Monitoring and economical design of alternative energy generators such as wind turbines is becoming increasingly critical; however acquisition of the dynamic output data can be a time-consuming and costly process. In recent years, low-cost wireless sensors have emerged as an enabling technology for structural monitoring applications. In this study, wireless sensor networks are installed in three operational turbines in order to demonstrate their efficacy in this unique operational environment. The objectives of the first installation are to verify that vibrational (acceleration) data can be collected and transmitted within a turbine tower and that it is comparable to data collected using a traditional tethered system. In the second instrumentation, the wireless network includes strain gauges at the base of the structure. Also, data is collected regarding the performance of the wireless communication channels within the tower. In both turbines, collected wireless sensor data is used for off-line, output-only modal analysis of the ambiently (wind) excited turbine towers. The final installation is on a turbine with embedded braking capabilities within the nacelle to generate an "impulse-like" load at the top of the tower. This ability to apply such a load improves the modal analysis results obtained in cases where ambient excitation fails to be sufficiently broad-band or white. The improved loading allows for computation of true mode shapes, a necessary precursor to many conditional monitoring techniques.

The structural state of a bridge is currently examined by visual inspection or by wired sensor techniques, which are relatively expensive, vulnerable to inclement conditions, and time consuming to undertake. In contrast, wireless sensor networks are easy to deploy and flexible in application so that the network can adjust to the individual structure. Different sensing techniques have been used with such networks, but the acoustic emission technique has rarely been utilized. With the use of acoustic emission (AE) techniques it is possible to detect internal structural damage, from cracks propagating during the routine use of a structure, e.g. breakage of prestressing wires. To date, AE data analysis techniques are not appropriate for the requirements of a wireless network due to the very exact time synchronization needed between multiple sensors, and power consumption issues. To unleash the power of the acoustic emission technique on large, extended structures, recording and local analysis techniques need better algorithms to handle and reduce the immense amount of data generated. Preliminary results from utilizing a new concept called Acoustic Emission Array Processing to locally reduce data to information are presented. Results show that the azimuthal location of a seismic source can be successfully identified, using an array of six to eight poor-quality AE sensors arranged in a circular array approximately 200 mm in diameter. AE beamforming only requires very fine time synchronization of the sensors within a single array, relative timing between sensors of $1{\mu}s$ can easily be performed by a single Mote servicing the array. The method concentrates the essence of six to eight extended waveforms into a single value to be sent through the wireless network, resulting in power savings by avoiding extended radio transmission.

Accurate sensing of mechanical strains in civil structures is critical for optimizing structure reliability and lifetime. For instance, combined with intelligent control systems, electromechanical sensor output feedback has the potential to be employed for nondestructive damage evaluation. Application of Ionic Polymer Transducers (IPTs) represents a relatively new sensing approach with more than an order of magnitude higher sensitivity than traditional piezoelectric sensors. The primary reason this sensor has not been widely used to date is an inadequate understanding of the physics responsible for IPT sensing. This paper presents models and experiments defending the hypothesis of a streaming potential sensing mechanism.

We report on the development of a new technology for the fabrication of Micro-Electro-Mechanical-System (MEMS) strain sensors to realize a novel type of crackmeter for health monitoring of ageing civil infrastructures. The fabrication of micromachined silicon MEMS sensors based on a Silicon On Insulator (SOI) technology, designed according to a Double Ended Tuning Fork (DETF) geometry is presented, using a novel process which includes a gap narrowing procedure suitable to fabricate sensors with low motional resistance. In order to employ these sensors for crack monitoring, techniques suited for bonding the MEMS sensors on a steel surface ensuring good strain transfer from steel to silicon and a packaging technique for the bonded sensors are proposed, conceived for realizing a low-power crackmeter for ageing infrastructure monitoring. Moreover, the design of a possible crackmeter geometry suited for detection of crack contraction and expansion with a resolution of $10{\mu}m$ and very low power consumption requirements (potentially suitable for wireless operation) is presented. In these sensors, the small crackmeter range for the first field use is related to long-term observation on existing cracks in underground tunnel test sections.

