Tomonori Nagayama

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.

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.

Structural health monitoring utilizing Intel’s Imote2 wireless sensor platform

The computational and wireless communication capabilities of smart sensors densely distributed over structures can provide rich information for structural monitoring. While smart sensor technology has seen substantial advances during recent years, interdisciplinary efforts to address issues in sensors, networks, and application specific algorithms are needed to realize their potential. This paper first discusses each of these issues, and then reports on research that combines the results to develop a structural health monitoring (SHM) system suitable for implementation on a network of smart sensors. Experimental verification is provided using Intels Imote2 smart sensors installed on a threedimensional truss structure. The Imote2 is employed herein because it has the high computational and wireless communication performance required for advanced SHM applications. This SHM system is then investigated from sensing, network, and SHM algorithm perspectives.

A multi-scale sensing and diagnosis system combining accelerometers and gyroscopes for bridge health monitoring

This paper presents a multi-scale sensing and diagnosis system combining accelerometers and gyroscopes for bridge health monitoring. Since the damage metric estimated from acceleration measurement is insensitive to damage near the hinged support of a bridge, the damage diagnosis performance is limited near the support region. However, the performance can be improved by using two or more complementary data measured from multi-scale sensing. To more effectively diagnose the integrity of an overall bridge structure, angular velocity is complementary to acceleration, because of its high sensitivity to damage near the hinged support. This study proposes a multi-scale sensing and diagnosis system for bridge health monitoring based on a two-step approach. First, the damage diagnosis based on acceleration measurement is performed on the whole structure by using deflection estimated by modal flexibility. Next, the angular-velocity-based damage diagnosis is additionally carried out to localize missed damage by the acceleration-based approach near the hinged support. For validating the feasibility of the proposed system, a series of numerical and experimental studies on a simply supported beam model was performed. It was found that the multiple damages (one is near the center and another is near the support) can be successfully localized by the proposed multi-scale sensing and diagnosis system, while the damage near the support was missed by a conventional damage metric estimated from acceleration measurements.

Development of high-sensitivity accelerometer board for structural health monitoring

State-of-the-art wireless smart sensor technology enables a dense array of sensors to be distributed through a structure to provide an abundance of structural information. However, the relatively low resolution of the MEMS sensors that are generally adopted for wireless smart sensors limits the networks ability to measure lowlevel vibration often found in the ambient vibration response of building structures. To address this problem, development of a high-sensitivity acceleration board for the Imote2 platform using a low-noise accelerometer is presented. The performance of this new sensor board is validated through extensive laboratory testing. In addition, the use of the high-sensitivity accelerometer board as a reference sensor to improve the capability to capture structural behavior in the smart sensor network is discussed.

Vibration of Reinforced Concrete Viaducts Under High-speed Train Passage: Measurement and Prediction Including Train-Viaduct Interaction

This study investigates the dynamic interactions between high-speed trains and reinforced concrete viaducts using field measurements and numerical simulations. The dynamic responses of a 40 year old viaduct under high-speed train passage are measured. Using general finite-element method software, a new numerical vibration prediction scheme for a train–bridge system is developed. Following the Newmark scheme, a decoupling algorithm is derived through the contact force between a train and viaduct. Track irregularity is also taken into account. The proposed numerical scheme is verified through a comparison between calculated responses and in situ measured responses. This approach is expected to provide not only an accurate simulation tool for train-induced vibration, but also instructive information for the retrofit of railway structures, especially at higher speeds.

Structural health monitoring system of a cable-stayed bridge using a dense array of scalable smart sensor network

This paper presents a structural health monitoring (SHM) system using a dense array of scalable smart wireless sensor network on a cable-stayed bridge (Jindo Bridge) in Korea. The hardware and software for the SHM system and its components are developed for low-cost, efficient, and autonomous monitoring of the bridge. 70 sensors and two base station computers have been deployed to monitor the bridge using an autonomous SHM application with consideration of harsh outdoor surroundings. The performance of the system has been evaluated in terms of hardware durability, software reliability, and power consumption. 3-D modal properties were extracted from the measured 3-axis vibration data using output-only modal identification methods. Tension forces of 4 different lengths of stay-cables were derived from the ambient vibration data on the cables. For the integrity assessment of the structure, multi-scale subspace system identification method is now under development using a neural network technique based on the local mode shapes and the cable tensions.

Dynamic characteristics of an overpass bridge in a full-scale destructive test

Verification of vibration-based damage detection through a full-scale actual structural testing is an important learning opportunity. From such a test, the evolution of dynamic characteristics can be observed, damage detection methods can be validated, and baseline criteria for typical structural damage can be formulated. This paper describes a case study on a full-scale destructive testing of an overpass reinforced concrete bridge. Damage is introduced by cutting one of the bridge piers at the footing level allowing vertical settlement. This type of damage is expected to simulate the condition in which a bridge suffers from nonuniform pier settlement or hidden damage inside piles of buried foundations. By applying time and frequency domain vibration analysis, as well as a system identification technique, changes in dynamic characteristics caused by the damage are evaluated. The results clearly indicate the changes in frequencies as an indicator of damage presence, while the change in mode shapes can be used to locate the damage. The paper also discusses the application of the damage detection method based on outlier analysis of the autospectra function using the bridge ambient acceleration responses. The results indicate that the presence of damage at an early stage can be detected by observing the outliers in multivariate data, and the detection accuracy improved when damage has significantly changed the dynamic characteristics of the structure.

Decentralized structural health monitoring using smart sensors

Decentralized computing is required to harvest the rich information that a dense array of smart sensors can make available for structural health monitoring (SHM). Though smart sensor technology has seen substantial advances during recent years, implementation of smart sensors on full-scale structures has been limited. Direct replacement of wired sensing systems with wireless sensor networks is not straight-forward as off-the-shelf wireless systems are unlikely to provide the data users expect. Sensor component characteristics limit the quality of data collected due to packet loss during communication, time synchronization errors, and slow communication speeds. This paper describes a scalable, decentralized approach to SHM using smart sensors, including the middleware services which address these issues common to smart sensor applications for structural health monitoring. In addition, the results of experimental validation are given. Finally, ongoing research addressing other factors critical to successful implementation of a full-scale smart sensor network for SHM are discussed.