Wireless Structural Health Monitoring of Bridges: Current ... · Wireless Structural Health Monitoring of Bridges Page 1 Hoult Wireless Structural Health Monitoring of Bridges: Current

Wireless Structural Health

Monitoring of Bridges Page 1 Hoult

Wireless Structural Health Monitoring of Bridges: Current Challenges and Future Innovations

Neil A. Hoult, Research Associate, University of Cambridge Paul R.A. Fidler, Computer Associate, University of Cambridge

Campbell R. Middleton, Senior Lecturer, University of Cambridge

Synopsis In order to ensure that bridges continue to provide strong links between communities both now and well into the future, effective bridge maintenance schemes must be used. Monitoring can be an important tool in such a scheme although it is generally reserved for high profile bridges because the high cost of the wired systems currently used precludes their pervasive use. The development of Wireless Sensor Networks (WSNs) and microelectromechanical systems (MEMS) sensors could allow inexpensive monitoring systems to be installed and maintained. This paper will introduce some of the technology in terms of hardware and sensors that is available including WSNs that are designed for both long-term deterioration monitoring and short-term high speed data acquisition. Then, using a WSN that has been installed for long-term monitoring of a three-span reinforced concrete (RC) bridge as a case study, some of the current challenges including battery life, wireless connectivity and system cost will be discussed. The future use of WSNs will be investigated by examining sensors that are being developed and how power harvesting might be able to eliminate the need for battery changes. Introduction Globally ever-increasing loading and fatigue demands are being placed on bridge networks. This is especially true in Australia where the widespread use of road trains has meant that a single vehicle can embody both of these increased requirements. At the same time deterioration means that the capacity of these bridges is constantly being reduced. There is also a limited budget available to maintain, repair or replace these structures. In order to effectively manage this situation, engineers and managers require up-to-date information about the condition of the bridge stock so that optimised decisions can be made about each asset. This will result in, ideally, maximised network capacity for minimum expenditure allowing Australia’s and New Zealand’s bridges to continue to be strong links between communities. One way to provide engineers and managers with this up-to-date information is to install a monitoring system. However, traditional wired monitoring systems can cost approximately A$6000 per sensor including installation costs of A$3000 (Çelebi 2002). Thus a significant investment is required even if only a few parameters of the bridge are needed to properly evaluate its condition. Another possibility is to install a wireless sensor network (WSN), which has potentially lower installation and individual sensor costs. However, WSNs have their own problems relating to radio connectivity and battery life that may make them unsuitable for some applications, at least in the near term.



This paper will provide a brief introduction to WSN technology both in terms of the hardware and sensors available. Some of the current challenges with these systems in terms of power and radio connectivity will be highlighted drawing upon experience from a WSN deployment on a three span reinforced concrete (RC) bridge in the UK. A short discussion of the costs associated with these systems will underscore the economic feasibility of using WSNs as part of a bridge management strategy both now and in the future. Finally some of the technologies that are currently being developed in terms of both sensors and power harvesting will be presented in order to illustrate some of the future innovations that are possible with these systems. Wireless Sensor Networks At their most general level WSNs consist of nodes and a gateway as illustrated in Figure 1. Each node has some level of computing power in the form of a central processing unit (CPU) and a radio. In many applications the nodes are also battery powered because access to mains power is difficult or impossible. The nodes may also have sensors attached allowing for data about a particular parameter to be acquired. The gateway, as the names suggests, connects the nodes of the WSN to the outside world through either Internet connectivity, some form of accessible memory or both. It typically uses a more powerful CPU and, unlike the nodes, is often mains powered. There are several different WSN topologies (how each node in the network communicates with other nodes of the gateway) that are possible and which one is appropriate depends on a number of variables. For example, a ‘star’ topology, as depicted in Figure 1(a), occurs when each node in the network connects only to the gateway (Crossbow 2007). This is an effective topology when, for example, the nodes are approximately equidistant from the gateway. A network where all the nodes in the network can transmit to one another as well as the gateway is said to have a ‘mesh’ topology (Crossbow 2007) as illustrated in Figure 1(b). This is an effective topology when all the nodes are quite close to each other as it allows each node to establish the best connection between itself and the gateway. It also potentially allows for more than one transmission route to the gateway should a node stop operating for any reason providing redundancy in the network and increasing reliability.

