Finite element model updating of existing steel bridge based on structural health monitoring

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.

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Structural Health Monitoring-Oriented Finite-Element Model for a Large Transmission Tower

International Journal of Civil Engineering ◽

10.1007/s40999-016-0069-3 ◽

2016 ◽

Vol 16 (1) ◽

pp. 79-92 ◽

Cited By ~ 10

Author(s):

XiaoXiang Cheng ◽

Jun Dong ◽

XiaoLin Han ◽

QingGuo Fei

Keyword(s):

Finite Element ◽

Structural Health Monitoring ◽

Finite Element Model ◽

Health Monitoring ◽

Element Model ◽

Transmission Tower ◽

Structural Health

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

Structural Health Monitoring ◽

10.1177/1475921720939518 ◽

2020 ◽

pp. 147592172093951 ◽

Cited By ~ 1

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.

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