Separation Method of Strain Caused by Concrete Shrinkage and Creep in Bridge Health Monitoring

2014 ◽  
Vol 668-669 ◽  
pp. 1421-1425
Author(s):  
Ya Feng Gong ◽  
Xiao Bo Sun ◽  
Xiang Hui Li ◽  
Yu Bo Jiao
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Field Measurement ◽  
Measurement Data ◽  
Element Model ◽  
Measured Data ◽  
Bridge Health Monitoring ◽  
Concrete Shrinkage ◽  
Shrinkage And Creep

The strain caused by concrete shrinkage and creep is separated through the field measurement data in this paper. Then D62 model and ACI model of finite element model is established to predict shrinkage and creep. Compared with the measured data, the feasibility and practicability of these two models can be proved.

Continuing Structural Health Monitoring after Repair: Starting from Scratch?

2008 ◽  
Vol 56 ◽  
pp. 495-501
Author(s):  
Jyrki Kullaa
Keyword(s):  
Finite Element ◽  
Numerical Model ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Measurement Data ◽  
Element Model ◽  
Structural Condition ◽  
Structural Health ◽  
Before And After

Aging structures need repairing if their lifetime is to be extended. If the structure has been monitored before and after repair, the information from both configurations can be utilized. The data before repair include the environmental or operational influences, whereas the data after repair represent the current structural condition. Also, if damage is proportional to the worked modifications, its extent can be assessed solely from the measurement data; no finite element model is needed. The proposed method is verified with a numerical model of a vehicle crane.

scholarly journals A Multiscale Finite Element Model Validation Method of Composite Cable-Stayed Bridge Based on Structural Health Monitoring System

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Rumian Zhong ◽  
Zhouhong Zong ◽  
Qiqi Liu ◽  
Haifei Zhou
Keyword(s):  
Finite Element ◽  
Structural Health Monitoring ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Element Model ◽  
Measured Data ◽  
Cable Stayed Bridge ◽  
Multiscale Fem ◽  
Multiscale Finite Element ◽  
Relative Errors

A two-step response surface method for multiscale finite element model (FEM) updating and validation is presented with respect to Guanhe Bridge, a composite cable-stayed bridge in the National Highway number G15, in China. Firstly, the state equations of both multiscale and single-scale FEM are established based on the basic equation in structural dynamic mechanics to update the multiscale coupling parameters and structural parameters. Secondly, based on the measured data from the structural health monitoring (SHM) system, a Monte Carlo simulation is employed to analyze the uncertainty quantification and transmission, where the uncertainties of the multiscale FEM and measured data were considered. The results indicate that the relative errors between the calculated and measured frequencies are less than 2%, and the overlap ratio indexes of each modal frequency are larger than 80% without the average absolute value of relative errors. These demonstrate that the proposed method can be applied to validate the multiscale FEM, and the validated FEM can reflect the current conditions of the real bridge; thus it can be used as the basis for bridge health monitoring, damage prognosis (DP), and safety prognosis (SP).

STRUCTURAL HEALTH MONITORING ORIENTED FINITE ELEMENT MODEL OF TSING MA BRIDGE TOWER

2007 ◽  
Vol 07 (04) ◽  
pp. 647-668 ◽  
Author(s):  
QING GUO FEI ◽  
YOU LIN XU ◽  
CHI LUN NG ◽  
K. Y. WONG ◽  
W. Y. CHAN ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Dynamic Characteristics ◽  
Health Monitoring ◽  
Field Measurement ◽  
Natural Frequencies ◽  
Element Model ◽  
Cable System ◽  
Tower Model ◽  
Bridge Tower

The modeling, updating and validation of a structural health monitoring oriented finite element model (FEM) of the Tsing Ma suspension bridge towers are presented in this paper. The portal-type bridge tower is composed of two hollow reinforced concrete legs and four deep pre-stressed cross-beams with a steel truss cast in the concrete of each cross-beam to form a narrow corridor for access between two legs. Except that steel trusses are modeled by beam elements, all structural components are modeled by solid elements to facilitate local damage detection, in particular at member joints. The established tower model is then updated using sensitivity-based model updating method taking the natural frequencies identified from field measurement data as reference. Furthermore, a two-level validation criterion is proposed and implemented to examine the replication performance of the updated finite element model of the bridge tower in terms of (1) natural frequencies in higher modes of vibration and (2) dynamic characteristics of the tower-cable system. The validation results show that a good replication of dynamic characteristics is achieved by the updated tower model when compared to the field measurement results. Finally, stress distribution and concentration of the bridge tower are investigated through nonlinear static analysis of the tower-cable system.

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 damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating

2020 ◽  
pp. 147592172092748 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Data Driven ◽  
Finite Element Model Updating

Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model.

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

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.

Sign in / Sign up

Export Citation Format

Share Document