Sung-Han Sim

Development and Application of High-Sensitivity Wireless Smart Sensors for Decentralized Stochastic Modal Identification

State-of-the-art smart sensor technology enables deployment of dense arrays of sensors, which is critical for structural health monitoring (SHM) of complicated and large-scale civil structures. Despite recent successful implementation of various wireless smart sensor networks (WSSNs) for full-scale SHM, the low-cost micro-electro-mechanical systems (MEMS) sensors commonly used in smart sensors cannot readily measure low-level ambient vibrations because of their relatively low resolution. Combined use of conventional wired high- sensitivity sensors with low-cost wireless smart sensors has been shown to provide improved spectral estimates of response that can lead to improved experimental modal analysis. However, such a heterogeneous network of wired and wireless sensors requires central collection of an enormous amount of raw data and off-network processing to achieveglobal time synchronization; consequently, many of the advantages of WSSNs for SHM are lost. In this paper, the development of a new high-sensitivity accelerometer board (SHM-H) for the Imote2 wireless smart sensor (WSS) platform is presented. The use of a small number of these high-sensitivity WSSs, composed of the SHM-H and Imote2, as reference sensors in the Natural Excitation Technique—based decentralized WSSN strategy is explored and is shown to provide a cost- effective means of improving modal feature extraction in the decentralized WSSN for SHM. DOI: 10.1061/(ASCE)EM.1943-7889 .0000352.

Displacement Estimation Using Multimetric Data Fusion

While displacement is valuable information for the structural behavior, measuring displacements from large civil structures is often challenging and costly. To overcome difficulties found in direct measurements such as using linear variable differential transformer and LASER-based methods, indirect displacement estimation approaches are alternatively developed. Such indirect approaches in general rely on acceleration or strain that is relatively cost effective and convenient to measure. However, these measurements have own characteristics that limit wider application of the indirect estimation. For example, as the double integration of acceleration results in the low-frequency drift in the estimated displacement, high-pass filters are often used to suppress the drift, assuming displacements are close to a zero mean process; strain is difficult to use for high-frequency modes. These types of limitations can be resolved by the fusion of different types of measurements. This study develops an indirect displacement estimation method based on the multimetric data (i.e., acceleration and strain) that can estimate nonzero mean, dynamic displacements. The proposed approach is numerically validated, showing better estimation than the single measurement-based methods. Furthermore, the performance of the proposed approach is verified using dynamic response data measured from the Sorok Bridge, a cable-stayed bridge in Korea.

Multimetric Sensing for Structural Damage Detection

Vibration-based damage detection methods have been widely studied for structural health monitoring of civil infrastructure. Acceleration measurements are frequently employed to extract the dynamic characteristics of the structure and locate damage because they can be obtained conveniently and possess relatively little noise. However, considering the fact that damage is a local phenomenon, the sole use of acceleration measurements that are intrinsically global structural responses limits damage detection capabilities. This paper investigates the possibility of using both global and local measurements to improve the accuracy and robustness of damage detection methods. A multimetric approach based on the damage locating vector method is proposed. Numerical simulations are conducted to verify the efficacy of the proposed approach.

Hybrid wireless smart sensor network for full-scale structural health monitoring of a cable-stayed bridge

Rapid advancement of sensor technology has been changing the paradigm of Structural Health Monitoring (SHM) toward a wireless smart sensor network (WSSN). While smart sensors have the potential to be a breakthrough to current SHM research and practice, the smart sensors also have several important issues to be resolved that may include robust power supply, stable communication, sensing capability, and in-network data processing algorithms. This study is a hybrid WSSN that addresses those issues to realize a full-scale SHM system for civil infrastructure monitoring. The developed hybrid WSSN is deployed on the Jindo Bridge, a cable-stayed bridge located in South Korea as a continued effort from the previous years deployment. Unique features of the new deployment encompass: (1) the worlds largest WSSN for SHM to date, (2) power harvesting enabled for all sensor nodes, (3) an improved sensing application that provides reliable data acquisition with optimized power consumption, (4) decentralized data aggregation that makes the WSSN scalable to a large, densely deployed sensor network, (5) decentralized cable tension monitoring specially designed for cable-stayed bridges, (6) environmental monitoring. The WSSN implementing all these features are experimentally verified through a long-term monitoring of the Jindo Bridge.

Development of a Wireless Displacement Measurement System Using Acceleration Responses

Displacement measurements are useful information for various engineering applications such as structural health monitoring (SHM), earthquake engineering and system identification. Most existing displacement measurement methods are costly, labor-intensive, and have difficulties particularly when applying to full-scale civil structures because the methods require stationary reference points. Indirect estimation methods converting acceleration to displacement can be a good alternative as acceleration transducers are generally cost-effective, easy to install, and have low noise. However, the application of acceleration-based methods to full-scale civil structures such as long span bridges is challenging due to the need to install cables to connect the sensors to a base station. This article proposes a low-cost wireless displacement measurement system using acceleration. Developed with smart sensors that are low-cost, wireless, and capable of on-board computation, the wireless displacement measurement system has significant potential to impact many applications that need displacement information at multiple locations of a structure. The system implements an FIR-filter type displacement estimation algorithm that can remove low frequency drifts typically caused by numerical integration of discrete acceleration signals. To verify the accuracy and feasibility of the proposed system, laboratory tests are carried out using a shaking table and on a three storey shear building model, experimentally confirming the effectiveness of the proposed system.

