Long-term gage reliability for structural health monitoring of steel bridges

2012 ◽  
Author(s):  
Vasilis A. Samaras ◽  
Jeremiah Fasl ◽  
Matt Reichenbach ◽  
Todd Helwig ◽  
Sharon Wood ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Steel Bridges ◽  
Structural Health

An On-Going Monitoring Project of a New Timber Structure

2013 ◽  
Vol 778 ◽  
pp. 757-764 ◽  
Author(s):  
Francesca Lanata
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Environmental Conditions ◽  
Environmental Changes ◽  
Construction Material ◽  
Structural Response ◽  
Timber Structures ◽  
The Real ◽  
Structural Health

Structural design, regardless of construction material, is based mainly on deterministic codes that partially take into account the real structural response under service and environmental conditions. This approach can lead to overdesigned (and expensive) structures. The differences between the designed and the real behaviors are usually due to service loads not taken into account during the design or simply to the natural degradation of materials properties with time. This is particularly true for wood, which is strongly influenced by service and environmental conditions. Structural Health Monitoring can improve the knowledge of timber structures under service conditions, provide information on material aging and follow the degradation of the overall building performance with time.A long-term monitoring control has been planned on a three-floor structure composed by wooden trusses and composite concrete-wood slabs. The structure is located in Nantes, France, and it is the new extension to the Wood Science and Technology Academy (ESB). The main purpose of the monitoring is to follow the long-term structural response from a mechanical and energetic point of view, particularly during the first few service years. Both static and dynamic behavior is being followed through strain gages and accelerometers. The measurements will be further put into relation with the environmental changes, temperature and humidity in particular, and with the operational charges with the aim to improve the comprehension of long-term performances of wooden structures under service. The goal is to propose new improved and optimized methods to make timber constructions more efficient compared to other construction materials (masonry, concrete, steel).The paper will mainly focus on the criteria used to design the architecture of the monitoring system, the parameters to measure and the sensors to install. The first analyses of the measurements will be presented at the conference to have a feedback on the performance of the installed sensors and to start to define a general protocol for the Structural Health Monitoring of such type of timber structures.

Development of a Convolutional Neural Network Assisted Fiber Optics Based Passive Structural Health Monitoring System

2021 ◽  
Author(s):  
Ainulla Khan ◽  
Krishnan Balasubramaniam
Keyword(s):  
Structural Health Monitoring ◽  
Fiber Optics ◽  
Health Monitoring ◽  
Alternative Energy ◽  
Power Sources ◽  
Structural Health ◽  
Learning Framework ◽  
Fbg Sensors ◽  
Non Destructive

Abstract The continuous Non-Destructive Evaluation of assets for long-term assurance of performance has led to several developments over the deployment of a Real-Time Structural Health Monitoring (SHM) system. Considering the challenges involved under the implementation of an SHM system for the applications working under harsh environmental conditions with limited access to power sources this work is aimed to contribute towards overcoming those challenges by using the noise from the structure’s machinery or any ambient source as an alternative energy source and employing Fiber Optics based sensing, for its applicability under harsh environments. The required SHM system is realized with the cross-correlation of a fully diffused noise field, sensed using the Fiber Bragg Grating (FBG) sensors at two random locations. With no control on the input received as noise, to this end, a method is developed based on a Deep Learning framework, which is aimed towards a Universal Deployment of the passive SHM system. The methodology is designed to perform the health monitoring of the system, independent of the input perturbations. The validation performed on simulation data has demonstrated the feasibility of the developed technique towards the required kind of passive SHM system.

scholarly journals Structural Health Monitoring System Based on FBG Sensing Technique for Chinese Ancient Timber Buildings

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 110 ◽  
Author(s):  
Shao-Fei Jiang ◽  
Ze-Hui Qiao ◽  
Ni-Lei Li ◽  
Jian-Bin Luo ◽  
Sheng Shen ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Structural Deformation ◽  
Beam Deflection ◽  
Health State ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System

