• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.93-103
• /
• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• Smart Structures and Systems
• /
• v.15 no.1
• /
• pp.99-117
• /
• 2015

The Stonecutters Bridge (SCB) in Hong Kong is the third-longest cable-stayed bridge in the world with a main span stretching 1,018 m between two 298 m high single-leg tapering composite towers. A Wind and Structural Health Monitoring System (WASHMS) is being implemented on SCB by the Highways Department of The Hong Kong SAR Government, and the SCB-WASHMS is composed of more than 1,300 sensors in 15 types. In order to establish a linkage between structural health monitoring and maintenance management, a Structural Health Rating System (SHRS) with relevant rating tools and indices is devised. On the basis of a 3D space frame finite element model (FEM) of SCB and model updating, this paper presents the development of an SHR-oriented 3D multi-scale FEM for the purpose of load-resistance analysis and damage evaluation in structural element level, including modeling, refinement and validation of the multi-scale FEM. The refined 3D structural segments at deck and towers are established in critical segment positions corresponding to maximum cable forces. The components in the critical segment region are modeled as a full 3D FEM and fitted into the 3D space frame FEM. The boundary conditions between beam and shell elements are performed conforming to equivalent stiffness, effective mass and compatibility of deformation. The 3D multi-scale FEM is verified by the in-situ measured dynamic characteristics and static response. A good agreement between the FEM and measurement results indicates that the 3D multi-scale FEM is precise and efficient for WASHMS and SHRS of SCB. In addition, stress distribution and concentration of the critical segments in the 3D multi-scale FEM under temperature loads, static wind loads and equivalent seismic loads are investigated. Stress concentration elements under equivalent seismic loads exist in the anchor zone in steel/concrete beam and the anchor plate edge in steel anchor box of the towers.

• Structural Engineering and Mechanics
• /
• v.19 no.2
• /
• pp.123-139
• /
• 2005

An automatic modal identification program is developed for continuous extraction of modal parameters of three cable-supported bridges in Hong Kong which are instrumented with a long-term monitoring system. The program employs the Complex Modal Indication Function (CMIF) algorithm for identifying modal properties from continuous ambient vibration measurements in an on-line manner. By using the LabVIEW graphical programming language, the software realizes the algorithm in Virtual Instrument (VI) style. The applicability and implementation issues of the developed software are demonstrated by using one-year measurement data acquired from 67 channels of accelerometers permanently installed on the cable-stayed Ting Kau Bridge. With the continuously identified results, variability in modal vectors due to varying environmental conditions and measurement errors is observed. Such an observation is very helpful for selection of appropriate measured modal vectors for structural health monitoring use.

• Smart Structures and Systems
• /
• v.20 no.2
• /
• pp.139-150
• /
• 2017

The structural strain plays a significant role in structural condition assessment of in-service bridges in terms of structural bearing capacity, structural reliability level and entire safety redundancy. Therefore, it has been one of the most important parameters concerned by researchers and engineers engaged in structural health monitoring (SHM) practices. In this paper, an SHM system instrumented on the Jiubao Bridge located in Hangzhou, China is firstly introduced. This system involves nine subsystems and has been continuously operated for five years since 2012. As part of the SHM system, a total of 166 fiber Bragg grating (FBG) strain sensors are installed on the bridge to measure the dynamic strain responses of key structural components. Based on the strain monitoring data acquired in recent two years, the strain-based structural condition assessment of the Jiubao Bridge is carried out. The wavelet multi-resolution algorithm is applied to separate the temperature effect from the raw strain data. The obtained strain data under the normal traffic and wind condition and under the typhoon condition are examined for structural safety evaluation. The structural condition rating of the bridge in accordance with the AASHTO specification for condition evaluation and load and resistance factor rating of highway bridges is performed by use of the processed strain data in combination with finite element analysis. The analysis framework presented in this study can be used as a reference for facilitating the assessment, inspection and maintenance activities of in-service bridges instrumented with long-term SHM system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.1382-1385
• /
• 2007

In this paper, we study about the measuring algorithm that can implement Structural Health Monitoring (SHM) more efficiently by two measurement methods using smart sensor. Through the impedance measurement method, the damage condition of structures on wide area is monitored first, and then it changes the mode to guided wave measurement mode by mode switching algorithm when impedance measurement mode detects abnormal signals. Efficient handling of the real-time data would be available by analyzing location and shape of damage through guided wave measurement.