A wireless smart sensor network for automated monitoring of cable tension

As cables are primary load carrying members in cable-stayed bridges, monitoring the tension forces of the cables provides valuable information regarding structural soundness. Incorporating wireless smart sensors with vibration-based tension estimation methods provides an efficient means of autonomous long-term monitoring of cable tensions. This study develops a wireless cable tension monitoring system using MEMSIC’s Imote2 smart sensors. The monitoring system features autonomous operation, sustainable energy harvesting and power consumption, and remote access using the internet. To obtain the tension force, an in-network data processing strategy associated with the vibration-based tension estimation method is implemented on the Imote2-based sensor network, significantly reducing the wireless data transmission and the power consumption. The proposed monitoring system has been deployed and validated on the Jindo Bridge, a cable-stayed bridge located in South Korea.

Wireless displacement sensing system for bridges using multi-sensor fusion

Accurate displacement sensing or estimation is an important task for reliably assessing the condition of civil infrastructure such as bridges and buildings, because the structural displacement describes the behavior of a structure and indicates structural safety according to the design limit. However, it is difficult to directly measure the displacement of a bridge structure due to the inaccessibility of a reference point especially when bridges are built over a highway, a river or the sea. As an alternative, an indirect displacement estimation using two different types of measurements such as strain and acceleration (i.e., multimetric data) has been developed. While the approach has been seen as promising, the combination of the traditional sensing system based on wired sensors and the multimetric data-based algorithm is inappropriate or impractical in real-world applications of the approach. This paper proposes a new displacement sensing system by incorporating wireless sensor technology with the multimetric data-based algorithm, which can address the difficulties and issues found in the traditional sensing system to realize a practical means of measuring displacement in full-scale bridges. The proposed wireless displacement sensing system enables (a) time-synchronized acceleration and strain measurement, (b) high-precision strain sensing and (c) improved applicability due to the wireless communication as well as the previous two features. The effectiveness of the proposed system is experimentally verified in laboratory and full-scale experiments.

Virtual laboratory for experimental structural dynamics

This article presents a Java‐powered virtual laboratory (VL) which has been developed to provide a means for on‐line interactive structural dynamics experiments for undergraduate and graduate education. This VL intends to provide a conceptual and practical understanding of a wide range of topics related to the collection, analysis, and interpretation of data from dynamic testing, including sensor type and placement, aliasing, windowing, nonlinearities, etc. A multi‐story shear building is employed as a test bed and the responses of the structure are obtained through the linear/nonlinear dynamic analysis. The VL is available on‐line at http://sstl.cee.uiuc.edu/java/esd/virtuallab.html. This article presents the unique features and usage of this VL.

Concrete Crack Identification Using a UAV Incorporating Hybrid Image Processing

Crack assessment is an essential process in the maintenance of concrete structures. In general, concrete cracks are inspected by manual visual observation of the surface, which is intrinsically subjective as it depends on the experience of inspectors. Further, it is time-consuming, expensive, and often unsafe when inaccessible structural members are to be assessed. Unmanned aerial vehicle (UAV) technologies combined with digital image processing have recently been applied to crack assessment to overcome the drawbacks of manual visual inspection. However, identification of crack information in terms of width and length has not been fully explored in the UAV-based applications, because of the absence of distance measurement and tailored image processing. This paper presents a crack identification strategy that combines hybrid image processing with UAV technology. Equipped with a camera, an ultrasonic displacement sensor, and a WiFi module, the system provides the image of cracks and the associated working distance from a target structure on demand. The obtained information is subsequently processed by hybrid image binarization to estimate the crack width accurately while minimizing the loss of the crack length information. The proposed system has shown to successfully measure cracks thicker than 0.1 mm with the maximum length estimation error of 7.3%.

A decentralized receptance-based damage detection strategy for wireless smart sensors

Various structural health monitoring strategies have been proposed recently that can be implemented in the decentralized computing environment intrinsic to wireless smart sensor networks (WSSN). Many are based on changes in the experimentally determined flexibility matrix for the structure under consideration. However, the flexibility matrix contains only static information; much richer information is available by considering the dynamic flexibility, or receptance, of the structure. Recently, the stochastic dynamic damage locating vector (SDDLV) method was proposed based on changes of dynamic flexibility matrices employing centrally collected output-only measurements. This paper investigates the potential of the SDDLV method for implementation on a network of wireless smart sensors, where a decentralized, hierarchical, in-network processing approach is used to address issues of scalability of the SDDLV algorithm. Two approaches to aggregate results are proposed that provide robust estimates of damage locations. The efficacy of the developed strategy is first verified using wired sensors emulating a wireless sensor network. Subsequently, the decentralized damage detection strategy is implemented on MEMSIC’s Imote2 smart sensor platform and validated experimentally on a laboratory scale truss bridge.