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Instrumentation plan for 56 nodes on main span of the Golden Gate Bridge 

Instrumentation plan for 56 nodes on main span of the Golden Gate Bridge 

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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...

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... ticks, event handling, or other hardware bias. Spatial jitter is the time-synchronization error between different nodes, which occurs because of uncertainty in estimating latency in propagating a global time across the network and the drift of the clock at a node. To provide time synchronization in a network by controlling jitter, a TinyOS component named flooding time-synchronization protocol ͑ FTSP ͒ is used. FTSP propagates the global time generated by the base station through the network by a series of hand- shakes between adjacent nodes, until the entire network is in the same time zone. Experiments by Maróti et al. ͑ 2004 ͒ show that the protocol limits the spatial jitter to 67 ␮ s over a network of 59 nodes and 11 hops. High-frequency sampling and logging can increase this jitter, so Kim et al. ͑ 2007 ͒ performed a jitter analysis and the tests showed that the temporal jitter using the Timer component of TinyOS is limited to 10 ␮ s for a sampling rate of up to 6.67 kHz. For a harmonic signal of 25 Hz, the highest frequency of interest in the current application, this time synchronization error causes a maximum 0.16% error in the measured value of the signal. For an ambient acceleration signal of 10 m g amplitude, the jitter is equivalent to 16 ␮ g noise level, which falls below the sensitivity of the MEMS accelerometers. The jitter analysis of the nodes with FTSP provided insight into the limitations of the motes for a WSN in structural health monitoring. While the MicaZ microcontrollers are faster than the flash memory, other microcontroller tasks are delayed because of sampling, which is a time-consuming operation and thus blocking computation and communication. Using multiplexed ADC with its own clock would marginally limit temporal jitter, but at the cost of consuming additional power for the separate clock. A faster microcontroller would have smaller jitter but still have the same fundamental problem that would need to be addressed. For structural health monitoring, Kim et al. ͑ 2007 ͒ developed an application named Sentri, based on TinyOS, for high-level control of a wireless network from a base station. The control program consists of two components: one for the individual nodes and the second one for the base station. The node software allows the mote to listen to the network, join the network, control the sensor board ͑ sampling, filtering, logging ͒ , and be a sender/receiver for multihop communication. It is designed for a very small memory footprint because of the limited resources for the motes. The base station control software has more functionality for sending inquir- ies to all nodes in the network, evaluating connectivity and communications, and executing commands on parts or the entire network. The sensor nodes, network, and the system software were tested by several laboratory and field experiments to determine sensitivity of the sensors, communication reliability and bandwidth, and the robustness of the system components. Pakzad et al. ͑ 2005 ͒ describe these tests, which included quiet-environment tests to determine the noise floor of the MEMS accelerometers, shaking table tests to asses the accuracy of the sensors over a wide frequency range, and a variety of field tests to study the multihop networking and reliable data collection components. Each sensor node was individually calibrated by a rotary tilt table and shift and scale factors for converting the ADC output to acceleration were determined for all channels. Long-span, suspension bridges have been the subject of study for structural health monitoring because they are important physical infrastructure and can have unique vibration properties and response to earthquake ground motion. As two examples of studies using data from wired sensors, Smyth et al. ͑ 2003 ͒ examined the Vincent Thomas Bridge in the 1987 Whittier and 1994 Northridge earthquakes with linear and nonlinear system identification techniques to develop a multiinput, multioutput dynamic model of the bridge using data from 26 accelerometer sensors on the superstructure and the footings. Abdel-Ghaffar and Scanlan ͑ 1985a,b ͒ used spectral densities and ambient vibration data caused by wind and traffic and collected at 28 locations on the span and a tower of the Golden Gate Bridge to estimate vibration frequencies and mode shapes of the bridge. Building upon the Abdel-Ghaffar and Scanlan study, the Golden Gate Bridge was selected for the full-scale deployment of the scalable wireless sensor network described in the previous sections. The bridge has a main-span ͑ 1,280 m ͒ , two side-spans ͑ 342.9 m ͒ , and two towers ͑ 210.3 m above the water level ͒ . The objective of the WSN deployment was to identify the vibration characteristics of the main span and the south tower. The