(a) Star topology (b) Mesh topology

Figure 1 – WSN topologies



Data Requirements and Hardware There are typically two potential WSN applications: low data rate and high data rate. Low data rate applications occur when the value of the variable being measured does not change very often. An example of this type of application is monitoring the relative humidity (RH) and temperature within a room such as in the anchorage chamber of a suspension bridge (Hoult et al. 2008). In this case the nodes do not have to have significant computational capacity or memory. Also, because the nodes are not required to sample data continuously they also do not need to run constantly at full power, which can significantly extend the life of the batteries. Power will be discussed in greater detail later. High data rate applications include, for example, structural vibration measurement (Lynch et al. 2006) and water pipeline pressure transient monitoring (Stoianov et al. 2007). The increased rate of data acquisition places additional computational, radio transmission and power requirements on the node. These three requirements are also interrelated. For example, one possibility is to transmit all the data as it is acquired but this is limited by the speed at which data can be transmitted at a given radio frequency and also uses a considerable amount of power. Processing the data so that less of it needs to be transmitted lowers power and bandwidth requirements. But the low-power CPUs on the node have limited computational capacity and may not be able to perform the required processing. Some researchers (Stoianov et al. 2007) overcome the computational requirements by using a more powerful CPU. However that in turn further increases the power requirements reducing the potential battery life. Other researchers have used triggering techniques (Feltrin et al. 2007) whereby data is only acquired at a high data rate for a brief period when an event occurs and the node remains in a low-power mode at other times to reduce power demands. However, this only works for certain applications. In other cases it has been suggested that the ease of deployment of WSNs means that the system can be installed, the necessary data acquired and then the system can be taken away again in order to overcome the power limitations (Lynch et al. 2006). Thus the parameter being measured will govern the choice of data acquisition rate for the WSN but there is still a complex optimization exercise that needs to be performed before the correct WSN hardware can be chosen. Later in this paper a low data rate network intended for long-term monitoring and some of the issues surrounding that type of network will be introduced. Sensors One recent technological development that has made WSNs a feasible alternative to wired systems is the maturity of microelectromechanical systems (MEMS) based sensors. MEMS sensors have two advantages over the sensors that have traditionally been employed in wired monitoring systems: reduced size and energy consumption. The reduced size of these sensors makes them more appealing for use in applications with limited clearances (e.g. on tunnel walls and on the soffit of low bridges) as well as making them less noticeable, which reduces the susceptibility of the system to vandalism. The lower energy consumption allows the battery life of an individual sensor node to be extended. Currently a wide variety of environmental parameters such as temperature, relative humidity (RH), barometric pressure, light



and sound can be measured using MEMS sensors. There are also sensors available to measure physical parameters such as acceleration, pressure and inclination. However strain and displacement, which are often critical parameters for determining the performance of a structure, cannot yet be measured using a commercially available MEMS sensor. In the “Future Innovations” section of this paper the possibility of a MEMS-based strain sensor will be discussed. Currently researchers attach more conventional sensors to the sensor nodes. For example, Nagayama et al. (2004) have interfaced a node with a foil strain gauge. The Ferriby Road Bridge deployment, which is discussed in the next section, employs linear potentiometric displacement transducers (LPDTs) to measure changes in displacement. Ferriby Road Bridge Deployment As part of a larger project investigating the possibility of using WSNs for pervasive monitoring of civil infrastructure, a WSN was installed on a three span reinforced concrete slab bridge in the UK known as the Ferriby Road Bridge, which is illustrated in Figure 2. The bridge serves as the northern approach span to the Humber Bridge, the UK’s longest suspension bridge.

