• Smart Structures and Systems
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• 제20권2호
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• pp.139-150
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• 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.

• Structural Engineering and Mechanics
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• 제81권5호
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• pp.647-664
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• 2022

The powerful data mapping capability of computational deep learning methods has been recently explored in academic works to develop strategies for structural health monitoring through appropriate characterization of dynamic responses. In many cases, these studies concern laboratory prototypes and finite element models to validate the proposed methodologies. Therefore, the present work aims to investigate the capability of a deep learning algorithm called Sparse Autoencoder (SAE) specifically focused on detecting structural alterations in real-case studies. The idea is to characterize the dynamic responses via SAE models and, subsequently, to detect the onset of abnormal behavior through the Shewhart T control chart, calculated with SAE extracted features. The anomaly detection approach is exemplified using data from the Z24 bridge, a classical benchmark, and data from the continuous monitoring of the San Vittore bell-tower, Italy. In both cases, the influence of temperature is also evaluated. The proposed approach achieved good performance, detecting structural changes even under temperature variations.

• Smart Structures and Systems
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• 제5권4호
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• pp.469-482
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• 2009

The emerging sensor-based structural health monitoring (SHM) technology has a potential for cost-effective maintenance of aging civil infrastructure systems. The author proposes to integrate continuous and global monitoring using on-structure sensors with targeted local non-destructive evaluation (NDE). Significant technical challenges arise, however, from the lack of cost-effective sensors for monitoring spatially large structures, as well as reliable methods for interpreting sensor data into structural health conditions. This paper reviews recent efforts and advances made in addressing these challenges, with example sensor hardware and health monitoring software developed in the author's research center. The hardware includes a novel fiber optic accelerometer, a vision-based displacement sensor, a distributed strain sensor, and a microwave imaging NDE device. The health monitoring software includes a number of system identification methods such as the neural networks, extended Kalman filter, and nonlinear damping identificaiton based on structural dynamic response measurement. These methods have been experimentally validated through seismic shaking table tests of a realistic bridge model and tested in a number of instrumented bridges and buildings.

• Smart Structures and Systems
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• 제7권2호
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• pp.83-102
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• 2011

As a testbed for various structural health monitoring (SHM) technologies, a super-tall structure - the 610 m-tall Guangzhou Television and Sightseeing Tower (GTST) in southern China - is currently under construction. This study aims to explore state-of-the-art wireless sensing technologies for monitoring the ambient vibration of such a super-tall structure during construction. The very nature of wireless sensing frees the system from the need for extensive cabling and renders the system suitable for use on construction sites where conditions continuously change. On the other hand, unique technical hurdles exist when deploying wireless sensors in real-life structural monitoring applications. For example, the low-frequency and low-amplitude ambient vibration of the GTST poses significant challenges to sensor signal conditioning and digitization. Reliable wireless transmission over long distances is another technical challenge when utilized in such a super-tall structure. In this study, wireless sensing measurements are conducted at multiple heights of the GTST tower. Data transmission between a wireless sensing device installed at the upper levels of the tower and a base station located at the ground level (a distance that exceeds 443 m) is implemented. To verify the quality of the wireless measurements, the wireless data is compared with data collected by a conventional cable-based monitoring system. This preliminary study demonstrates that wireless sensing technologies have the capability of monitoring the low-amplitude and low-frequency ambient vibration of a super-tall and slender structure like the GTST.

• 국제초고층학회논문집
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• 제4권1호
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• pp.39-44
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• 2015

This paper presents the structural health monitoring (SHM) of Shanghai Tower. In order to provide useful information for safety evaluation and regular maintenance under construction and in-service condition, a comprehensive structural health monitoring (SHM) system is installed in Shanghai Tower, which is composed of a main monitoring station and eleven substations. Structural responses at different construction stages are measured using this SHM system and presented in this study. Meanwhile, a detailed finite element model (FEM) is created and comparison of results between SHM and FEM is carried out. Results indicate that the time-dependent property of concrete creep is of great importance to structural response and the measured data can be used in FEM updating to obtain more accurate FEM models at different construction stages. Therefore, installation of structural health monitoring system in super-tall buildings could be considered as an effective way to assure structural safety during the construction process.

• Structural Monitoring and Maintenance
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• 제3권1호
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• pp.33-49
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• 2016

Structural Health Monitoring (SHM) has been attracting numerous research efforts around the world because it targets at monitoring structural conditions and performance to prevent catastrophic failure, and to provide quantitative data for engineers and infrastructure owners to design a reliable and economical asset management strategy. In the past decade, with supports from Australian Research Council (ARC), Cooperative Research Center for Infrastructure and Engineering Asset Management (CIEAM), CSIRO and industry partners, intensive research works have been conducted in the School of Civil, Environmental and Mining Engineering, University of Western Australia and Centre for Infrastructural Monitoring and Protection, Curtin University on various techniques of SHM. The researches include the development of hardware, software and various algorithms, such as various signal processing techniques for operational modal analysis, modal analysis toolbox, non-model based methods for assessing the shear connection in composite bridges and identifying the free spanning and supports conditions of pipelines, vibration based structural damage identification and model updating approaches considering uncertainty and noise effects, structural identification under moving loads, guided wave propagation technique for detecting debonding damage, and relative displacement sensors for SHM in composite and steel truss bridges. This paper aims at summarizing and reviewing the recent research advances on SHM of civil infrastructure in Western Australia.

• Smart Structures and Systems
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• 제29권1호
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Structural Engineering and Mechanics
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• 제30권2호
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• pp.191-209
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• 2008

A structural monitoring system based on cheap and wireless monitoring system is investigated in this paper. Due to low-cost and low power consumption, micro-electro-mechanical system (MEMS) is suitable for wireless monitoring and the use of MEMS and wireless communication can reduce system cost and simplify the installation for structural health monitoring. For system identification using wireless MEMS, a finite element (FE) model updating method through correlation with the initial analytical model of the structure to the measured one is used. The system identification using wireless MEMS is evaluated experimentally using a three storey frame model. Identification results are compared to ones using data measured from traditional accelerometers and results indicate that the system identification using wireless MEMS estimates system parameters with reasonable accuracy. Another smart sensor considered in this paper for structural health monitoring is Lead Zirconate Titanate (PZT) which is a type of piezoelectric material. PZT patches have been applied for the health monitoring of structures owing to their simultaneous sensing/actuating capability. In this paper, the system identification for building structures by using PZT patches functioning as sensor only is presented. The FE model updating method is applied with the experimental data obtained using PZT patches, and the results are compared to ones obtained using wireless MEMS system. Results indicate that sensing by PZT patches yields reliable system identification results even though limited information is available.

• Structural Monitoring and Maintenance
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• 제1권1호
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• pp.1-45
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• 2014

The developments and implementations of the structural health monitoring (SHM) system for large infrastructures have been gradually recognized by researchers, engineers and administrative authorities in the last decades. This paper summarizes an updated review on innovations and applications in SHM for infrastructures carried out by researchers at Dalian University of Technology. Invented sensors and data acquisition system are firstly briefly described. And then, some proposed theories and methods including the sensing technology, sensor placement method, signal processing and data fusion, system identification and damage detection are discussed in details. Following those, the activities on the standardization of SHM and several case applications on specific types of structure are reviewed. Finally, existing problems and promising research efforts in the field of SHM are given.

• 풍력에너지저널
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• 제9권4호
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.