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Structural control of civil infrastructure in response to large external loads, such as earthquake or wind, is still not widely employed due to several key issues, such as latency in the system and challenges with information exchange. To promote information flow, wireless sensor networks have emerged as a potential solution that is also a low-cost alternative to the traditional wired sensing and actuation infrastructure. However, these systems also introduce additional challenges such as latency in the wireless communication channel and computational inundation at individual sensing nodes. Inspiration can be drawn from the real-time sensing and actuation capabilities of the biological central nervous system to overcome some of these challenges experienced by wireless sensor nodes. A novel bio-inspired wireless sensor node was developed that is capable of real-time time-frequency decomposition of a sensor signal, thus drawing inspiration from the frequency selectivity of certain neurons. Similar to the functionality of neurons, the node uses asynchronous sampling based on the content of the perceived signal, resulting in large power savings and compressed data communication. In this study, the bio-inspired wireless sensor node is utilized for a feedback control application in order to overcome the challenges currently seen in wireless control. The sensor node is able to transmit frequency- specific data in real-time to a controller node which constructs a control force using minimal computational resources. This study validates that performance of the bio-inspired wireless feedback control architecture on a one-story partial- scale shear structure that is seismically excited and controlled via active mass actuators.

While wireless sensor networks have been successfully deployed on a variety of civil infrastructure systems for structural monitoring, past studies have shown that there is room for improvement in terms of network robustness and overall resource consumption efficiency. The mechanisms employed by biological nervous systems (e.g. signal modulation, communication, and integration) can be used as inspiration for overcoming the performance bottlenecks seen in existing wireless sensor nodes and networks. The mammalian auditory system is of particular interest due to its unique signal decomposition techniques (performed by the cochlea) that enable real-time processing of complex sound signals. In this article, a novel wireless sensor architecture based on the operational principles of cochlea is described. The performance of the proposed sensor is validated on a single-degree-of-freedom structure that is excited by seismic ground motion signals, thus demonstrating its real-time monitoring capabilities while maintaining high data compression rates.

Wireless sensor networks (WSNs) have emerged as a reliable, low-cost alternative to the traditional wired sensing paradigm. While such networks have made significant progress in the field of structural monitoring, significantly less development has occurred for feedback control applications. Previous work in WSNs for feedback control has highlighted many of the challenges of using this technology including latency in the wireless communication channel and computational inundation at the individual sensing nodes. This work seeks to overcome some of those challenges by drawing inspiration from the real-time sensing and control techniques employed by the biological central nervous system and in particular the mammalian cochlea. A novel bio-inspired wireless sensor node was developed that employs analog filtering techniques to perform time-frequency decomposition of a sensor signal, thus encompassing the functionality of the cochlea. The node then utilizes asynchronous sampling of the filtered signal to compress the signal prior to communication. This bio-inspired sensing architecture is extended to a feedback control application in order to overcome the traditional challenges currently faced by wireless control. In doing this, however, the network experiences high bandwidths of low-significance information exchange between nodes, resulting in some lost data. This study considers the impact of this lost data on the control capabilities of the bio-inspired control architecture and finds that it does not significantly impact the effectiveness of control.

Civil structures, such as buildings and bridges, are constantly at risk of failure due to external environmental loads, such as earthquakes or strong winds. To minimize the effects of these loads, active feedback control systems have been proposed but such systems still face numerous challenges which impede their widespread adoption. In order to overcome many of these challenges, inspiration can be drawn from the signal processing and actuating techniques employed by the biological central nervous system to develop a bio-inspired control algorithm. In this study the front-end, signal processing techniques employed by biological sensory systems, and in particular the mammalian auditory system, are drawn upon in order to alleviate computations at the actuation node. This results in a simplistic control law that is a weighted combination of input information about the structure's response such that F = WN , where F is the applied control force, W is a pre-determined weighting matrix, and N is a deconstructed representation of the structural response to the applied excitation. There is no empirical solution for deriving an optimal weighting matrix, W , and in this study numerous methods are explored in order to determine values for this matrix that produce the most effective control. These methods include particle swarm optimization, artificial neural networks, and optimal control theory. The various weighting matrices are integrated into the proposed bio-inspired control algorithm and applied in simulation to a five story benchmark structure. These methods are also compared to a traditional linear quadratic regulator (LQR) to gain insight into the overall performance of the bio-inspired control algorithm. Of the three training techniques, the particle swarm optimization technique offers the most effective control which is comparable in performance to the traditional LQR.

Over the last several decades, wireless sensing technologies have arisen as an inexpensive and robust method of data collection, used in a variety of structural monitoring applications for various types of infrastructure systems. In comparison with their natural counterparts (i.e., cabled monitoring systems), wireless monitoring systems offer low-cost and low-power communication between a large number of sensing devices. However, successful implementation of such technologies is highly dependent on several factors, such as constant power supply, reliable communication, and dependable data collection strategies. These can often be challenging to achieve in real-world applications, due to harsh environmental conditions and overall limitations of the current technology. While numerous studies have effectively overcome such challenges in a variety of infrastructure systems, bridges have had numerous successful deployments of wireless sensor networks. As such, this chapter focuses on four particular cases that demonstrate successful long-term deployment of wireless sensor networks on bridges for structural monitoring purposes.

