Journal of Infrastructure Systems

There is currently an explosion of research on microsensor and wireless network technology, and methods of discerning structural state from such data. Besides the inherent interest in such fascinating technology, the question remains as to why an owner, operator, or user of the built environment should care. The construction industry is now adopting new types of contracting practice. Previously the contractor simply implemented a given design, but the current trend is for clients to commission certain performance requirements to be met with performance-based design. The emphasis of the industry is becoming the delivery of certain structural behavior states rather than simply building to a client’s set plans. The contracting process becomes the determination of the performance criteria, and delivery becomes a long-term fulfillment of these criteria. This can only take place if the performance states can be measured, and the measurement utilized in a decision-making process. The tools needed for both the evaluation of the delivery after construction and during operation are changing accordingly. The process is increasingly dependent on densely spaced sensor data, valid models to turn the data into physical behavior, and decision-making tools to determine whether the performance requirements are being met. This process must take place in order to satisfy all stakeholders. This paper describes the approaches in monitoring the performance, the obtaining of data and refining into information for decision making. Example cases concern the continuous performance and condition monitoring within buildings and civil structures, and indicators of the economical impacts of informed decisions including structural safety, hygrothermal, and energy aspects. A case study of structural health monitoring of the Golden Gate Bridge is also presented. The enabling sensor technology is described, and the exploitation of the data for life cycle needs is discussed.

Localization (determination of the position of each sensor node) of a large scale sensor network is an important issue for applying network sensing technique to infrastructure monitoring. This paper presents a concept and an implementation of the hierarchical localization method applicable for large scale sensor network. This system consists of parent nodes with a low priced L1 GPS receiver and child nodes with an acoustic ranging device. Relative positions between child nodes are estimated based on acoustic ranging through a distributed algorithm, called the inverse Delaunay algorithm. This algorithm localizes all the nodes simultaneously; thus, the accumulation of the error in the localization is suppressed. Relatively localized child sensor nodes are given global coordinates with the help of GPS on parent nodes. In spite of its reasonable cost, the robustness and the accuracy of GPS positioning is highly enhanced by using the “almost static” assumption, which is appropriate for infrastructure monitoring. This hierarchical localization method is implemented in a system consisting of off-the-shelf sensor platforms, and field experiments have been conducted for the performance evaluation of the system.

This paper presents a method, which can be implemented under limited resources such as wireless sensor network devices, for damage detection of civil structures. The detection method is applied to transient waves from vibration responses, and it consists of Haar wavelet decomposition, thresholding, quantization, and differentiating metric. Before the differentiating metric, a target signal is compressed into the wavelet coefficients “signature.” The results indicate that a single measurement channel without time synchronization of other measurement points is effective for instant damage detection through the case study using the ASCE structural health monitoring (SHM) benchmark program. It is also shown that we can reduce the data to a small fraction of the original data volume using this method, while retaining enough information to detect structural damage and its severity. The approach illustrated in this paper implies a promise of wavelet-based data compression and analysis for the increasing volume of SHM data.

The detection of abrupt changes in observed/estimated physical processes is an issue of concern in many applications. An algorithm for single change point detection in Gaussian white noise processes is presented in this paper. The algorithm progressively includes and analyzes the observed data points, and hence is suitable for detection of change point in on-line monitoring data. Two example problems are presented, wherein Monte Carlo simulation technique is used, to illustrate the proposed algorithm. The performance of the algorithm with respect to changes in shift amplitude, intensity of the signal, and rate of sampling are studied, and the observed trends indicate that the proposed algorithm possesses the desirable properties of change detection algorithms.

An objective of the structural health monitoring system is to identify the state of the structure and to detect the damage when it occurs. Analysis techniques for damage identification of structures, based on vibration data measured from sensors, have received considerable attention. Recently, a new adaptive damage tracking technique, based on the extended Kalman filter approach, has been proposed. Simulation studies demonstrated that the adaptive extended Kalman filter (AEKF) approach is capable of tracking the variations of structural parameters, such as the degradation of stiffness, due to damages. In this paper, we present experimental studies to verify the capability of the AEKF approach in identifying the structural damage by conducting a series of experimental tests using a small-scale three-story building model. Two types of excitations have been used, including the white noise applied to the top floor of the model and the earthquakes applied to the base. To simulate structural damage during the test, an innovative device is proposed in this paper to reduce the stiffness of some stories. Different damage scenarios have been simulated and tested. Measured response data and the AEKF approach are used to track the variation of stiffness during the test. The tracking results for the stiffness are also compared with the stiffness predicted by the finite-element method. Experimental results demonstrate that the AEKF approach is capable of tracking the variation of stiffness parameters leading to the detection of structural damage.

