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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
... Wireless sensor networks are widely used in national defense and military, environmental monitoring, facility agriculture, medical and health care, smart home, traffic management, anti-terrorism and disaster relief, and other fields. SHM system based on wireless sensor network [22][23][24][25] includes three subsystems: sensing subsystem, data acquisition subsystem, and information transmission subsystem. Each subsystem involves different software and hardware to achieve different functions; they cooperate with each other to complete tasks together. ...
In recent years, the number of super high-rise buildings is increasing due to the rapid development of economy and construction technology. It is important to evaluate the health condition of super high-rise buildings to make them operate safely. However, conventional structural health monitoring (SHM) system requires a great number of wires to connect the sensors, power sources, and the data acquisition equipment, which is an extremely difficult process to plan the layout of all wires. Hence, one of the usually used compromising approaches is to limit the number of sensors to reduce the usage of wires. Recently, wireless sensor networks and cloud platform have been widely used in SHM system for super high-rise buildings because of their convenient installation, low maintenance cost, and flexible deployment. This paper presents a comprehensive review of the existing SHM system for super high-rise buildings based on wireless sensor network and cloud platform, which usually consists of sensing network subsystem, data acquisition subsystem, data transmission subsystem, and condition evaluation subsystem. This paper also reviews the crucial techniques and typical examples of SHM system used for famous super high-rise buildings. In addition, the existing difficulties in wireless sensor network and cloud platform based SHM system for super high-rise buildings and the future research directions are discussed and summarized.
... The MPMF results are compared with 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) 49,50 . In particular, there are seven vertical modes and three torsional modes below 0.5 Hz (see Table 3 in "Methods"). ...
Monitoring and managing the structural health of bridges requires expensive specialized sensor networks. In the past decade, researchers predicted that cheap ubiquitous mobile sensors would revolutionize infrastructure maintenance; yet extracting useful information in the field with sufficient precision remains challenging. Herein we report the accurate determination of critical physical properties, modal frequencies, of two real bridges from everyday vehicle trip data. We collected smartphone data from controlled field experiments and uncontrolled Uber rides on a long-span suspension bridge in the USA (The Golden Gate Bridge) and developed an analytical method to accurately recover modal properties. We also successfully applied the method to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy. Further analysis projected that the inclusion of crowdsourced data in a maintenance plan for a new bridge could add over fourteen years of service (30% increase) without additional costs. Our results suggest that massive and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure.
... Thus, it's really clear that there has been tremendous scope of WSN in different crucial application areas where humans can't be there for whole time. These applications include target coverage [4], structure health monitoring [5], underwater sensor network (UWSN) [6], monitor environment and disaster management [7] applications like smart city, health care and agriculture [8]. Some programs are so extreme that people can't even stand themselves. ...
The size of the wireless sensor network (WSN) is extending continually with use of IOT networks. The main difficulty for design wide area WSN is to maintain the higher stability period and energy efficiency (EE) for the routing protocols. The creation of clustering-based routing protocols was applied to the optimization of overall network energy. But, traditional clustering methods were unable to produce improved node heterogeneity, and extended network lifetime. Distributed clustering-based routing protocols are specially designed for enhancing the EE of the networks. In addition, EE can be improved by enhancing the heterogeneity of the node distribution. This paper aims to design the extended distributed clustering-based EE routing protocol. The heterogeneity of nodes is improved by introducing the additional intermediate advanced nodes layer in the network. Therefore, paper proposed to design the Multi-Level Heterogynous EDEEC rousing protocol called ML-HEDEEC by adapting optimum energy enhancing parameters. The notes are divided to normal, advance, advance-interdicted and supper nodes based on the energy allocated to them. The probability of nodes is modified for better clustering and cluster head election by introducing additional energy enhancement parameter. In addition, it is proposed to automatically adopt the network initial energy based on the scaling of network dimensions. This may lead to enhance EE of the network and may improve stability period. Finally, the results are evaluated for a case of WSN routing under the dynamic sink locations. Performance is compared for various distributed clustering protocols and other state of art protocols viz. LEACH, SEP, zonal- SEP, DEC considering the network scaling. Various performances of the network stability, packets sent to base station, and lifetime,.are defined for result evaluation. The network dimensions are scaled up to four times and proposed protocol is tested under scaling consideration. In addition, sink locations are also varied for dynamic sink locations performance evaluation. Overall paper efficiently designed and test heterogeneous improved routing protocol with extended lifetime and stability.
