freeze-thaw cycles, and de-icing salt (for bridge and road), stability, life frame and power consumption of sensors are main concerns, especially for embedded continuous monitoring wireless sensors.
Some issues include time synchronization, multi-scale network topologies, and decentralized data processing within large scale networks. They are related to both hardware and software. For hardware, scalability involves sensitivity and range of MEMS sensors, communication bandwidth of the radio, and power usage. The software issues include reliability of command dissemination and
data transfer, management of large volumes of data, and scalable algorithms for analyzing data.
• Structural health monitoring (SHM)
SHM is for sensing the state of a structural system, diagnosing the structure’s current condition, performing a prognosis of expected future performance, and providing information for decisions about maintenance, safety, and emergency actions. SHM is becoming increasingly popular with the rapid reduction in size and cost of MEMS-based wireless sensors. For SHM, accelerometers are usually used to measure dynamic responses of the structure on disaster or ambient vibrations. To assess the health state of the structure, two damage assessment algorithms are used, autoregressive (AR) and system identification. The autoregressive (AR) method is based on the time history of AR coefficients at a single location to identify local damages. It needs continuous measurements of data. The challenge is to identify the structure health/damage based on the local results. Physics-based modeling for structure analysis is needed. System identification is to analyze global vibration characteristics of the structure (modal frequencies, mode shapes, and damping ratios) to assess the damage. However, it is not adequately sensitive for identifying the health of the structure and also requires a significant number of sensors for a large structure. Future research on developing other damage assessment methods and embedded algorithms is needed.
• Building performance monitoring
Interest in building performance monitoring with wireless sensor networks is increasing. Future research areas include sensing, network, algorithms and systems for indoor environment, energy consumption, intelligent and green building.
• Bridge Management Systems
Most current BMSs can manage bridges at both the project and network levels. One of the main tasks performed by any BMS is the determination of the condition as well as deterioration of bridges. While the information used in deterioration models and condition assessment is best collected from the physical/testing inspections, now this information is mainly acquired through the visual inspections of the structures. Current BMSs have not taken advantage of wireless sensor network technologies for health monitoring and condition assessment. The following areas need to be addressed:
Developing sensor-based practical and reliable NDE methods for inspection. Deploying wireless sensor networks for BMSs / Infrastructure Management
System as a regular means for real time monitoring and life-cycle management. Developing stable, durable, and cost-effective sensing technologies for corrosion
and quantitative measurement of deterioration. • Bridge condition assessment
The condition assessment is usually carried out for each element of a bridge. Since the behavior of each component can affect the functions of the others, a realistic analysis needs to consider the interactions of components as well. To give an overall condition for a bridge, a promising solution is to develop a physical-based approach for structure-level condition/safety assessment from the condition of elements or distributed sensors.
• Facility Management
Management systems for building, road and other infrastructures have not been as sufficiently developed and widely adopted as BMSs. Theoretically, all these elements of infrastructures could be combined to form an all-encompassing infrastructure
management system which would have some potential benefits. At the present time, however, there are still many barriers in both techniques and politics, such as
multidisciplinary modeling, NDE and sensor technologies, code/standards, multi-owners and stockholders.
• Agent-based wireless sensor networks
In a wireless network, each wireless sensor is a distributed subsystem with hardware and software, which can perform its own tasks and collaborate with a central server or other sensors. A multi-agent based system can be used to improve the flexibility for changing circumstances, efficiency for local control and processing, cooperative ability and scalability for large structures.
• Integrated system for life-cycle management
As an emerging technology, it is anticipated that sensors and wireless networks can be combined with an integrated management system throughout the entire life-cycle of a structure (from planning, design, construction to operation), to provide information for scheduling, safety monitoring, process monitoring, quality monitoring and control, health and damage monitoring, performance monitoring, decision making and optimization. The information can benefit each party of the project through each phase (designer, owner, contractor, manager and user).
REFERENCE
AASHTO (2005), User’s Manual of Pontis (Release 4.4). American Association of State Highway and Transportation Officials, Inc.
Alampalli, S. (2000). “Effects of testing, analysis, damage, and environment on modal parameters.” Mech. Syst. Signal Process, 14(1), 63–74.