deployment on the Golden Gate Bridge provided the opportunity to test the WSN in a difficult environment and with a linear topology that required a large number of hops for communication. Fig. 6 shows the instrumentation plan for the bridge with a total of 64 nodes, 56 on the main span ͑ measuring transverse and vertical acceleration ͒ and eight on the South Tower ͑ measuring transverse and longitudinal acceleration ͒ . On the main span 53 nodes were deployed on the west side, and three nodes on the east side. Each main span node was attached to the top flange of the floor girder directly inside of the cable. Fig. 7 shows a node with the bidirectional antenna, along with the clamps and guy wires for temporary installation of the testbed. The node spacing on the west side was selected based on the range of the radio with a majority of nodes placed 30.5 m apart, but at places where an obstacle obstructs a clear line of sight this distance was reduced to 15.25 m. The three nodes on the east side, added in the second phase after changing the batteries, were located at the two quarter spans and the midspan of the bridge. The east side nodes have radio communication with the west side nodes under the roadway deck. For the South Tower, there is a node on each side of each strut. The tower nodes have a clear line of sight between them and hence have greater radio range than the main-span nodes. The node on the west side of the strut above the superstructure collects data from all the nodes on the tower and transmits them to the network on the main-span. Installation of the network began on July 14, 2006 and the last set of data was collected on September 22, 2006. The 512 kB flash memory of each node can buffer about 250,000 samples of data, which can be allocated to any combination of the five sensor channels on the node ͑ four accelerometers and a temperature sensor ͒ . Each run starts with a pause to synchronize the network and disseminate a command to start sampling at a future time. After the scheduled sampling takes place there is a pause to establish the network routing. The recorded data are then transferred from each node to the base station using the reliable data communication and pipelining. A complete cycle of sampling and data collection for the full network produces 20 MB of data and takes about 9 hours. There were a total of 174 such runs during the deployment. This total includes runs where the network was being installed and tested so all of the collected data sets do not contain data from all of the nodes. The network was fully installed on the west side of the main span and the south tower on August 1, 2006 and 13 sets of data were collected with the first set of batteries. At the time of changing batteries on September 22, 2006, the nodes on the east side of the main span were installed and three more sets of data collected. The runs include a variety of combinations of sensor channels, which always included the two low-level Silicon Design 1221 accelerometers, but the other two high-level ADXL202 accelerometers and the temperature sensor were turned off in some of the runs to reduce the volume of the data or increase the sampling rate. As an example of the ambient vibration data, the vertical accelerations from the low-level accelerometers in a typical run ͑ 174 ͒ are shown in Figs. 8–10 for the three quarter points on the main- span. The sampling frequency was 50 Hz over 1,600 s, resulting in 80,000 samples per channel. Each figure includes plots of the signal and the power spectral density ͑ PSD ͒ using the Welch method ͑ Welch 1967 ͒ . The amplitudes of the ambient accelerations are about ±10 m g , but spikes of up to 50 m g are apparent, presumably caused by heavy vehicles traveling on the roadway. The PSD plots show clear and consistent peaks at frequencies at several nodes. These spectral peaks are distinct in lower frequencies. Twenty peaks are visible in the frequency plots, which correspond to vertical and torsional vibration mode shapes of the bridge, as will be shown later. The PSD plots indicate that the low- frequency noise level is very small compared with the peaks of the spectra ͑ the power of the noise is about two orders of magni- tude smaller than the power at peak frequencies ͒ . Three aspects of the wireless communication performance for the network were examined empirically using data from the bridge: effective bandwidth, loss rate, and average network bandwidth. These three metrics are important indications of network quality and are critical to scalability of the network. The effective bandwidth is defined as the amount of data per unit time that is sent to the base station from a node n -hops away. Fig. 11 shows the effective bandwidth based on empirical measurements of network performance for four different runs. The one-hop bandwidth of about 1,200 bytes / s is reduced by each additional hop because each node has to receive, buffer, and transmit the packets in the communication stream. A pipeline length of K = 5 was established for the network, so the effective bandwidth remains relatively constant for nodes that are beyond the fifth node up to ...