• Structural Engineering and Mechanics
• /
• v.18 no.5
• /
• pp.609-626
• /
• 2004

The Land Transport Authority of Singapore has a continuing program of highway bridge upgrading, to refurbish and strengthen bridges to allow for increasing vehicle traffic and increasing axle loads. One subject of this program has been a short span bridge taking a busy highway across a coastal inlet near a major port facility. Experiment-based structural assessments of the bridge were conducted before and after upgrading works including strengthening. Each assessment exercise comprised two separate components; a strain and acceleration monitoring exercise lasting approximately one month, and a full-scale dynamic test carried out in a single day. This paper reports the application of extreme value statistics to estimate bridge live loads using strain measurements.

• Earthquakes and Structures
• /
• v.19 no.1
• /
• pp.29-44
• /
• 2020

The effectiveness and the sensitivity of a Wireless impedance/Admittance Monitoring System (WiAMS) for the prompt damage diagnosis of two single-storey single-span Reinforced Concrete (RC) frames under cyclic loading is experimentally investigated. The geometrical and the reinforcement characteristics of the RC structural members of the frames represent typical old RC frame structure without consideration of seismic design criteria. The columns of the frames are vulnerable to shear failure under lateral load due to their low height-to-depth ratio and insufficient transverse reinforcement. The proposed Structural Health Monitoring (SHM) system comprises of specially manufactured autonomous portable devices that acquire the in-situ voltage frequency responses of a network of twenty piezoelectric transducers mounted to the RC frames. Measurements of external and internal small-sized piezoelectric patches are utilized for damage localization and assessment at various and increased damage levels as the magnitude of the imposed lateral cycle deformations increases. A bare RC frame and a strengthened one using a pair of steel crossed tension-ties (X-bracing) have been tested in order to check the sensitivity of the developed WiAMS in different structural conditions since crack propagation, damage locations and failure mode of the examined frames vary. Indeed, the imposed loading caused brittle shear failure to the column of the bare frame and the formation of plastic hinges at the beam ends of the X-braced frame. Test results highlighted the ability of the proposed SHM to identify incipient damages due to concrete cracking and steel yielding since promising early indication of the forthcoming critical failures before any visible sign has been obtained.

• Structural Monitoring and Maintenance
• /
• v.1 no.2
• /
• pp.173-182
• /
• 2014

We describe the application of a distributed optical fiber sensor based on stimulated Brillouin scattering, as an integrated system for safety monitoring of railway infrastructures. The strain distribution was measured statically and dynamically along 60 meters of rail track, as well as along a 3-m stone arch bridge. We show that, gluing an optical fiber along the rail track, traffic monitoring can be performed in order to identify the train passage over the instrumented sector and determine its running conditions. Furthermore, dynamic and static strain measurements on a rail bridge are reported, aimed to detect potential structural defects. The results indicate that distributed sensing technology represents a valuable tool in railway traffic and safety monitoring.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.10 no.2
• /
• pp.191-202
• /
• 2006

Computer programs for a structure design help the optimum design that considers each condition. however, the findings can not explain accurately a behavior of the real-living structure because each condition of a structure is simplified and generalized. The smart structure is introduced to overcome these problems, and we can understand a behavior of the real-living structure by means of Health Monitoring System. In this study, we compare a behavior by means of the existing structure design with a behavior of the living structure by means of an experiment. As a result, we examine adequacy of a measuring system and developing possibility in the future.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.36 no.1
• /
• pp.45-52
• /
• 2016

Blast furnaces are crucial equipment for steel production. A typical furnace risks unexpected accidents caused by contraction and expansion of the walls under an environment of high temperature and pressure. In this study, an acoustic emission (AE) monitoring system was tested for evaluating the large-scale structural health of a blast furnace. Based on the growth of shell cracks with the emission of high energy levels, severe damage can be detected by monitoring increases in the AE energy parameter. Using this monitoring system, steel mill operators can establish a maintenance period, in which actual shell cracks can be verified by cross-checking the UT. From this study, we expect that AE systems permit early fault detection for structural health monitoring by establishing evaluation criteria based on the severity of shell cracking.