Tube Lines has carried out a "knowledge and investigation programme" on the deep tube tunnels comprising the Jubilee, Northern and Piccadilly lines, as required by the PPP contract with London Underground. Many of the tunnels have been in use for over 100 years, so this assessment was considered essential to the future safe functioning of the system. This programme has involved a number of generic investigations which guide the assessment methodology and the analysis of some 5,000 individual structures. A significant amount of investigation has been carried out, including ultrasonic thickness measurement, detection of brickwork laminations using radar, stress measurement using magnetic techniques, determination of soil parameters using CPT, pressuremeter and laboratory testing, installation of piezometers, material and tunnel segment testing, and trialling of remote photographic techniques for inspection of large tunnels and shafts. Vibrating wire, potentiometer, electro level, optical and fibre-optic monitoring has been used, and laser measurement and laser scanning has been employed to measure tunnel circularity. It is considered that there is scope for considerable improvements in non-destructive testing technology for structural assessment in particular, and some ideas are offered as a "wish-list". Assessment reports have now been produced for all assets forming Tube Lines' deep tube tunnel network. For assets which are non-compliant with London Underground standards, the risk to the operating railway has to be maintained as low as reasonably practicable (ALARP) using enhanced inspection and monitoring, or repair where required. Monitoring techniques have developed greatly during recent years and further advances will continue to support the economic whole life asset management of infrastructure networks.

In the last decade, wireless sensor networks have emerged as a promising technology that could accelerate progress in the field of structural monitoring. The main advantages of wireless sensor networks compared to conventional monitoring technologies are fast deployment, small interference with the surroundings, self-organization, flexibility and scalability. These features could enable mass application of monitoring systems, even on smaller structures. However, since wireless sensor network nodes are battery powered and data communication is the most energy consuming task, transferring all the acquired raw data through the network would dramatically limit system lifetime. Hence, data reduction has to be achieved at the node level in order to meet the system lifetime requirements of real life applications. The objective of this paper is to discuss some general aspects of data processing and management in monitoring systems based on wireless sensor networks, to present a prototype monitoring system for civil engineering structures, and to illustrate long-term field test results.

Internationally the load carrying capacity of bridges is decreasing due to material deterioration while at the same time increasing live loads mean that they are often exposed to stresses for which they were not designed. However there are limited resources available to ensure that these bridges are fit for purpose, meaning that new approaches to bridge maintenance are required that optimize both their service lives as well as maintenance costs. Wireless sensor networks (WSNs) provide a tool that could support such an optimized maintenance program. In many situations WSNs have advantages over conventional wired monitoring systems in terms of installation time and cost. In order to evaluate the potential of these systems two WSNs were installed starting in July 2007 on the Humber Bridge and on a nearby approach bridge. As part of a corrosion prevention strategy, a relative humidity and temperature monitoring system was installed in the north anchorage chambers of the main suspension bridge where the main cables of the bridge are anchored into the foundation. This system allows the Bridgemaster to check whether the maximum relative humidity threshold, above which corrosion of the steel wires might occur, is not crossed. A second WSN which monitors aspects of deterioration on a reinforced concrete bridge located on the approach to the main suspension bridge was also installed. Though both systems have provided useful data to the owners, there are still challenges that must be overcome in terms of monitoring corrosion of steel, measuring live loading and data management before WSNs can become an effective tool for bridge managers.

A specific type of structural damage that would benefit from remote sensing is discussed and a solution is suggested.

Civil infrastructure, in both its construction and maintenance, represents the largest societal investment in this country, outside of the health care industry. Despite being the lifeline of US commerce, civil infrastructure has scarcely benefited from the latest sensor technological advances. Our future should focus on harnessing these technologies to enhance the robustness, longevity and economic viability of this vast, societal investment, in light of inherent uncertainties and their exposure to service and even extreme loadings. One of the principal means of insuring the robustness and longevity of infrastructure is to strategically deploy smart sensors in them. Therefore, the objective is to develop novel, durable, smart sensors that are especially applicable to major infrastructure and the facilities to validate their reliability and long-term functionality. In some cases, this implies the development of new sensing elements themselves, while in other cases involves innovative packaging and use of existing sensor technologies. In either case, a parallel focus will be the integration and networking of these smart sensing elements for reliable data acquisition, transmission, and fusion, within a decision-making framework targeting efficient management and maintenance of infrastructure systems. In this paper, prudent and viable sensor and health monitoring technologies have been developed and used in several large structural systems. Discussion will also include several practical bridge health monitoring applications including their design, construction, and operation of the systems.

Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.