scholarly journals Probabilistic Damage Detection and Identification of Coupled Structural Parameters using Bayesian Model Updating with Added Mass

2021 ◽  
Author(s):  
jice zeng ◽  
Young Hoon Kim
Keyword(s):  
Convergence Rate ◽  
Damage Detection ◽  
Bayesian Model ◽  
Model Updating ◽  
Coupling Effect ◽  
Structural Parameters ◽  
Added Mass ◽  
Distribution Functions ◽  
Single Chain ◽  
Characteristic Equations

Damage detection inevitably involves uncertainties originated from measurement noise and modeling error. It may cause incorrect damage detection results if not appropriately treating uncertainties. To this end, vibration-based Bayesian model updating (VBMU) is developed to utilize vibration responses or modal parameters to identify structural parameters (e.g., mass and stiffness) as probability distribution functions (PDF) and uncertainties. However, traditional VBMU often assumes that mass is well known and invariant because simultaneous identification of mass and stiffness may yield an unidentifiable problem due to the coupling effect of the mass and stiffness. In addition, the posterior PDF in VBMU is usually approximated by single-chain based Markov Chain Monte Carlo (MCMC), leading to a low convergence rate and limited capability for complex structures. This paper proposed a novel VBMU to address the coupling effect and identify mass and stiffness by adding known mass. Two vibration data sets are acquired from original and modified systems with added mass, giving the new characteristic equations. Then, the posterior PDF is reformulated by measured data and predicted counterparts from new characteristic equations. For efficiently approximating the posterior PDF, Differential Evolutionary Adaptive Metropolis (DREAM) Algorithm are adopted to draw samples by running multiple Markov chains parallelly to enhance convergence rate and sufficiently explore possible solutions. Finally, a numerical example with a ten-story shear building and a laboratory-scale three-story frame structure are utilized to demonstrate the efficacy of the proposed VBMU framework. The results show that the proposed method can successfully identify both mass and stiffness, and their uncertainties. Reliable probabilistic damage detection can also be achieved.

Identification of Structural Stiffness and Mass using Bayesian Model Updating Approach with Known Added Mass: Numerical Investigation

2020 ◽  
Vol 20 (11) ◽  
pp. 2050123
Author(s):  
Jice Zeng ◽  
Young Hoon Kim
Keyword(s):  
Bayesian Approach ◽  
Bayesian Model ◽  
Prior Probability ◽  
Model Updating ◽  
Coupling Effect ◽  
Structural Parameters ◽  
Original System ◽  
New Approach ◽  
Vibration Data ◽  
Structural Mass

The Bayesian model updating approach (BMUA) has been widely used to update structural parameters using modal measurements because of its powerful ability to handle uncertainties and incomplete data. However, a conventional BMUA is mainly used to update stiffness with the assumption that structural mass is known. Because simultaneously updating stiffness and mass leads to unidentifiable case or coupling effect of stiffness and mass, this assumption in conventional BMUA is questionable to update stiffness when the mass has significantly changed. This study proposes a new updating framework based on two structural systems: original and modified systems. A modified system is created by adding known mass to the original system. Different from the conventional Bayesian approach, two sets of measured vibration data in the proposed Bayesian approach are obtainable to address the coupling effect existing in the conventional Bayesian approach. To this end, a new approach reformulates the prior probability distribution function and the objective function. Two numerical simulations are considered to demonstrate the performance of the proposed approach, including (1) parameter identification, (2) posterior uncertainties, (3) probabilistic damage detections. The proposed BMUA outperforms a conventional BMUA in identifying both stiffness and mass.

A Multi-State Strategy for Structural Damage Detection Using Sensitivity of Weighted Transmissibility Function

2021 ◽  
pp. 2150144
Author(s):  
Ziwei Luo ◽  
Huanlin Liu ◽  
Ling Yu
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Model Updating ◽  
Structural Parameters ◽  
Experimental Studies ◽  
Fe Model ◽  
Limited Information ◽  
Structural Damage Detection ◽  
State Strategy ◽  
Output Only