A dichotomy exists in structural monitoring installations: those with sensors tethered to the data repository with wires, and those which use wireless communication to create a distributed network of sensors. This chapter discusses the impact of the paradigm shift from the wired monitoring systems traditionally used, to the wireless systems recently developed. This new technology is capable of achieving effective measurements on par with its predecessor, and introduces new possibilities of large-scale networks of hundreds of sensors made possible by low installation costs and scalable in-network data processing. Design and selection considerations covered in this chapter include network architecture, wireless node hardware, and distributed embedded software. Although rapidly maturing and reaching commercialization, research opportunities still exist in the fields of data and power management, which are discussed as the chapter culminates.

While sensing technologies for structural monitoring applications have made significant advances over the last several decades, there is still room for improvement in terms of computational efficiency, as well as overall energy consumption. The biological nervous system can offer a potential solution to address these current deficiencies. The nervous system is capable of sensing and aggregating information about the external environment through very crude processing units known as neurons. Neurons effectively communicate in an extremely condensed format by encoding information into binary electrical spike trains, thereby reducing the amount of raw information sent throughout a neural network. Due to its unique signal processing capabilities, the mammalian cochlea and its interaction with the biological nervous system is of particular interest for devising compressive sensing strategies for dynamic engineered systems. The cochlea uses a novel method of place theory and frequency decomposition, thereby allowing for rapid signal processing within the nervous system. In this study, a low-power sensing node is proposed that draws inspiration from the mechanisms employed by the cochlea and the biological nervous system. As such, the sensor is able to perceive and transmit a compressed representation of the external stimulus with minimal distortion. Each sensor represents a basic building block, with function similar to the neuron, and can form a network with other sensors, thus enabling a system that can convey input stimulus in an extremely condensed format. The proposed sensor is validated through a structural monitoring application of a single degree of freedom structure excited by seismic ground motion.

Damage detection on engineered systems is a challenging task that has been explored by numerous researchers. In recent years wireless sensors systems have arisen as a vehicle for low-power, low-cost, and localized damage detection that can be applied to various structural systems. Such sensors, however, are limited in their computational capacity and as a result, careful consideration must be taken as to which algorithms can be effectively embedded so as to balance energy constraints with computational efficiency. In this study, two classifier algorithms (least squares classifier and Fisher's linear discriminant analysis) are explored for detecting damage on a cooling system test bed. In particular, the algorithms are used to determine the valve configuration of the system and to verify if damage exists within the valves. To validate the efficiency of the algorithms in the embedded domain, the algorithms are implemented on a wireless sensing network and used to classify the system state of the test bed.

Wireless sensing technologies have recently emerged as an inex pensive and robust method of data collection in a variety of structural monitoring applications. In comparison with cabled monitoring systems, wi reless systems offer low-cost and low-power communication between a network of sensing devices. Wireless sensing networks possess embedded data processing capabilities which allow fo r data processing directly at the sensor, thereby eliminating the need for the transmission of raw data. In this study, the Volterra/Weiner neural network (VWNN), a powerful modeling tool for non-linear hysteretic behavior, is decentralized for embedment in a network of wirele ss sensors so as to take advantage of each sensor’s processing capabilities. The VWNN was chosen for modeling nonlinear dynamic systems because its architecture is computationally efficient and allows computational tasks to be decomposed for parallel execution. In the algorithm, each sensor collects it own data and performs a seri es of calculations. It then shares its resulting calculations with every other sensor in the network, while the other sensors are simultaneously exchanging their information. Because resource conservation is important in embedded sensor design, the data is pruned wh erever possible to eliminate excessive communication between sensors. Once a sensor has its required data, it continues its calculations and computes a prediction of the system acceleration. The VWNN is embedded in the computational core of the Narada wireless sensor node for on-line execution. Data generated by a steel framed structure excited by seismic ground motions is used for validation of the embedded VWNN model. Keywords: distributed computing, embedded neural networks, wireless sensor networks, structural health monitoring

Structural monitoring for civil infrastructure is a rapidly developing field that has made significant advancements over the last decade. However, a number of performance bottlenecks remain including challenges with cost-effectively scaling monitoring systems up to large nodal counts. Due to the many parallels between biological sensory systems and engineered sensing systems, the biological nervous system can offer potential solutions to the current deficiencies of structural monitoring systems. The nervous system is capable of real-time processing and data transmission of external stimuli through an extremely condensed format with very basic processing units. This study explores the mammalian auditory system for inspiration because it achieves efficient data acquisition processes that outperform existing engineered sensing systems. Specifically, the auditory system realizes this through three steps: (1) real-time decomposition of a convoluted time-based signal into frequency components, (2) information compression for each component, and (3) efficient high-speed data transmission to the auditory cortex. In this paper, these three main mechanisms are explored and a bio-inspired structural monitoring system is proposed. The functionality of the proposed system is compared to traditional data compression techniques (wavelet transforms and compressed sensing) on various vibratory signals. While the wavelet transform is able to outperform the proposed sensor by minimizing signal reconstruction errors, the proposed bio-inspired sensor achieves similar compression rates but, unlike the others, does so using real-time processing. (Some figures may appear in colour only in the online journal)