Aoki, S., Fujino, Y., and Abe, M., 2003, “Intelligent Bridge Maintenance System Using MEMS and Network Technology,” in Smart Systems and NDE for Civil
Infrastructures, San Diego, CA, March 3–6, Proceedings of the SPIE, Vol. 5057, 37– 42.
Bagheri-Zadeh, S.H., Kim, H.,Hounsell, H.,Wood, C.R., Soleymani, H., and King, M (2007), Field Study of Concrete Maturity Methodology in Cold Weather, Journal of Construction Engineering and Management, Vol. 133, No. 11, (2007)827–835 Basheer, M. R., Rao, V., and Derriso, M., 2003, “Self-organizing Wireless Sensor
Networks for Structural Health Monitoring,” in Proceedings of the 4th International Workshop on Structural Health Monitoring, Stanford, CA, September 15–17, 1193– 1206.
Bennett, R., Hayes-Gill, B., Crowe, J. A., Armitage, R., Rodgers, D., and Hendroff, A., 1999, “Wireless Monitoring of Highways,” in Smart Systems for Bridges, Structures, and Highways, Newport Beach, CA, March 1–2, Proceedings of the SPIE, Vol. 3671, 173–182.
Bisby, L.A. and Briglio, M.B. (2004). ISIS Canada Educational Module No. 5: An Introduction to Structural Health Monitoring, Prepared by ISIS Canada, Page 3. Bogue, R. (2007), “MEMS sensors: past, present and future” Sensor Review, 27(1), 7-13 Brownjohn, J.M. and Pan, T.C. (2008) Identifying Loading and Response Mechanisms
from Ten Years of Performance Monitoring of a Tall Building, Journal of Performance of Constructed Facilities, Vol. 22, No. 1, (2008), 24–34.
Caffrey, J., Govindan, R., Johnson, E., Krishnamachari, B., Masri, S., Sukhatme, G., Chintalapudi, K., Dantu, K., Rangwala, S., Sridharan, A., Xu, N., and Zuniga, M., 2004, “Networked Sensing for Structural Health Monitoring,” in Proceedings of the 4th International Workshop on Structural Control, New York, NY, June 10–11, 57–66. Carkhuff, B. and Cain, R., 2003, “Corrosion Sensors for Concrete Bridges,” IEEE
Instrumentation and Measurement Magazine, Vol. 6, No. 2, 19–24.
Chase, S. B. (1998). “Newly developed technologies for nondestructive evaluation of highway bridges.” Structural Engineering World Wide 1998, San Francisco, paper T208-1
Chung, H-C., Enomoto, T., Shinozuka, M., Chou, P., Park, C., Yokoi, I., and Morishita, S., 2004, “Real-time Visualization of Structural Response with Wireless MEMS Sensors,” in Proceedings of the 13th World Conference on Earthquake Engineering, Vancouver, BC, Canada, August 2–6.
Culler, D. E. and Mulder, H., 2004, “Smart Sensors to Network the World,” Scientific American, Vol. 290, No. 6, 84–91.
Cunha, A., and Caetano, E. (2006). “Experimental modal analysis of civil engineering structures.” J. Sound Vib., 40(6), 12–20.
Czepiel, E. (1996), "Bridge Management Systems Literature Review and Search", ITI technical report no. 11, November 1996
Daigle, L.; Lounis, Z.; Cusson, D. (2004), "Numerical prediction of early-age cracking and corrosion in high performance concrete bridges - case study," TAC Annual Conference 2004 - Transportation Innovation-Accelerating the Pace (Quebec City, September 19, 2004), pp. 1-20, September 01, 2004
Du, H., Chen, D. (2007), “Recent development of MEMS sensor technology.” Sensor Review, 27(1)
El-Salakawy, E.,Brahim Benmokrane, B.,El-Ragaby, A. and Nadeau, D. (2005),Field Investigation on the First Bridge Deck Slab Reinforced with Glass FRP Bars
Constructed in Canada, Journal of Composites for Construction, Vol. 9, No.6, (2005), 470–479
Farrar, C. R., Allen, D. W., Ball, S., Masquelier, M. P., and Park, G., 2005, “Coupling Sensing Hardware with Data Interrogation Software for Structural Health Monitoring,” in Proceedings of the 6th International Symposium on Dynamic Problems of
Mechanics (DINAME), Ouro Preto, Brazil, February 29–March 4.
