scholarly journals Structural Health Monitoring for Condition Assessment Using Efficient Supervised Learning Techniques

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 17 ◽  
Author(s):  
Alireza Entezami ◽  
Hashem Shariatmadar ◽  
Stefano Mariani
Keyword(s):  
Structural Health Monitoring ◽  
Supervised Learning ◽  
Health Monitoring ◽  
Learning Strategy ◽  
Structural Condition ◽  
Condition Assessment ◽  
Statistical Characteristics ◽  
Classification Methods ◽  
Structural Health ◽  
Vibration Data

Pattern recognition can be adopted for structural health monitoring (SHM) based on statistical characteristics extracted from raw vibration data. Structural condition assessment is an important step of SHM, since changes in the relevant properties may adversely affect the behavior of any structure. It looks therefore necessary to adopt efficient and robust approaches for the classification of different structural conditions using features extracted from the said raw data. To achieve this goal, it is essential to correctly distinguish the undamaged and damage states of the structure; the aim of this work is to present and compare classification methods using feature selection techniques to classify the structural conditions. All of the utilized classifiers need a training set pertinent to the undamaged/damaged conditions of the structure, as well as relevant class labels to be adopted in a supervised learning strategy. The performance and accuracy of the considered classification methods are assessed through a numerical benchmark concrete beam.

A Simplified Methodology for Condition Assessment of Bridge Bearings Using Vibration Based Structural Health Monitoring Techniques

2021 ◽  
pp. 2150133
Author(s):  
Babar Nasim Khan Raja ◽  
Saeed Miramini ◽  
Colin Duffield ◽  
Shilun Chen ◽  
Lihai Zhang
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Condition ◽  
Condition Assessment ◽  
Concrete Bridges ◽  
Traffic Loading ◽  
Structural Health ◽  
Vibration Data ◽  
Bridge Bearing ◽  
Bridge Bearings

The mechanical properties of bridge bearings gradually deteriorate over time resulting from daily traffic loading and harsh environmental conditions. However, structural health monitoring of in-service bridge bearings is rather challenging. This study presents a bridge bearing condition assessment framework which integrates the vibration data from a non-contact interferometric radar (i.e. IBIS-S) and a simplified analytical model. Using two existing concrete bridges in Australia as a case study, it demonstrates that the developed framework has the capability of detecting the structural condition of the bridge bearings in real-time. In addition, the results from a series of parametric studies show that the effectiveness of the developed framework is largely determined by the stiffness ratio between bridge bearing and girder ([Formula: see text], i.e. the structural condition of the bearings can only be effectively captured when the value of [Formula: see text] ranges from 1/100 and 100.

scholarly journals Establishment and Application of the Wind and Structural Health Monitoring System for the Runyang Yangtze River Bridge

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Hao Wang ◽  
Aiqun Li ◽  
Tong Guo ◽  
Tianyou Tao
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Yangtze River ◽  
Model Updating ◽  
Structural Condition ◽  
Condition Assessment ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System

Structural health monitoring can provide a practical platform for detecting the evolution of structural damage or performance deterioration of engineering structures. The final objective is to provide reasonable suggestions for structural maintenance and management and therefore ensure the structural safety according to the real-time recorded data. In this paper, the establishment of the wind and structural health monitoring system (WSHMS) implemented on the Runyang Yangtze River Bridge (RYRB) in China is introduced. The composition and functions of the WSHMS are presented. Thereinto, the sensory subsystem utilized to measure the input actions and structural output responses is introduced. And the core functions of the data management and analysis subsystem (DMAS) including model updating, structural condition identification, and structural condition assessment are illustrated in detail. A three-stage strategy is applied into the FE model updating of RYRB, and a two-phase strategy is proposed to adapt to structural health diagnosis and damage identification. Considering the structural integral security and the fatigue characteristic of steel material, the condition assessment of RYRB is divided into structural reliability assessment and structural fatigue assessment, which are equipped with specific and elaborate module for effective operation. This research can provide references for the establishment of the similar structural health monitoring systems on other cable-supported bridges.

scholarly journals Supervised learning strategy for classification and regression tasks applied to aeronautical structural health monitoring problems

Ultrasonics ◽  
2021 ◽  
Vol 113 ◽  
pp. 106372
Author(s):  
Roberto Miorelli ◽  
Andrii Kulakovskyi ◽  
Bastien Chapuis ◽  
Oscar D’Almeida ◽  
Olivier Mesnil
Keyword(s):  
Structural Health Monitoring ◽  
Supervised Learning ◽  
Health Monitoring ◽  
Learning Strategy ◽  
Structural Health ◽  
Classification And Regression

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

scholarly journals Condition assessment of reinforced concrete beams – Comparing digital image analysis with optic fibre Bragg gratings

2018 ◽  
Vol 199 ◽  
pp. 06011
Author(s):  
Elsabe Kearsley ◽  
SW Jacobsz
Keyword(s):  
Reinforced Concrete ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Construction Material ◽  
Bragg Gratings ◽  
Condition Assessment ◽  
Reinforced Concrete Beams ◽  
Large Strains ◽  
Structural Health ◽  
Concrete Elements

Reinforced concrete is the most widely used construction material and thus effective condition assessment of reinforced concrete elements forms a significant part of structural health monitoring. An effective structural health monitoring system should be able to give the owner prior warning that structural elements are reaching conditions approaching either serviceability or ultimate limit states. The aim of this investigation is to compare strain data recorded during load testing of a reinforced concrete beam using Fibre optic Bragg Gratings (FBG) and a photographic technique to determine circumstances most suitable for the use of each of the techniques. The test results indicate that FBG sensors can be used to detect small strains as well as large strains in uncracked concrete elements, while optical images can be used to accurately map crack development over the surface area of the structure.

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