Inspecting and monitoring oil-gas pipelines, roads, rivers, and canals are very important in ensuring the reliability and life expectancy of these civil systems. An autonomous unmanned aerial vehicle (UAV) can decrease the operational costs, expedite the monitoring process, and be used in situations where a manned inspection is not possible. This paper addresses the problem of monitoring these systems using an autonomous UAV based on visual feedback. A single structure detection algorithm that can identify and localize various structures including highways, roads, and canals is presented in the paper. A fast learning algorithm that requires minimal supervision is applied to obtain detection parameters. The real time detection algorithm runs at $5\mathrm{Hz}$ or more with the onboard video collected by the UAV. Both hardware simulations and flight results of the vision-based control algorithm are presented in this paper. A fixed wing UAV equipped with a camera onboard was able to track a $700\mathrm{m}$ canal based on vision several times with an average cross-track error of around $10\mathrm{m}$ .

This paper reviews the development of energy harvesting for low-power embedded structural health monitoring (SHM) sensing systems. A statistical pattern recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized followed by a discussion of SHM sensor network paradigms, power requirements for these networks, and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. The paper concludes by defining some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes. Finally, it is noted that many of the technologies discussed herein are applicable to powering any type of low-power embedded sensing system regardless of the application.

A piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. A built-in active sensing system composed of two lead–zirconate–titanate patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole damage $0.5\mathrm{cm}$ in diameter at the web section and transverse cut damage $7.5\mathrm{cm}$ in length and $0.5\mathrm{cm}$ in depth at the head section). Two damage-sensitive features were separately extracted from each method: (1) Feature I: root-mean-square deviations of impedance signatures; and (2) Feature II: sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining appropriate damage indices from these two damage-sensitive features, a two-dimensional damage feature (2D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2D DF space. As a result, optimal separable hyperplanes were successfully established by the two-step SVM classifier: damage detection was accomplished by the first step SVM, and damage classification was carried out by the second step SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by 30 test patterns obtained in advance from the experimental study.

An integrated hardware and software system for a scalable wireless sensor network (WSN) is designed and developed for structural health monitoring. An accelerometer sensor node is designed, developed, and calibrated to meet the requirements for structural vibration monitoring and modal identification. The nodes have four channels of accelerometers in two directions and a microcontroller for processing and wireless communication in a multihop network. Software components have been implemented within the TinyOS operating system to provide a flexible software platform and scalable performance for structural health monitoring applications. These components include a protocol for reliable command dissemination through the network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. The prototype WSN was deployed on a long-span bridge with 64 nodes. The data acquired from the testbed were used to examine the scalability of the network and the data quality. Robust and scalable performance was demonstrated even with a large number of hops required for communication. The results showed that the WSN provides spatially dense and accurate ambient vibration data for identifying vibration modes of a bridge.

With recent advances in low-cost wireless sensing and data acquisition technology, it has become feasible to instrument a large structure with a dense array of wireless sensors. Furthermore, analog-to-digital conversion and data processing capabilities of current wireless sensor prototypes offer the ability to efficiently distribute data processing tasks across a large network of wireless sensing nodes. For decades, the structural engineering community has been adapting input-output modal identification techniques for use in large-scale civil structures. However, unlike in mechanical or aerospace engineering, it is often difficult to excite a large civil structure in a controlled manner. Thus, additional emphasis has been placed on developing a number of output-only modal identification methods for use in structural engineering applications. In this paper, three of these output-only methods (peak picking, random decrement, and frequency domain decomposition) are modified for implementation in a distributed array of processors embedded within a network of wireless sensor prototypes. The software architecture proposed emphasizes parallel data processing and minimal communication so as to ensure scalability and power efficiency. Using the balcony of a historic theater in metropolitan Detroit as a testbed, this network of wireless sensors is allowed to collect and process acceleration response data during a set of vibration tests. The embedded algorithms proposed in this study are used to autonomously determine the balcony’s modal properties with network-derived results found to be comparable to those derived from traditional offline techniques.