Citations

... In structural health monitoring, current systems operate as a mounted cluster of sensors permanently fixed onto and around the structure in question. 1 These systems are typically installed later by specialists with dedicated instruments and vehicles as they are rarely considered during the structure's construction. This procedure is very time-consuming and cost-ineffective, as the sensing systems can't be removed when not in use and their maintenance is as complex as the installation process. ...
Conference Paper
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The rapid assessment of infrastructure following extreme weather or seismic events is important to ensure the stability of structures before their continued use. This work presents an amplitude compensation technique for accurate acceleration measurements formulated for unmanned aerial vehicle's (UAV) deliverable sensor packages. These packages are designed for measuring the acceleration of structures, for instance, railroad bridges and power transmission towers. Current technology for structural health monitoring is expensive, stationary, and requires maintenance by certified personnel. These attributes prevent rapid assessment of remote and hard-to-reach structures. Low-cost, UAV-delivered sensor packages are an ideal solution due to their ability to be deployed on a large scale in a timely manner; cutting down on cost and the danger affiliated with structural health monitoring following extreme and hazardous events. One challenge to this approach is that the UAV deployable sensor package consists of several systems, including mounting hardware, embedded electronics, and energy storage that result in a loss of transmissibility between the structure and the package’s accelerometer. This work proposes a frequency response-based filter to isolate the structure’s vibration signature from interference caused by the sensor package itself. Utilizing an input-output relationship between the sensor package and a calibrated reference accelerometer, a model transfer function is constructed. Compensation is performed in the post-processing stage using the inverse transfer function model. This approach is shown to enhance the signal-to-noise ratio by 1.2 dB, an increase of 7.17\%. This work investigates algorithm robustness and sensitivity to noise across the sensor package's bandwidth of 6-20 Hz. A discussion on the limitations of the system is provided.
... In the past two decades, technological advances have seen a trend in implementing SHM through the speed of wireless acceleration (Agbabian et al., 1991;Peter et al., 2003;Lynch et al., 2004;Brownjohn, 2006;Kim et al., 2007;Pakzad et al., 2008). The development of new monitoring technologies in sensing systems, such as fibre-optic sensors, piezoelectric sensors, magnetostrictive sensors, and self-reinforcing fibre structure composites, has tremendous potential for detecting various physical and chemical parameters related to structural health (Sun et al., 2010;Meyer et al., 2010;Glisic et al., 2013;Leung et al., 2015;Spencer et al., 2016;Moreu et al., 2017;Noel et al., 2017). ...
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Structural Health Monitoring (SHM) is a powerful monitoring method for assessing structural health in infrastructure, including damage detection and structural prognostics. The emergence of new monitoring technologies in sensing systems has been recognised for its potential in detecting various physical and chemical parameters related to SHM, but it also fraught with risks and unknown threats. This paper aims to investigate the risk factors that should be considered to decide whether to adopt a new monitoring technology. This research is based on preliminary identification of risks that could affect the adoption of new monitoring technology, focusing on the context of building and engineering structures. A content analysis approach that involves cross-referencing various sources of information was performed to identify the risks of new monitoring technology. The analysis revealed eleven main risks that influence the adoption of new monitoring technology in building and engineering structures. It emphasizes the importance of the risk assessment model for assessing the risk data for better SHM algorithm adoption decision, which considers technological risk and the external risks originating ed from the project environment.
... 16 Also, due to the accessible cost of the components employed in WSNs and the development of power-optimized systems 17 and advanced synchronization strategies, 18,19 dense distributions of smart nodes are increasingly used for SHM applications, as they can be exploited to study in detail the dynamic behavior of structures and accurately localize damage. 20 Jang et al. 21 developed an eventtriggered system using 70 sensor nodes and two base stations. ...