... Detecting microseismic events originating from an unstable rock mass is important for locating growing cracks and to analyze the triggering mechanisms of possible future collapses. In [21], a network of sensor nodes based on MEMS analog output accelerometers and MicaZ [22] mote hardware is deployed. Each node is equipped with a microcontroller and a 2.4 GHz transceiver. ...
Rockfalls and landslides are hazards triggered from geomorphological and climatic factors other than human interaction. The economic and social impacts are not negligible, therefore the topic has become an important field in the application of remote monitoring. Wireless sensor networks (WSNs) are particularly suited for the deployment of such systems, thanks to the different technologies and topologies that are evolving nowadays. Among these, LoRa modulation technique represents a fitting technical solution for nodes communication in a WSN. In this paper, a smart autonomous LoRa-based rockfall and landslide monitoring system is presented. The structure has been operating in Pantelleria Island, Sicily, Italy. The sensing elements are disposed in sensor nodes arranged in a star topology. Network access to the LoRaWAN and the Internet is provided through gateways using a portable, solar powered device assembly. A system overview concerning both hardware and functionality of the nodes and gateways devices, then a power analysis is reported, and a monthly recorded result is presented, with related discussion.
... However, the laboratory studies utilized a wired data acquisition system and were based on simplifying the traffic loading as a harmonic function, whereas real traffic-induced bridge responses consist of impulsive signals. To fill these gaps in the knowledge and transfer this technology from the laboratory to the field for long-term bridge fatigue crack monitoring, this paper presents two critical novelties: (1) by integrating the SEC with the Xnode sensing platform, a wireless large-area strain sensor (WLASS) is created to wirelessly collect the large-area strain data to support fatigue crack monitoring; (2) an effective automated algorithm is developed based on wavelet transform to process the traffic-induced bridge response data consisting of numerous impulsive peak events for monitoring fatigue crack growth. The remainder of this paper is organized as follows. ...
This paper presents a field implementation of the structural health monitoring (SHM) of fatigue cracks for steel bridge structures. Steel bridges experience fatigue cracks under repetitive traffic loading, which pose great threats to their structural integrity and can lead to catastrophic failures. Currently, accurate and reliable fatigue crack monitoring for the safety assessment of bridges is still a difficult task. On the other hand, wireless smart sensors have achieved great success in global SHM by enabling long-term modal identifications of civil structures. However, long-term field monitoring of localized damage such as fatigue cracks has been limited due to the lack of effective sensors and the associated algorithms specifically designed for fatigue crack monitoring. To fill this gap, this paper proposes a wireless large-area strain sensor (WLASS) to measure large-area strain fatigue cracks and develops an effective algorithm to process the measured large-area strain data into actionable information. The proposed WLASS consists of a soft elastomeric capacitor (SEC) used to measure large-area structural surface strain, a capacitive sensor board to convert the signal from SEC to a measurable change in voltage, and a commercial wireless smart sensor platform for triggered-based wireless data acquisition, remote data retrieval, and cloud storage. Meanwhile, the developed algorithm for fatigue crack monitoring processes the data obtained from the WLASS under traffic loading through three automated steps, including (1) traffic event detection, (2) time-frequency analysis using a generalized Morse wavelet (GM-CWT) and peak identification, and (3) a modified crack growth index (CGI) that tracks potential fatigue crack growth. The developed WLASS and the algorithm present a complete system for long-term fatigue crack monitoring in the field. The effectiveness of the proposed time-frequency analysis algorithm based on GM-CWT to reliably extract the impulsive traffic events is validated using a numerical investigation. Subsequently, the developed WLASS and algorithm are validated through a field deployment on a steel highway bridge in Kansas City, KS, USA.
... 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. ...
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). ...
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. ...
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). ...
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. ...
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.
