Illinois Structural Health Monitoring Project Research Articles

Autonomous evaluation of ambient vibration of underground spaces induced by adjacent subway trains using high-sensitivity wireless smart sensors

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC\'s Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

Embedding Compressive Sensing-Based Data Loss Recovery Algorithm Into Wireless Smart Sensors for Structural Health Monitoring

Lossy transmission is a common problem for monitoring systems based on wireless sensors. Reliable communication protocols, which enhance communication reliability by repetitively transmitting unreceived packets, is one approach to tackle the problem of data loss. An alternative approach allows data loss to some extent and seeks to recover the lost data from an algorithmic point of view. Compressive sensing (CS) provides such a data loss recovery technique. This technique can be embedded into smart wireless sensors and effectively increases wireless communication reliability without retransmitting the data; the promise of this approach is to reduce communication and thus power savings. The basic idea of CS-based approach is that, instead of transmitting the raw signal acquired by the sensor, a transformed signal that is generated by projecting the raw signal onto a random matrix, is transmitted. Some data loss may occur during the transmission of this transformed signal. However, according to the theory of CS, the raw signal can be effectively reconstructed from the received incomplete transformed signal given that the raw signal is compressible in some basis and the data loss ratio is low. Specifically, this paper targets to provide accurate compensation for stationary and compressible acceleration signals obtained from structural health monitoring (SHM) systems with data loss ratio below 20%. This CS-based technique is implemented into the Imote2 smart sensor platform using the foundation of Illinois Structural Health Monitoring Project Service Tool-suite. To overcome the constraints of limited onboard resources of wireless sensor nodes, a method called random demodulator (RD) is employed to provide memory and power efficient construction of the random sampling matrix. Adaptation of RD sampling matrix is made to accommodate data loss in wireless transmission and meet the objectives of the data recovery. The embedded program is tested in a series of sensing and communication experiments. Examples and parametric study are presented to demonstrate the applicability of the embedded program as well as to show the efficacy of CS-based data loss recovery for real wireless SHM systems.

Decentralized NExT/ERA and RDT/ERA System Identification in Wireless Smart Sensor Networks

Wireless smart sensor networks (WSSN) have many advances compared with traditional structural health monitoring (SHM) such as wireless process, real-time calculation and low cost. However power consumption is considered as one of the most limitations in this field. The unique features offered by decentralized data aggregation (DDA) technique with the potential to overcome power consumption enable implementation of the dense array of WSSN on large structures. This paper presents a system identification of a simply supported plate based on the random decrement technique (RDT) and natural excitation technique (NExT) in combination with eigensystem realization algorithm (ERA) using Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. Finally the system parameters including natural frequency and mode shapes are in accordance with numerical simulation showing efficacy and feasibility of decentralized NExT/ERA and RDT/ERA system identification.

Structural modal identification based on distributed wireless smart sensor networks

This paper presents the vibration test of a simply supported plywood plate using ISM400 wireless smart sensor developed by the Illinois Structural Health Monitoring Project(ISHMP).Decentralized data aggregation(DDA)as well as centralized data collection(CDC)was used for data acquisition and processing.Then a time domain algorithms integrating natural excitation technique(NExT)and eigensystem realization algorithm(ERA),was applied to identify modal parameters of the plate and the identification result was compared with the finite element result.The modal parameters(i.e.natural frequency and mode shape)identified from experimental data using DDA is not only in accordance with that using CDC,but also in accordance with the numerical simulation result,which shows that the wireless smart sensor networks(WSSN)based on DDA is reasonable and effective.Compared with conventional centralized processing technique,the unique features offered by distributed WSSN,including high integration and electrical energy saving,make deployment of a dense array of sensors on large civil structures both feasible and economical.

Modal Analysis of Simply Supported Plate Using Wireless Smart Sensor Networks

The unique features offered by wireless smart sensor networks (WSSN), including high integration, wireless communication, on-board computation, small in size, simple installation, and cost effectiveness, facilitate a new paradigm to structural health monitoring (SHM) for civil infrastructure. The paper is an example use of WSSN and the Illinois SHM Project toolsuite. An experimental study of modal analysis of a simply supported plate structure has been undertaken. The eigensystem realization algorithm (ERA) in conjunction with natural excitation technique (NExT) and stabilization diagram (SD) is used to extract the modal information, including natural frequency, damping ratio and mode shapes, from the ambient vibration test data. The results show that WSSN designed for SHM application utilizing a smart sensor platform suited to high-frequency, high-fidelity data acquisition and demanding data processing algorithms.

Full-Scale Experimental Validation of High-Fidelity Wireless Measurement on a Historic Truss Bridge

To meet the growing demands to monitor our aging infrastructure, wireless smart sensor networks (WSSN) have been the subject of intense interest due to their versatility and low cost. However, the performance of commercially available sensors is not sufficient to realize the high-fidelity data required for SHM. In particular, synchronization among the wireless sensor nodes has been found to be inadequate for data intensive applications such as SHM. To this end, the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite has been developed as an open-source framework to enable reliable SHM application. One of the services, RemoteSensing, was specifically designed to enable monitoring of civil infrastructure through accurate time synchronization and reliable communication. In this paper, the performance of the Imote2 wireless sensor platform using a commercially available low cost sensor board is validated directly against a wired sensor system, in the context of validation of its capability for high-fidelity data measurement for bridge health monitoring. A series of vibration tests have been conducted using human jumping to excite the subject of this study, a 76-m historic steel truss bridge in Mahomet, Illinois. The dynamic properties of the bridge have been obtained by the peak picking method for both wireless and wired systems and compare well; thus, demonstrating the efficacy of the wireless sensor system.

Structural health monitoring of a cable-stayed bridge using smart sensor technology: deployment and evaluation

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.

Decentralized random decrement technique for efficient data aggregation and system identification in wireless smart sensor networks

Smart sensors have been recognized as a promising technology with the potential to overcome many of the inherent difficulties and limitations associated with traditional wired structural health monitoring (SHM) systems. The unique features offered by smart sensors, including wireless communication, on-board computation, and cost effectiveness, enable deployment of the dense array of sensors that are needed for monitoring of large-scale civil infrastructure. Despite the many advances in smart sensor technologies, power consumption is still considered as one of the most important challenges that should be addressed for the smart sensors to be more widely adopted in SHM applications. Data communication, the most significant source of the power consumption, can be reduced by appropriately selecting data processing schemes and the related network topology. This paper presents a new decentralized data aggregation approach for system identification based on the Random Decrement Technique (RDT). Following a brief overview of the RDT, which is an output-only system identification approach, a decentralized hierarchical approach is described and shown to be suitable for implementation in the intrinsically distributed computing environment found in wireless smart sensor networks (WSSNs). RDT-based decentralized data aggregation is then implemented on the Imote2 smart sensor platform based on the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. Finally, the efficacy of the RDT method is demonstrated experimentally in terms of the required data communication and the accuracy of identified dynamic properties.