... A recent branch of research is oriented towards the application of SHM algorithms to large structures and infrastructures using wireless sensing systems, facing the issues that may arise when dealing with complex network topologies. 20,21,26 In particular, line topology is particularly suitable in the case of densely distributed structures having a onedimensional development. 27 However, in traditional applications, the weight of the data packets increases along the transmission path, becoming particularly demanding in the final portion of the network, where the nodes consume a significant amount of energy in forwarding all the data packets collected throughout the sensing system. ...
Article
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In this paper, a new strategy for vibration-based structural health monitoring is proposed, specifically designed for smart sensors with edge computing capabilities organized in a line topology. This solution is aimed at maximizing resource optimization and enables the identification of modal parameters even for large or densely instrumented structures, where star-topology monitoring systems are typically unsuitable. In particular, an efficient data management procedure is proposed to reduce data transmission, thus improving efficiency and minimizing maintenance interventions for battery replacement in wireless applications. The maximum volume of transmitted data can be selected by the user, based on the specific requirements of the network. Although the considerable reduction of data size, the proposed approach enables accurate estimation of the structural parameters in challenging scenarios where other techniques generally fail. Modal parameters are identified in an online fashion, enabling near real-time detection and localization of early damage. Applications to a real case study instrumented with a dense sensor network show the effectiveness of the proposed approach and the possibility of localizing structural defects in slightly damaged civil structures.
... Advances in the wireless technology and embedded processing have made much lower-cost wireless smart sensor networks (WSSNs) an attractive alternative to wired, centralised data acquisition (DAQ) systems. The majority of the work using wireless smart sensors for structural monitoring has focused on using the sensors to emulate traditional wired sensor systems (Arms et al. 2004;Pakzad et al. 2008;Whelan and Janoyan 2009). These systems require that all data be sent back to a central DAQ system for further processing; hence, the amount of wireless communication required in the network can become costly in terms of excessive communication times and the associated power it consumes as the network size increases. ...
... These systems require that all data be sent back to a central DAQ system for further processing; hence, the amount of wireless communication required in the network can become costly in terms of excessive communication times and the associated power it consumes as the network size increases. For example, a wireless sensor network implemented on the Golden Gate Bridge that generated 20 MB of data (1600 s of data, sampling at 50 Hz on 64 sensor nodes) took over 9 h to complete the communication of the data back to a central location (Pakzad et al. 2008). ...
Chapter
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Based on the variety of methods available for gathering data for the aircraft health status, the challenge is to reduce the overall amount of data in a trackable and safe manner to ensure that the remaining data are characteristic of the current aircraft status. This chapter will cover available data reduction strategies for this task and discuss the data intensity of the SHM methods of Chaps. 10.1007/978-3-030-72192-3_5 to 10.1007/978-3-030-72192-3_8 and established approaches to deal with the acquired data. This includes aspects of algorithms and legal issues arising in this context.
... This solution is based on cross-layer hardware design, which brings cost, power consumption and performance problems to a certain extent. Later, Shamim N et al. [6]designed and developed an acceleration sensor node for structural health monitoring to meet the requirements of structural vibration monitoring and modal recognition. These collection nodes exhibit powerful and scalable performance even when the number of hops is required for communication. ...
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When errors occur in the sensor networks, there are noise in the acquired data. In this paper, based on the response correlation function between two points of the structure, the least squares complex exponential method is used to identify the influence of noise on the structural modal parameters. Through the construction of the steel beam platform experiment, the experiment verifies the influence of the noise in the collected data on the recognition of modal parameters. The results show that when there is noise in the collected response data, the noise component will cause errors in the identification of structural modal parameters, and the lower the modal order, the more sensitive the noise level.
... We likewise present an ongoing correspondence schedulability examination for sensor organizations dependent on accurate portrayal which uses information with respect to network geography and remaining burden attributes to break down the schedulability of a bunch of intermittent streams with constant prerequisites [2]. ...