Figure 2 – Ferriby Road Bridge A 2002 visual inspection of the Ferriby Road Bridge noted that there were cracks in the soffit of the bridge running transverse to the span at mid-span and parallel to the span between the piers. The same inspection also noted that the elastomeric bearings at the abutments had a slight inclination transverse to the span of the bridge. Although neither the cracking nor the bearing inclination required intervention at the time, it was possible that these issues could worsen over time and eventually require remediation. In order to supplement the visual inspection and determine the rate of deterioration, a seven node WSN, as illustrated in Figure 3, using the Crossbow MICAz mote platform was installed in March 2008. Three LPDT nodes (Nodes 1 to 3) as shown in Figure 4(a) were installed on the soffit of the bridge to measure changes in crack width. Three inclinometer nodes (Nodes 4 to 6) as illustrated in Figure 4(b) were installed on the elastomeric bearings at the top of the southwestern and southeastern-most piers. Two of these nodes measured bearing inclination transverse to the span and one measured bearing inclination parallel to the span. The LPDT and inclinometer nodes had sensors to measure the RH and temperature both inside and outside their protective enclosures. A final node measures temperature inside the box containing the gateway. Another feature of this network was that the gateway was not only connected to the Internet via a mobile phone modem but was powered by a battery that was recharged by a solar panel. This



meant that the entire network was, in fact, wireless. This installation will be used as an example to facilitate the discussion in the “Current Challenges” section.

Figure 3 – Ferriby Road Bridge WSN layout

(a) LPDT node (b) Inclinometer node

Figure 4 – Sensor nodes used at Ferriby Road Bridge Current Challenges As with all emerging technologies there are a variety of obstacles that WSNs must overcome before they can be considered a useful tool for bridge management. Reliability and robustness of the hardware and software, especially in the case of long-term deployments, is one such issue that has to be resolved (Hoult et al. 2008). As the technology matures it is anticipated that those types of problems will become less critical but three of the current challenges that will always need to be dealt with are power, radio connectivity and cost. Power One of the main features of a WSN node is that it can be installed without the requirement for cabling to provide connectivity for data or power. However, without a mains power supply the node can only operate for a limited amount of time before a battery change is required. For high data rate networks this length of time can be quite short, sometimes as little as a few days (Lynch et al. 2006). For low data rate networks the period between battery changes can be increased to months or even years (Hoult et al. 2008). The Ferriby Road Bridge deployment employed long life lithium batteries to provide power for each node. Using 3.6V – 35Ah batteries it is



calculated that the LPDT nodes can operate for 4 years. Similarly the 3.6V – 19Ah batteries should allow the inclinometer nodes to operate for 1.5 years. However it is unlikely these estimates will be entirely accurate for a number of reasons. First of all, they are based on current measurements taken from individual nodes outside of the network. Once the nodes are in a network there will be additional power requirements resulting from forwarding data from other nodes in the network to the gateway but until the network is formed it is difficult to know which nodes will have the highest data forwarding demands placed on them. Second, the calculated battery life expectancy was based on the manufacturer’s specifications at an operating temperature of 25°C whereas the actual operating temperature of the batteries has varied over time with the average being 11.8°C. Whilst this should not have a significant effect on the life expectancy it will nevertheless reduce it. Even if the estimates about battery life are accurate, this still means that the batteries will have to be changed in the inclinometer nodes once every 1.5 years with all the associated time, man power and equipment costs. Another consideration is the size of the power source. For the LPDT nodes the batteries had the same form factor as a ‘DD’ battery and the inclinometers had ‘D’ size batteries as indicated in Figures 3(a) and (b) respectively. This constrains the overall size of the node as they must be at least as big as the batteries even when very small MEMS sensors are used. This was not a significant issue at the Ferriby Road Bridge where the soffit of the bridge was 8m off the ground thus minimizing any clearance and vandalism issues. However on structures where clearance is an issue or where the nodes are accessible the smallest possible node size is preferable. A possibility for overcoming the limitations of batteries as a power source (i.e. limited lifespan and size) is to employ some form of power harvesting technique, which will be dealt with in greater detail in the “Future Innovations” section. Radio Connectivity When the Ferriby Road Network was first installed the gateway could only communicate with nodes 1, 4 and 7 and not nodes 2, 3, 4, 5 and 6. This problem was solved by installing larger external antennas on nodes 3, 5 and 6 as well as on the gateway. In this case the solution to this problem is not as instructive to managers and engineers as the possible causes of these problems. Radio connectivity can be affected by a number of problems including the antenna hardware, the transmission frequency, the node hardware and software (the MICAz and TinyOS in this case), the material composition of the surrounding structure, the spacing between the antenna and the structure, and fading. Although each one of these issues can have an affect, the last two are believed to have impacted the performance of the Ferriby Road Bridge WSN and are problems that could affect any such WSN installation. As such they will be discussed in greater detail. WSN installations on civil infrastructure, as noted earlier, are often subject to tight clearance requirements, which limit the distance between the surface of the structure and the antenna. However, work by Wu et al. (2008) has shown that this antenna-wall offset can have an affect on the transmission distance in the direction parallel to the wall as illustrated in Figure 5.