In practice, a model-based structural damage detection (SDD) method is helpful for locating and quantifying damages with the aid of reasonable finite element (FE) model. However, only limited information in single or two structural states is often used for model updating in existing studies, which is not reasonable enough to represent real structures. Meanwhile, as an output-only damage indicator, transmissibility function (TF) is proven to be effective for SDD, but it is not sensitive enough to change in structural parameters. Therefore, a multi-state strategy based on weighted TF (WTF) is proposed to improve sensitivity of TF to change in parameters and in order to further obtain a more reasonable FE model for SDD in this study. First, WTF is defined by TF weighted with element stiffness matrix, and relationships between WTFs and change in structural parameters are established based on sensitivity analysis. Then, a multi-state strategy is proposed to obtain multiple structural states, which is used to reasonably update the FE model and detect structural damages. Meanwhile, due to fabrication errors, a two-stage scheme is adopted to reduce the global and local discrepancy between the real structure and the FE model. Further, the [Formula: see text]-norm and the [Formula: see text]-norm regularization techniques are, respectively, introduced for both model updating and SDD problems by considering the characteristics of problems. Finally, the effectiveness of the proposed method is verified by a simply supported beam in numerical simulations and a six-storey frame in laboratory. From the simulation results, it can be seen that the sensitivity to structural damages can be improved by the definition of WTF. For the experimental studies, compared with the FE model updated from the single structural state, the FE model obtained by the multi-state strategy has an ability to more reasonably describe the change of states in the frame. Moreover, for the given structural damages, the proposed method can detect damage locations and degrees accurately, which shows the validity of the proposed method and the reliability of the updated FE model.

Damage detection in an offshore platform using incomplete noisy FRF data by a novel Bayesian model updating method

2020 ◽  
Vol 217 ◽  
pp. 108023 ◽  
Author(s):  
Amin Fathi ◽  
Akbar Esfandiari ◽  
Manouchehr Fadavie ◽  
Alireza Mojtahedi
Keyword(s):  
Damage Detection ◽  
Bayesian Model ◽  
Model Updating ◽  
Offshore Platform

A Probabilistic Model Updating Algorithm for Fatigue Damage Detection in Aluminum Hull Structures

2010 ◽  
Author(s):  
Masahiro Kurata ◽  
Jun-Hee Kim ◽  
Jerome P. Lynch ◽  
Kincho H. Law ◽  
Liming W. Salvino
Keyword(s):  
Damage Detection ◽  
Bayesian Model ◽  
High Speed ◽  
Model Updating ◽  
Detection Method ◽  
Residual Life ◽  
Search Space ◽  
Computational Effort ◽  
Detection Approach ◽  
Naval Engineering

The use of aluminum alloys in the design of naval structures offers the benefit of light-weight ships that can travel at high-speed. However, the use of aluminum poses a number of challenges for the naval engineering community including higher incidence of fatigue-related cracks. Early detection of fatigue induced cracks enhances maintenance of the ships and is critical for preventing the catastrophic failure of the hull. Furthermore, monitoring the integrity of the aluminum hull can provide valuable information for estimating the residual life of hull components. This paper presents a model-based damage detection methodology for fatigue assessment of hulls that are instrumented with a long-term hull monitoring system. At the core of the data driven damage detection approach is a Bayesian model updating algorithm enhanced with systematic enumeration and pruning of candidate solutions. The Bayesian model updating approach significantly reduce the computational effort by systematically narrowing the search space using errors functions constructed using the estimated modal properties associated with the condition of the structure. This study proposes the use of the Bayesian model updating technique to detect damage in an aluminum panel modeled using high-fidelity finite element models. The performance of the proposed damage detection method is tested through simulation of a progressively growing fatigue crack introduced in the vicinity of a welded stiffener element. An experimental study verifies the accuracy of the proposed damage detection method using an aluminum plate excited with a controlled excitation in the laboratory.

scholarly journals Stiffness Modification-Based Bayesian Finite Element Model Updating to Solve Coupling Effect of Structural Parameters: Formulations

2021 ◽  
Vol 11 (22) ◽  
pp. 10615
Author(s):  
Jice Zeng ◽  
Young Hoon Kim
Keyword(s):  
Model Updating ◽  
Coupling Effect ◽  
Structural Parameters ◽  
Linear Optimization ◽  
Hessian Matrix ◽  
Element Model ◽  
Optimization Method ◽  
Finite Element Model Updating ◽  
Damage Estimation ◽  
Objective Functions