Glaser, S. D., 2004, “Some Real-world Applications of Wireless Sensor Nodes,” in Smart Structures and Materials: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, San Diego, CA, March 15–18, Proceedings of the SPIE, Vol. 5391, 344–355.
Glaser, S.D., Tolman, A. (2008), “Sense of Sensing: From Data to Informed Decisions for the Built Environment”, J. of Infrastructure Systems, 14(1), 4-11.
Hammad, A., Zhang, C., Hu, Y. & Mozaffari, E. (2005), Mobile Model-Based Bridge Lifecycle Management Systems, Journal of Computer-Aided Civil and Infrastructure Engineering.
Hammad,A., Yan,J., Mostofi, B. (2007), Recent Development of Bridge Management Systems in Canada, Annual Conference of the Transportation Association of Canada Saskatoon, Saskatchewan,2007
Henze, G.P., (2001), BUILDING ENERGY MANAGEMENT AS CONTINUOUS
QUALITY CONTROL PROCESS, Journal of Architectural Engineering, Vol. 7, No. 4, 97–106
Hill, J., 2000, “A System Architecture Supporting Networked Sensors,” Master’s Thesis, University of California-Berkeley, Berkeley, CA.
Kalamees, T., Vinha, J., and Kurnitski, J. (2006). “Indoor humidity loads and moisture production in lightweight timber-frame detached houses.” J. Build. Phys., 29(3), 219– 246.
Kim, S., Pakzad, S., Culler, D. E., Demmel, J., Fenves, G., Glaser, S., and Turon, M. (2006). “Health monitoring of civil infrastructures using wireless sensor networks.” Technical Rep. No. UCB/EECS-2006-121,EECS Dept., Univ. of California, Berkeley,
Calif., (http:// www.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-121.pdf).
Kling, R. M., 2003, “Intel Mote: An Enhanced Sensor Network Node,” in Proceedings of the International Workshop on Advanced Sensors, Structural Health Monitoring and Smart Structures, Tokyo, Japan, November 10–11.
Krautkramer, J., and Krautkramer, H. (1990). Ultrasonic testing of materials, 4th Ed., Springer, New York
Kurata, N., Spencer, B. F. Jr., and Ruiz-Sandoval, M., 2004, “Building Risk Monitoring Using Wireless Sensor Network,” in Proceedings of the 13th World Conference on Earthquake Engineering, August 2–6, Vancouver, BC, Canada.
Kyle B.R.; Lounis, Z.; Vanier, D.J. (2002), "Multi-objective optimisation of asset
maintenance management," v. 1of 2, 9th International Conference on Computing in Civil and Building Engineering (Taipei, Taiwan, April 03, 2002), pp. 701-706, May 01, 2002
Loo, T., Williamson, D. and Quinton, R. (2003). Bridge Expert Analysis and Decision Support System (BEADS). The 9th International Bridge Management Conference, Orlando, Florida, April, 2003.
Lounis, Z. (2006), "Risk-based maintenance optimization of aging highway bridge decks," Sustainable Infrastructure: Techniques, Tools & Guidelines - Building Science Insight 2006 September 01, 2006
Lounis, Z. (2007a) "Aging highway bridges," Canadian Consulting Engineer, 48, (1), Jan/Feb., pp. 30, 34, January 01, 2007
Lounis, Z. (2007b), "A Probabilistic cumulative damage model for service life prediction of concrete structures," ACI Workshop on Advances in Service Life Models that Consider Multiple Sources of Deterioration (Atlanta, Georgia, April 24, 2007), pp. 1-16, April 24, 2007
Lounis, Z.; Daigle, L. (2007c), "Project to provide risk-based life-cycle management of aging highway bridge decks," Construction Innovation, 12, (1), March, pp. 6, March 01, 2007
Lounis, Z.; Martin-Perez, B.; Hunaidi, O. (2001), "Decision support tools for life prediction and rehabilitation of concrete bridge decks," Supersized Session on Asset
Management, APWA International Public Work Congress (Philadelphia, PA, September 08, 2001), pp. 67-77, October 01, 2001
Lynch, J. P. (2007), “An overview of wireless structural health monitoring for civil structures.” Philos. Trans. R. soc. London, Ser. A, 365, 345–372.
Lynch, J. P., and Loh, K. J. (2006), “A summary review of wireless sensors and sensor networks for structural health monitoring.” Shock Vib., 38(2), 91–128.