Figure 5 – Effect of antenna-wall offset distance of transmission distance from Wu et al. (2008) In Figure 5 the face of the structure is represented by a vertical line extending from 90° to -90° and the transmission distances are given for a variety of structural material types (i.e. metal, concrete and plastic). It can be seen from this figure that for antenna-wall offset distances less than 31.25mm, the transmission distance parallel to the face of the structure is reduced in comparison to offset distances greater than this value. This effect is believed to have been part of the reason nodes 2 and 3 could not communicate with the gateway. As suggested earlier this was only overcome by installing more powerful antennas and in the case of node 2, increasing the offset distance between the soffit of the bridge from approximately 15mm to approximately 75mm. Another important result to note is the presence of areas of reduced transmission strength over the full 180° transmission range when the offset distance is greater than 62.5mm for metallic structures. If WSNs are to become truly pervasive, it is likely that they will be installed by technicians with limited or no expertise in radio wave propagation theory and so potential problems such as these need to be identified in advance and steps taken to mitigate them. Thus one of the goals of the current work is to make engineers and managers aware of these issues. Another problem that affects any type of radio transmission is the phenomenon known as fading. Because radio waves take several different paths to get from one point to another, there is the potential for multiple waves originating at the transmitter to cancel each other out at the receiver because they arrive at slightly different times. This destructive interference can result in significant reductions in transmission strength within localized areas. In the case of the Ferriby Road Bridge it is possible that fading was one of the reasons for the lack of network connectivity. To overcome the effects of fading one can either increase the transmission strength or the receive

(d) 62.5mm offset (f) 125mm offset (g) 250mm offset

(a) 6mm offset (b) 20mm offset (c) 31.25mm offset



sensitivity, move the node or switch the frequency at which the nodes communicate. One might also consider determining the position of the fades in advance by taking measurements and avoiding those areas. However it is difficult to determine the position of fades using measurements as the position is affected by everything in the surrounding environment including the presence of the measurement equipment. Cost The cost advantages of WSNs could prove advantageous for both short and long-term monitoring. For short-term monitoring (e.g. determining the effects of nearby construction on a structure) the installation cost is the most significant factor as the capital cost can potentially be recovered through frequent reuse of the system. Çelebi (2002) estimated that the installation cost for a wired acceleration monitoring system including cabling was approximately A$3000 per sensor node (this is exclusive of the hardware costs). At the Ferriby Road Bridge it took a team of two approximately one hour to install each node (exclusive of the additional time to fix the radio connectivity issues already discussed). Thus the approximate cost of installing a single node is A$40 (based on an average salary of A$40585 from the Australian Bureau of Statistics (2008) and a 2000 hour work year). As was mentioned earlier, battery changes will also be required for the WSN and so that will add an additional maintenance cost unless some form of power harvesting can be developed, as discussed in the “Future Innovations” section. This estimate is, of course, not meant to be a completely accurate comparison as it is comparing two different monitoring systems, however it does offer an indication of the savings that are possible if WSN technology is used for short-term monitoring. In terms of long-term monitoring, wired monitoring systems are only installed on bridges that have identified deficiencies as their cost precludes their use in pervasive monitoring. Whilst the situation is unlikely to change in the near future, it is possible that if the capital and installation costs of monitoring systems could be reduced significantly then they could be installed as part of regular visual inspection programs and thus create a state of pervasive monitoring. The installation costs would be the same as those discussed for short-term monitoring. Çelebi (2002) estimated the hardware cost per node at A$1500 for the sensor hardware and A$1500 for the associated data acquisition hardware for a wired acceleration monitoring system. The hardware used at the Ferriby Road Bridge cost approximately A$150 for the node (the MICAz) plus an addition A$300 for the sensors. The gateway computer used is not commercially available but its estimated cost is approximately A$900, which works out to be a cost per node of about A$130. Thus the total cost per node is A$580 versus A$3000 for the wired system. However, this comparison is not entirely accurate as the cost quoted for the wired system is for a high-accuracy, high data rate acceleration monitoring system. This means that the hardware required for the two systems is entirely different. Perhaps the more important conclusion is that neither system appears to cost-effective enough for pervasive use. Even WSNs, which seem to be more cost-effective, are still several orders of magnitude more expensive per node than is required for pervasive monitoring. At the same time it seems that there are savings to be had for any monitoring application if WSNs are used and so they should be considered as possible replacement for wired monitoring systems.