The Bayesian model updating approach (BMUA) benefits from identifying the most probable values of structural parameters and providing uncertainty quantification. However, the traditional BMUA is often used to update stiffness only with the assumption of well-known mass, which allows unidentifiable cases induced by the coupling effect of mass and stiffness to be circumvented and may not be optimal for structures experiencing damages in both mass and stiffness. In this paper, the new BMUA tailored to estimating both mass and stiffness is presented by using two measurement states (original and modified systems). A new eigenequation with a stiffness-modified system is formulated to address the coupling effect of mass and stiffness. The posterior function is treated using an asymptotic approximation method, giving the new objective functions with stiffness modification. Analytical formulations of modal parameters and structural parameters are then derived by a linear optimization method. In addition, the covariance matrix of uncertain parameters is determined by the inverse of the Hessian matrix of the objective function. The performance of the proposed BMUA is evaluated through two numerical examples in this study; a probabilistic damage estimation is also implemented. The results show the proposed BMUA is superior to the traditional one in mass and stiffness updating.

scholarly journals Bayesian Model Updating Based on Kriging Surrogate Model and Simulated Annealing Algorithm

2022 ◽  
Vol 2148 (1) ◽  
pp. 012008
Author(s):  
Zenghui Wang ◽  
Hong Yin ◽  
Zhenrui Peng
Keyword(s):  
Simulated Annealing ◽  
Surrogate Model ◽  
Bayesian Model ◽  
Simulated Annealing Algorithm ◽  
Model Updating ◽  
Structural Parameters ◽  
Dynamic Responses ◽  
Monte Carlo Algorithm ◽  
Kriging Surrogate Model ◽  
Annealing Algorithm

Abstract Aiming at the problem of difficulty in selecting the proposal distribution and low computational efficiency in the traditional Markov chain Monte Carlo algorithm, a Bayesian model updating method using surrogate model technology and simulated annealing algorithm is proposed. Firstly, the Kriging surrogate model is used to mine the implicit relationship between the structural parameters to be updated and the corresponding dynamic responses, and the Kriging model that meets the accuracy requirement is used to replace the complex finite element model to participate in the iterative calculation to improve the model updating efficiency. Then, the simulated annealing algorithm is introduced to reorganize the Markov chains from different proposal distributions to obtain high-quality posterior samples, which are used to estimate the parameters posterior distributions. Finally, a space truss structure is used to verify the effectiveness of the proposed method.

Enhanced Sensitivity for Structural Damage Detection Using Incomplete Modal Data

2018 ◽  
Vol 18 (04) ◽  
pp. 1850054 ◽  
Author(s):  
Akbar Esfandiari ◽  
Maryam Vahedi
Keyword(s):  
Damage Detection ◽  
Mode Shape ◽  
Structural Damage ◽  
Structural Model ◽  
Model Updating ◽  
Early Stage ◽  
Structural Parameters ◽  
Accurate Estimation ◽  
Sensitivity Equation ◽  
Shape Data

The necessity of detecting structural damages in an early stage has led to the development of various procedures for structural model updating. In this regard, sensitivity-based model updating methods utilizing mode shape data are known as effective tools. For this purpose, accurate estimation of the mode shape changes is desired to achieve successful model updating. In this paper, Wang’s method is improved by including measured natural frequencies of the damaged structure in derivation of the sensitivity equation. The sensitivity equation is then solved using an incomplete subset of mode shape data in evaluation of the changes of the structural parameters. A comparative study of the results obtained by the proposed method with those by the modal method for a truss and a frame model indicated that the former is significantly more effective for damage detection than the latter. Furthermore, the capability of the proposed method for model updating in the presence of measurement and mass modeling errors is investigated.

scholarly journals Vision-Based Structural FE Model Updating Using Genetic Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 1622
Author(s):  
Gun Park ◽  
Ki-Nam Hong ◽  
Hyungchul Yoon
Keyword(s):  
Genetic Algorithm ◽  
Model Updating ◽  
Structural Parameters ◽  
Fe Model ◽  
Structural Members ◽  
Stiffness Reduction ◽  
Before And After ◽  
Scale Test ◽  
Fe Model Updating ◽  
Story Building

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

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