Lynch, J. P., Law, K. H., Kiremidjian, A. S., Carryer, E., Kenny, T. W., Partridge, A., and Sundararajan, A., 2002, “Validation of a Wireless Modular Monitoring System for Structures,” in Smart Structures and Materials: Smart Systems for Bridges,
Structures, and Highways, San Diego, CA, March 17–21, Proceedings of the SPIE, Vol. 4696, No. 2, 17–21.
Lynch, J. P., Sundararajan, A., Law, K. H., Carryer, E., Farrar, C. R., Sohn, H., Allen, D. W., Nadler, B., and Wait, J. R., 2004, “Design and Performance Validation of a Wireless Sensing Unit for Structural Health Monitoring Applications,” Structural Engineering and Mechanics, Vol. 17, No. 3, 393–408.
Lynch, J. P., Sundararajan, A., Law, K. H., Kiremidjian, A. S., Carryer, E., Sohn, H., and Farrar, C. R., 2003, “Field Validation of a Wireless Structural Health Monitoring System on the Alamosa Canyon Bridge,” in Smart Structures and Materials: Smart
Systems and Nondestructive Evaluation for Civil Infrastructures, San Diego, CA, March 3–6, Proceedings of the SPIE, Vol. 5057, 267–278.
Malkawi, A.N.,Yi, Y.K. and Lewis, G. (2005), Integrated Evaluation of a Photovoltaic Installation, Journal of Architectural Engineering, Vol. 11, No. 4, (2005), 131–138 Mita, A. and Takahira, S., 2003, “A Smart Sensor Using a Mechanical Memory for
Structural Health Monitoring of a Damage-controlled Building,” Smart Materials and Structures, Vol. 12, No. 2, 204–209.
Mitchell, K., Rao, V. S., and Pottinger, H. J., 2002, “Lessons Learned About Wireless Technologies for Data Acquisition,” in Smart Structures and Materials 2002: Smart Electronics, MEMS, and Nanotechnology, San Diego, CA, March 18–21,
Proceedings of the SPIE, Vol. 4700, 331–341.
Mohamed, O., and Rens, K. L. (2001). “Ultrasonic testing of properties of 50-year-old concrete.” Materials Evaluation: An Official Journal of the American Society for Nondestructive Evaluation, (59)12.
Morcous, G.; Lounis, Z. (2006), "Management of bridge decks using stochastic
deterioration models and multi-criteria optimization," 7th International Conference on Short & Medium Span Bridges, (Montreal, QC. August 23, 2006), pp. 1-10
National Bridge Inventory (NBI) (2006). <http://www.fhwa.dot.gov/bridge/nbi.htm> (Nov. 2006)
Novak, L. J., Grizzle, K. M., Wood, S. L., and Neikirk, D. P., 2003, “Development of State Sensors for Civil Engineering Structures,” in Smart Structures and Materials: Smart Systems and Non-destructive Evaluation for Civil Infrastructures, San Diego, CA, March 3–6, Proceedings of the SPIE, Vol. 5057, 358–363.
Ou, J., Li, H., and Yu, 2004, “Development and Performance of Wireless Sensor Network for Structural Health Monitoring,” in Smart Structures and Materials, San Diego, CA, March 15–18, Proceedings of the SPIE, Vol. 5391, 765–773.
Pakzad, S. N., Fenves, G. L., Kim, S., and Culler, D. E. (2008). “Design and
implementation of scalable wireless sensor network for structural monitoring.” J. Infrastruct. Syst., 14(1), 89–101.
Pei, J-S., Kapoor, C., Graves-Abe, T. L., Sugeng, Y., and Lynch, J. P., 2005, “Critical Design Parameters and Operating Conditions of Wireless Sensor Units for Structural Health Monitoring,” in Proceedings of the 23rd International Modal Analysis
Conference (IMAC XXIII), Orlando, FL, January 31–February 3.
Phares, B.M., Rolander, D., Graybeal, B., Wahser, G. (2001), Reliability of Visual Bridge Inspection
Qian, S.Y. and Cusson, D. 2004. Electrochemical Evaluation of the Performance of Corrosion-Inhibiting Systems in Concrete Bridges, Cement & Concrete Composites, Vol. 26, pp. 217-233