Future Innovations Sensors Earlier one of the current challenges that was highlighted was the need for MEMS sensors capable of measuring strain and displacement. MEMS strain sensors have existed for several years in the research community. For example, Wojciechowski et al. (2005) developed a vibration-based strain sensor with a gauge length of 200µm and a resolution of 33 nano-strain. But that study was lab-based and chiefly involved the calibration of the sensor. More promisingly, Obadat et al. (2003) detailed the field testing of a biaxial MEMS strain transducer for estimating the fatigue life of railroad tracks. However, one of the major stumbling blocks with their ‘BiAST’ sensor was that it was very difficult to attach to the railway tracks because the small size of the sensor made it hard to handle and limited the available area for bonding. Unfortunately these strain sensors have yet to become widely available commercially. Despite this there exist a number of potential benefits of using these sensors as part of WSN for structural monitoring including low power consumption, small size and high strain resolution if and when they become commercially available. Another sensor that could be useful in terms of monitoring structural deterioration is a camera. Currently the available camera nodes for WSNs have limited resolution, both commercially (e.g. the Crossbow IMB400 multimedia sensor board at 0.3MPixels) and in the research community (e.g. Chen et al. 2008 at 1.3MPixels), in comparison to available digital cameras (12MPixel resolution is currently available at present). However, this level of resolution may be adequate depending on what is to be monitored. For example, large-scale indicators of deterioration (e.g. spalling or efflorescence) could be detected by such images. Similarly an area of 128mm by 102mm could be monitored for cracks as small as 0.1mm with the device developed by Chen et al. However, deterioration and crack development could be tracked with greater resolution over larger areas if a higher resolution camera was available. The problem with moving to a camera with higher resolution is that the requirements in terms of power and bandwidth also increase. This can be offset by only transmitting what has changed between images and decreasing the sampling rate. To date most of the work in this area has looked at using camera nodes and WSNs for surveillance, and so while image comparison has been a research topic, low sampling rates have not (a camera that only takes pictures once a day being of limited use for surveillance). However structural monitoring would seem to be a better application for camera nodes due to the more efficient use of battery power. For example, the node developed by Chen et al. only lasts 16 hours when operated continuously for surveillance and powered by 4-AA batteries. If instead it was only acquiring and analyzing one or two images a day the time between battery changes could be extended significantly. Infrared thermography is another potential application of a camera node. In this case the differences in surface temperature on a structure can be used to isolate deterioration such as delamination in concrete bridges and the presense of moisture in masonry structures (e.g. Clark et al. 2003).