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WSNs are progressively utilized in our everyday life. In any case, as per the lifetime of a battery in Wireless Sensor Network hub is confined, the lifetime of a WSN is additionally restricted. Accordingly, it is pivotal to decide this restricted lifetime ahead of time for forestalling administration interferences. To show the expansiveness and points of interest of wireless sensor networks, design and usage subtleties are introduced for three frameworks, one in every one of these three application spaces. To examine and investigate the favorable position or inconvenience of these methodologies, some thorough overview writings were distributed; anyway they are either obsolete or compiled for correspondence conventions on single layer. In view of this insufficiency, in this paper, we present the state-of-the-art research draws near and talk about the significant highlights identified with real-time correspondences in WSN. Novel video plot prioritization and traffic booking estimations further streamline Vigil's transfer speed use.
... When monitoring civil infrastructure, automatic collection of data is preferable for reducing the fixed communication costs associated with continuous human onsite monitoring and freedom from the geographical constraints of the communication area. Although approaches to wirelessly acquire the vibration acceleration of a bridge have been proposed, problems such as synchronization of multiple sensors and securing power need to be overcome [16][17][18]. This study provides an approach to use the low-power wide area network (LPWA) [19], which has the disadvantage of low speed but low power consumption and the advantage of reaching a long distance, for the monitoring using the inclinometer with small amounts of data traffic. ...
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In recent years, aging of bridges has become a growing concern, and the danger of bridge collapse is increasing. To appropriately maintain bridges, it is necessary to perform inspections to accurately understand their current state. Until now, bridge inspections have involved a visual inspection in which inspection personnel come close to the bridges to perform inspection and hammering tests to investigate abnormal noises by hammering the bridges with an inspection hammer. Meanwhile, as there are a large number of bridges (for example, 730,000 bridges in Japan), and many of these are constructed at elevated spots; the issue is that the visual inspections are laborious and require huge cost. Another issue is the wide disparity in the quality of visual inspections due to the experience, knowledge, and competence of inspectors. Accordingly, the authors are trying to resolve or ameliorate these issues using unmanned aerial vehicle (UAV) technology, artificial intelligence (AI) technology, and telecommunications technology. This is discussed first in this paper. Next, the authors discuss the future prospects of bridge inspection using robot technology such as a 3-D model of bridges. The goal of this paper is to show the areas in which deployment of the UAV, robots, telecommunications, and AI is beneficial and the requirements of these technologies.
... Extensive research has been conducted over the past decade to develop and implement WSSs, addressing the key challenges of limited resources, data loss, and time synchronization. [4][5][6][7][8][9] Among the challenges, time synchronization rises in importance when several or up to hundreds of sensors need to collaborate to gather precisely synchronized measurements and perform distributed analysis. The required synchronization level for SHM is generally much more precise than typically available "for free" on wireless sensor platforms with off-the-shelf integrated radio modules. ...
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Full-text available
The implementation of a wireless smart sensor network (WSSN) to monitor a structure subjected to sudden events (e.g., earthquakes) poses many challenges. One typical challenge is to synchronize the data from different sensors during event-triggered sensing, because each wireless sensor has its own independent clock and their triggering time is generally different and unknowable. The challenge becomes more acute when real-time data acquisition is required to support vibration control or rapid damage assessment under sudden events. Although various strategies have been proposed to achieve synchronized sensing of WSSNs, all of these strategies fall short in addressing the challenges of sudden-event monitoring. In this paper, we first proposed an efficient two-stage time synchronization strategy for general structural health monitoring as a baseline and implemented it on a next-generation wireless smart sensor. Building upon the baseline method, efficient time synchronization strategies are proposed, which are fully autonomous and highly efficient for sudden-event monitoring. Two classes of events are considered: short-duration events, for example, bridge impacts, during which initial data loss issues are critical, and long-duration events, such as strong winds, which can benefit significantly from employing real-time monitoring. For short-duration events, a post-event time synchronization strategy is developed, which autonomously performs off-line synchronization after measurement. For long-duration events, a real-time time synchronization strategy is proposed, which can support real-time applications under sudden events, for example, vibration control. These time synchronization strategies are experimentally validated to demonstrate the efficiency and accuracy (under 20-μs maximum error) of the proposed sudden-event synchronized sensing strategies.
