ADVANCED FINITE ELEMENT MODEL OF TSING MA BRIDGE FOR STRUCTURAL HEALTH MONITORING

2011 ◽  
Vol 11 (02) ◽  
pp. 313-344 ◽  
Author(s):  
Y. F. DUAN ◽  
Y. L. XU ◽  
Q. G. FEI ◽  
K. Y. WONG ◽  
K. W. Y. CHAN ◽  
...  
Keyword(s):  
Finite Element ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Monitoring System ◽  
Health Monitoring ◽  
Element Model ◽  
Structural Performance ◽  
3D Finite Element ◽  
Health Monitoring System ◽  
Tsing Ma Bridge

The Tsing Ma Bridge is a cable suspension bridge carrying both highway and railway. A bridge health monitoring system called wind and structural health monitoring system (WASHMS) has been installed in the Tsing Ma Bridge and operated since 1997 to monitor the structural performance and its associated loads and environments. However, there exists a possibility that the worst structural conditions may not be directly monitored due to the limited number of sensors and the complexity of structure and loading conditions. Therefore, it is an essential task to establish structural performance relationships between the critical locations/components of the bridge and those instrumented by the WASHMS. Meanwhile, to develop and validate practical and effective structural damage detection techniques and safety evaluation strategies, the conventional modeling for cable-supported bridges by approximating the bridge deck as continuous beams or grids is not applicable for simulation of real damage scenarios. To fulfil these tasks, a detailed full three-dimensional (3D) finite element model of the Tsing Ma Bridge is currently established for direct computation of the stress/strain states for all important bridge components. This paper presents the details of establishing this full 3D finite element model and its calibration. The major structural components are modeled in detail and the connections and boundary conditions are modeled properly, which results in about half million elements for the complete bridge model. The calibration of vibration modes and stresses/strains due to passing trains is carried out, and a good agreement is found between the computed and measured results.

Structural Health Monitoring using deep learning with optimal finite element model generated data

2020 ◽  
Vol 145 ◽  
pp. 106972 ◽  
Author(s):  
Panagiotis Seventekidis ◽  
Dimitrios Giagopoulos ◽  
Alexandros Arailopoulos ◽  
Olga Markogiannaki
Keyword(s):  
Finite Element ◽  
Deep Learning ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Element Model ◽  
Structural Health

Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating

2018 ◽  
Vol 18 (4) ◽  
pp. 1189-1206 ◽  
Author(s):  
Dimitrios Giagopoulos ◽  
Alexandros Arailopoulos ◽  
Vasilis Dertimanis ◽  
Costas Papadimitriou ◽  
Eleni Chatzi ◽  
...  
Keyword(s):  
Finite Element ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Fatigue Damage ◽  
Health Monitoring ◽  
Model Updating ◽  
Element Model ◽  
Finite Element Model Updating ◽  
Damage Estimation ◽  
Vibration Measurements

Structural Health Monitoring System for High-Rising Buildings and its Application for Shanghai Tower

2011 ◽  
Vol 94-96 ◽  
pp. 1099-1104
Author(s):  
Lu Chen
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Weather Conditions ◽  
Severe Weather ◽  
Performance Data ◽  
Structural Performance ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System

Structural health monitoring system for high-rising buildings is a new structural effort that tries to make proper maintaining strategy to extend structural life of the building by collecting structural performance data under normal loads as well as severe weather conditions. This paper introduces the monitoring items of Shanghai Tower.

An inverse elastic method of crack identification based on sparse strain sensing sheet

2020 ◽  
pp. 147592172093951 ◽  
Author(s):  
Zeyu Xiong ◽  
Branko Glisic
Keyword(s):  
Finite Element ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Phase Field ◽  
Direct Contact ◽  
Crack Detection ◽  
Element Model ◽  
Crack Identification ◽  
Structural Health

Reliable damage detection over large areas of structures can be achieved by spatially quasi-continuous structural health monitoring enabled by two-dimensional sensing sheets. They contain dense arrays of short-gauge sensors, which increases the probability to have sensors in direct contact with damage (e.g. crack opening) and thus identify (i.e. detect, localize, and quantify) it at an early stage. This approach in damage identification is called direct sensing. Although the sensing sheet is a reliable and low-cost technology, the overall structural health monitoring system that is using it might become complex due to large number of sensors. Hence, intentional reduction in number of sensors might be desirable. In addition, malfunction of sensors can occur in real-life settings, which results in unintentional reduction in the number of functioning sensors. In both cases, reduction in the number of (functioning) sensors may lead to lack of performance of sensing sheet. Therefore, it is important to explore the performance of sparse arrays of sensors, in the cases where sensors are not necessarily in direct contact with damage (indirect sensing). The aim of this research is to create a method for optimizing the design of arrays of sensors, that is, to find the smallest number of sensors while maintaining a satisfactory reliability of crack detection and accuracy of damage localization and quantification. To achieve that goal, we first built a phase field finite element model of cracked structure verified by the analytical model to determine the crack existence (detection), and then we used the algorithm of inverse elastostatic problem combined with phase field finite element model to determine the crack length (quantification) and location (localization) by minimizing the difference between the sensor measurements and the phase field finite element model results. In addition, we experimentally validated the method by means of a reduced-scale laboratory test and assessed the accuracy and reliability of indirect sensing.

scholarly journals Validation of a procedure for the evaluation of the performance of an installed structural health monitoring system

2018 ◽  
Vol 18 (5-6) ◽  
pp. 1557-1568 ◽  
Author(s):  
Sebastian Heinlein ◽  
Peter Cawley ◽  
Thomas Vogt
Keyword(s):  
Finite Element ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Monitoring System ◽  
Health Monitoring ◽  
Complex Structure ◽  
Detection Ability ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System

Validation of the performance of guided wave structural health monitoring systems is vital if they are to be widely deployed; testing the damage detection ability of a system by introducing different types of damage at varying locations is very costly and cannot be performed on a system in operation. Estimating the damage detection ability of a system solely by numerical simulations is not possible as complex environmental effects cannot be accounted for. In this study, a methodology was tested and verified that uses finite element simulations to superimpose defect signals onto measurements collected from a defect-free structure. These signals are acquired from the structure of interest under varying environmental and operational conditions for an initial monitoring period. Measurements collected in a previous blind trial of an L-shaped pipe section, onto which a number of corrosion-like defects were introduced, were utilised during this investigation. The growth of three of these defects was replicated using finite element analysis and the simulated reflections were superimposed onto signals collected on the defect-free test pipe. The signal changes and limits of reliable detection predicted from the synthetic defect reflections superimposed on the measurements from the undamaged complex structure agreed well with the changes due to real damage measured on the same structure. This methodology is of great value for any structural health monitoring system as it allows for the minimum detectable defect size to be estimated for specific geometries and damage locations in a quick and efficient manner without the need for multiple test structures while accounting for environmental variations.

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