Power Harvesting One potential way to avoid the use of large batteries with finite operational lives is to use some form of sustainable energy supply or power harvesting. Taneja et al. (2008) deployed a WSN for environmental monitoring where the individual nodes were solar powered. However they discovered that while in an urban environment the solar energy was sufficient to power the nodes, in a forested area (the intended deployment area) the amount of solar energy was insufficient due to shading from the trees. Similar results were observed at the Ferriby Road Bridge where the solar panel supplied the gateway with enough power from March until November; however the battery capacity was exceeded at the beginning of December. This was somewhat surprising since the average power supplied by the solar panel was estimated at approximately 264Wh/day (based on a solar incident flux of 110W/m2, a 1m2 solar panel area and a solar panel efficiency of 10% as given by MacKay 2009) whereas the demand from the gateway was measured in the lab at approximately 72Wh/day. However there were several variables that could not be accounted for in this calculation. First of all, the exact power demand of the gateway could not be estimated due to the mobile phone modem. It is possible that in the lab the modem had to use relatively little power to obtain a connection to the nearest mobile phone relay whereas at the Ferriby Road Bridge more power may be required to make the same connection. Another consideration is that 264Wh/day estimate is an average value for the year. Using values for the month of November instead (based on a solar incident flux of approximately 30W/m2 given by MacKay 2009) the power supplied by the panel is approximately 72Wh/day. This suggests that supply almost exactly equals demand and would mean that if the level of solar incident flux was even slightly less than predicted the battery would begin to drain. These two examples illustrate one of the problems with power harvesting and that is its variability. Another problem is that these approaches tend to deliver fairly small amounts of power (the solar panel at the Ferriby Road Bridge is approximately 1m2 and yet it delivers as little as 3W of power). Thus WSN networks based on such solutions will not be able to maintain high sampling rates and may be subject to periods of inoperability due to the variability of the power supply. In the future these solutions may become more refined and thus more practically useful. As well, only solar power has been dealt with here as it is the most mature of the power harvesting technologies. It is possible that other techniques, such as vibration harvesting or micro-wind turbines, may prove to be more suitable depending on the application once they become commercially available. Conclusions Wireless sensor networks offer a more cost-effective alternative to conventional wired monitoring systems making them an appealing option. There are a variety of WSN technologies available and which one is appropriate depends on the monitoring application. However, there are issues in terms of radio connectivity as well as battery life and battery size that must be taken into consideration before choosing a particular WSN solution. In the future WSNs may offer a more complete solution as developments in terms of both sensors (including strain gauges and cameras) and power harvesting eliminate some of the current constraints.



Acknowledgements The authors wish to thank the EPSRC for their generous financial support of this project as well as Peter Hill and the Humber Bridge Board. The authors also are indebted to Peter Bennett, Yusuke Kobayashi, Tom Sanderson and Martin Touhey for their help with the installation as well as Ian Wassell and Yan Wu for providing Figure 4. References Australian Bureau of Statistics. (2008). “National Regional Profile: Australia.” <http://www.abs.gov.au/AUSSTATS/[email protected]/Latestproducts/0Economy12002-2006?opendocument&tabname=Summary&prodno=0&issue=2002-2006>. Accessed on January 14, 2009. Çelebi, M. (2002). Seismic Instrumentation of Buildings (with Emphasis on Federal Buildings). United States Geological Survey, Menlo Park, USA. Chen, P., Ahammad, P., Boyer, C., Huang, S-I., Lin, L., Lobaton, E., Meingast, M., Oh, S., Wang, S., Yan, P., Yang, A.Y., Yeo, C., Chang, L-C., Tygar, J.D., and Sastry, S.S. (2008). “Citric: A low-bandwidth wireless camera network platform.” Proceedings of the 2nd ACM/IEEE International Conference on Distributed Smart Cameras, Stanford, USA, 10pp. Clark, M.R., McCann, D.M. and Forde, M.C. (2003). “Application of infrared thermography to the non-destructive testing of concrete and masonry bridges.” NDT and E International, 36(4), 265-275. Crossbow. (2007). XMesh User’s Manual. Crossbow, San Jose, USA. Feltrin, G., Meyer, J. and Bischoff, R. (2007). “Wireless sensor networks for long term monitoring of civil structures.” Proceedings of the 2nd International Conference on Experimental Vibration Analysis for Civil Engineering Structures, Porto, 1, 95–111. Hoult, N.A., Fidler, P.R.A., Wassell, I.J. and Middleton, C.R. (2008). “Wireless Structural Health Monitoring at the Humber Bridge.” Proceedings of ICE, Bridge Engineering, 161(BE4), 189-195. Lynch, J.P., Wang, Y., Loh, K.J., Yi, J-H. and Yun, C-B. (2006). “Performance monitoring of the Geumdang Bridge using a dense network of high-resolution wireless sensors.” Smart Materials and Structures, 15(6), 1561-1575. MacKay, J.C. (2009). Sustainable Energy – without the hot air. UIT Cambridge Ltd, Cambridge, UK.



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Wireless Structural Health Monitoring of Bridges: Current ... · Wireless Structural Health Monitoring of Bridges Page 1 Hoult Wireless Structural Health Monitoring of Bridges: Current