... The MPMF results are compared with the the most comprehensive report on the modal properties of the Golden Gate Bridge. The true values in Tables 1 and 2 are based on data collected over a three-month period with a wireless network of 240 accelerometers and found over sixty vibrational modes (vertical, transverse, and torsional) [85,86] . In particular, there are seven vertical modes and three torsional modes below 0.5 Hz (see Table 4). ...
... Smartphones collect noisy IMU data. In order to provide guidance for the hyperparameter selection and establish sensitivity to measurement noise, we conducted a numerical simulation of the bridge-vehicle system based on identified modal characteristics of the Golden Gate Bridge from the latest comprehensive monitoring project [86] . The bridge is modeled as a multi-degree-of-freedom linear system with similar geometries to the original structure. ...
Preprint
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Modern bridge health monitoring methods require specialized sensor networks, which have costs that are prohibitive to bridge owners. Mobile sensor networks reduce costs by capturing vibrational signatures and indicators of structural decay using substantially fewer devices. Over the last decade, researchers have hypothesized that crowd-sourced mobile sensor data, collected ubiquitously and cheaply, will revolutionize our ability to maintain existing infrastructure. In this context, crowd-sourced data faces a precision challenge in which magnitude-level leaps must be made over past applications. As such, the field is missing a realistic field validation of this concept; researchers have primarily focused on synthetic models, controlled experiments, and idealized measurements. Here we fill this knowledge gap by showing that critical physical properties of a real bridge can be determined from everyday vehicle trip data. We collected smartphone data from controlled field experiments and UBER rides on the Golden Gate Bridge and developed an analytical method to recover dominant vibrational frequencies, which paves the way for scalable, cost-effective structural health monitoring based on this abundant data class. Our results are consistent with a comprehensive study on the Golden Gate Bridge; they certify the immediate value of large-scale data sources for studying the health of existing infrastructure, whether the data are crowdsensed or generated by organized vehicle fleets such as ridesourcing companies or municipalities
... 24 Several methods are developed for optimal sensor placement instead of dense sensor networks to reduce redundant data and improve observed information. [25][26][27][28] Petersen et al. 29 estimated full-field dynamic response of a long span floating bridge employing a joint-input state estimation algorithm and Kalman filter using acceleration measurements. Wang et al. 30 built Zernike moment descriptors to estimate full-field data as a function of load by training with Zernike polynomials and full-field strain data. ...
Article
The complex geometry of structural components results in uneven stress distribution in structures under loading. The regions with stress concentrations often act as critical locations from where damages can initiate and propagate under different types of loading. Accurate estimation of stress distribution, especially at such critical locations, is vital for a more reliable prediction of possible damage prognosis or remaining useful life estimation of the structure or a component. Traditional sensing methods often provide response measurements at a few localized points and demand sensor deployment in large numbers to get a distributed response. This may yet be sparse for the steep changes in stress at critical locations. In this study, we propose a hybrid data + model‐based submodeling (HDMS) method to achieve a refined estimate of distributed structural response in and around the critical locations. The HDMS method uses just the measured response on the preselected boundaries around the locations of interest, as input to drive the corresponding submodel of a structural component or a connection, given its geometry and material properties are known. The performance of the HDMS method in response estimation is demonstrated through two vertically loaded plates, one with two holes connected by a slit and the other with two wide slits, respectively. The refined response estimated by HDMS could determine asymmetric response, nonlinear behavior, and permanent set at the critical locations with an average error of less than 50 μstrain at higher load stages, making HDMS a versatile method for refined response estimation.