Dead Load Based Damage Identification Method for Long-term Structural Health Monitoring

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.

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A multi-way data analysis approach for structural health monitoring of a cable-stayed bridge

Structural Health Monitoring ◽

10.1177/1475921718790727 ◽

2018 ◽

Vol 18 (1) ◽

pp. 35-48 ◽

Cited By ~ 7

Author(s):

Mehrisadat Makki Alamdari ◽

Nguyen Lu Dang Khoa ◽

Yang Wang ◽

Bijan Samali ◽

Xinqun Zhu

Keyword(s):

Data Analysis ◽

Structural Health Monitoring ◽

Health Monitoring ◽

Large Scale ◽

Damage Identification ◽

Tensor Analysis ◽

Support Vector ◽

Damage Scenarios ◽

Structural Health ◽

Cable Stayed Bridge

A large-scale cable-stayed bridge in the state of New South Wales, Australia, has been extensively instrumented with an array of accelerometer, strain gauge, and environmental sensors. The real-time continuous response of the bridge has been collected since July 2016. This study aims at condition assessment of this bridge by investigating three aspects of structural health monitoring including damage detection, damage localization, and damage severity assessment. A novel data analysis algorithm based on incremental multi-way data analysis is proposed to analyze the dynamic response of the bridge. This method applies incremental tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies. A total of 15 different damage scenarios were investigated; damage was physically simulated by locating stationary vehicles with different masses at various locations along the span of the bridge to change the condition of the bridge. The effect of damage on the fundamental frequency of the bridge was investigated and a maximum change of 4.4% between the intact and damage states was observed which corresponds to a small severity damage. Our extensive investigations illustrate that the proposed technique can provide reliable characterization of damage in this cable-stayed bridge in terms of detection, localization and assessment. The contribution of the work is threefold; first, an extensive structural health monitoring system was deployed on a cable-stayed bridge in operation; second, an incremental tensor analysis was proposed to analyze time series responses from multiple sensors for online damage identification; and finally, the robustness of the proposed method was validated using extensive field test data by considering various damage scenarios in the presence of environmental variabilities.

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

10.1115/qnde2021-75192 ◽

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.

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