Due to the long-term service, Chinese ancient timber buildings show varying degrees of wear. Thus, structural health monitoring (SHM) for these cultural and historical treasures is desperately needed to evaluate the service status. Although there are some FBG sensing-based SHM systems, they are not suitable for Chinese ancient timber buildings due to the differences in architectural types, structural loads, materials, and environment. Besides, a technical gap in Fiber Bragg grating (FBG) sensing-based column inclination monitoring exists. To overcome these weaknesses, this paper develops an FBG sensing-based structural health monitoring system for Chinese ancient Chuan-dou-type timber buildings that aims at monitoring structural deformation, i.e., beam deflection and column inclination, temperature, humidity, and fire around the building. An in-situ test and simulation analyses were conducted to verify the effectiveness of the developed SHM system. To validate the long-term-operation of the developed SHM system, monitoring data within 15 months were analyzed. The results show good agreement between the developed SHM system in this paper and other methods. In addition, the SHM system operated well in the first year after its deployment. This implies that the developed SHM system is applicable and effective in the health state monitoring of Chinese ancient Chuan-dou-type timber buildings, laying a foundation for damage prognosis of such types of timber buildings.

Durability of Embedded PZTs in Structural Health Monitoring Systems under Cyclic Loading

2014 ◽  
Vol 891-892 ◽  
pp. 1255-1260 ◽  
Author(s):  
Sanghyun Yoo ◽  
Akbar Afaghi Khatibi ◽  
Everson Kandare
Keyword(s):  
Cyclic Loading ◽  
Structural Health Monitoring ◽  
Lead Zirconate Titanate ◽  
Health Monitoring ◽  
Low Cycle Fatigue ◽  
Maintenance Cost ◽  
Clear Understanding ◽  
Structural Health ◽  
Service Conditions

Structural Health Monitoring (SHM) systems are developed to decrease the maintenance cost and increase the life of engineering structures by fundamentally changing the way structural inspections are performed. However, this important objective can only be achieved through the consistent and predictable performance of a SHM system under different service conditions. The capability of a Piezoelectric lead Zirconate Titanate (PZT)-based SHM system in detecting structural flaws strongly depends on the sensor signals as well as actuator performance. But service conditions can change the behaviour of transducers, raising questions about long term SHM system capability. Although having a clear understanding of the reliable sensor life is important for surface mounted systems, however, this is particularly critical for embedded sensors. This is due to the fact that opportunity for replacement of sensors exists for surface bonded transducers while for the embedded systems, sensor replacement is not straightforward. Therefore, knowledge of the long term behaviour of embedded-SHM systems is critical for their implementation. This paper reports a study on the degradation of embedded PZT transducers under cyclic loadings. Carbon/epoxy laminates with an embedded PZT were subjected to fatigue loading and their performance was monitored using Scanning Laser Vibrometery (SLV). The functionality of PZT transducers under sensing and actuating modes were studied. High and low cycle fatigue tests were performed to establish strain-voltage relationships which can be used to identify critical cyclic loading parameters (number of cycles and R value) under sensing and actuating modes.

Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators

2020 ◽  
pp. 147592172091692 ◽  
Author(s):  
Sin-Chi Kuok ◽  
Ka-Veng Yuen ◽  
Stephen Roberts ◽  
Mark A Girolami
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Health Indicators ◽  
Operating Conditions ◽  
Learning Approach ◽  
Nonparametric Modeling ◽  
Structural Health ◽  
Model Free ◽  
Free Data

In this article, a novel propagative broad learning approach is proposed for nonparametric modeling of the ambient effects on structural health indicators. Structural health indicators interpret the structural health condition of the underlying dynamical system. Long-term structural health monitoring on in-service civil engineering infrastructures has demonstrated that commonly used structural health indicators, such as modal frequencies, depend on the ambient conditions. Therefore, it is crucial to detrend the ambient effects on the structural health indicators for reliable judgment on the variation of structural integrity. However, two major challenging problems are encountered. First, it is not trivial to formulate an appropriate parametric expression for the complicated relationship between the operating conditions and the structural health indicators. Second, since continuous data stream is generated during long-term structural health monitoring, it is required to handle the growing data efficiently. The proposed propagative broad learning provides an effective tool to address these problems. In particular, it is a model-free data-driven machine learning approach for nonparametric modeling of the ambient-influenced structural health indicators. Moreover, the learning network can be updated and reconfigured incrementally to adapt newly available data as well as network architecture modifications. The proposed approach is applied to develop the ambient-influenced structural health indicator model based on the measurements of 3-year full-scale continuous monitoring on a reinforced concrete building.

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