scholarly journals Complete Inverse Method Using Ant Colony Optimization Algorithm for Structural Parameters and Excitation Identification from Output Only Measurements

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Jun Chen ◽  
Xin Chen ◽  
Wei Liu
Keyword(s):  
Structural Health Monitoring ◽  
Objective Function ◽  
Ant Colony Optimization ◽  
Health Monitoring ◽  
Inverse Method ◽  
Structural Parameters ◽  
Ant Colony ◽  
Computation Method ◽  
Structural Health ◽  
Output Only

In vibration-based structural health monitoring of existing large civil structures, it is difficult, sometimes even impossible, to measure the actual excitation applied to structures. Therefore, an identification method using output-only measurements is crucial for the practical application of structural health monitoring. This paper integrates the ant colony optimization (ACO) algorithm into the framework of the complete inverse method to simultaneously identify unknown structural parameters and input time history using output-only measurements. The complete inverse method, which was previously suggested by the authors, converts physical or spatial information of the unknown input into the objective function of an optimization problem that can be solved by the ACO algorithm. ACO is a newly developed swarm computation method that has a very good performance in solving complex global continuous optimization problems. The principles and implementation procedure of the ACO algorithm are first introduced followed by an introduction of the framework of the complete inverse method. Construction of the objective function is then described in detail with an emphasis on the common situation wherein a limited number of actuators are installed on some key locations of the structure. Applicability and feasibility of the proposed method were validated by numerical examples and experimental results from a three-story building model.

Optimal Accelerometer Locations for Structural Health Monitoring Using Time-Domain System Identification

2006 ◽  
Vol 321-323 ◽  
pp. 273-277 ◽  
Author(s):  
Soon Jung Kwon ◽  
Hae Sung Lee ◽  
Soo Bong Shin
Keyword(s):  
System Identification ◽  
Structural Health Monitoring ◽  
Fisher Information ◽  
Health Monitoring ◽  
Time Domain ◽  
Fisher Information Matrix ◽  
Structural Parameters ◽  
Hessian Matrix ◽  
Information Matrix ◽  
Structural Health

The paper presents two algorithms for determining optimal accelerometer locations for structural health monitoring when structural condition is assessed by a system identification scheme in time-domain. The accelerometer locations are determined by ranking the components of an effective independent distribution vector computed from a Fisher information matrix. One of the proposed algorithms formulates a Fisher information matrix by multiplying acceleration matrix with its transpose and the other as a Gauss-Newton Hessian matrix composed of acceleration sensitivities with respect to structural parameters. Since the structural parameters cannot be known exactly in an actual application, a statistical approach is proposed by setting an error bound between the actual and the baseline values. To examine the algorithm, simulation studies have been carried out on a two-span planar truss. The results using locations selected by the two algorithms were compared.

scholarly journals Yonjung High-Speed Railway Bridge Assessment Using Output-Only Structural Health Monitoring Measurements under Train Speed Changing

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu ◽  
Mohamed A. Sayed
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Prestressed Concrete ◽  
High Speed ◽  
Model Identification ◽  
Steel Girders ◽  
Structural Health ◽  
High Speed Trains ◽  
Output Only ◽  
Bridge Assessment

Yonjung Bridge is a hybrid multispan bridge that is designed to transport high-speed trains (HEMU-430X) with maximum operating speed of 430 km/h. The bridge consists of simply supported prestressed concrete (PSC) and composite steel girders to carry double railway tracks. The structural health monitoring system (SHM) is designed and installed to investigate and assess the performance of the bridge in terms of acceleration and deformation measurements under different speeds of the passing train. The SHM measurements are investigated in both time and frequency domains; in addition, several identification models are examined to assess the performance of the bridge. The drawn conclusions show that the maximum deflection and acceleration of the bridge are within the design limits that are specified by the Korean and European codes. The parameters evaluation of the model identification depicts the quasistatic and dynamic deformations of PSC and steel girders to be different and less correlated when higher speeds of the passing trains are considered. Finally, the variation of the frequency content of the dynamic deformations of the girders is negligible when high speeds are considered.

Osmangazi Bridge Structural Health Monitoring System

2019 ◽  
Author(s):  
Fatih Zeybek
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Structural Parameters ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System ◽  
The World ◽  
Maintenance Activities ◽  
Hybrid Devices

<p>Osmangazi Bridge is the fourth longest span bridge in the world with it’s 1550 meter main span. The bridge and the first phase of the motorway were opened to traffic on 30th June, 2016.</p><p>Osmangazi Bridge is heavily instrumented with various hybrid devices for monitoring the real-time structural behavior (such as vehicular weight, wind speed, seismic, other environmental and structural parameters) and effects on the bridge (such as strains, acceleration, displacement, temperature).</p><p>This paper summarizes the basics of the Osmangazi Bridge’s SHMS (Structural Health Monitoring System) that’s used together with visual inspections, tests and subcontrol systems to make assessment and optimize the required maintenance activities of bridge</p>

scholarly journals Parameter identification for structural health monitoring based on Monte Carlo method and likelihood estimate

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771878688 ◽  
Author(s):  
Songtao Xue ◽  
Bo Wen ◽  
Rui Huang ◽  
Liyuan Huang ◽  
Tadanobu Sato ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Structural Health Monitoring ◽  
Parameter Identification ◽  
Health Monitoring ◽  
Gaussian Noise ◽  
Damage Identification ◽  
Structural Parameters ◽  
Likelihood Estimate ◽  
Structural Health

Structural parameters are the most important factors reflecting structural performance and conditions. As a result, their identification becomes the most essential aspect of the structural assessment and damage identification for the structural health monitoring. In this article, a structural parameter identification method based on Monte Carlo method and likelihood estimate is proposed. With which, parameters such as stiffness and damping are identified and studied. Identification effects subjected to three different conditions with no noise, with Gaussian noise, and with non-Gaussian noise are studied and compared. Considering the existence of damage, damage identification is also realized by the identification of the structural parameters. Both simulations and experiments are conducted to verify the proposed method. Results show that structural parameters, as well as the damages, can be well identified. Moreover, the proposed method is much robust to the noises. The proposed method may be prospective for the application of real structural health monitoring.

scholarly journals Output-only Structural Health Monitoring of a Riveted Steel Railway Bridge utilizing Proper Orthogonal Decomposition, Artificial Neural Network, and Strain Measurements

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 30
Author(s):  
Ahmed Rageh ◽  
Saeed Eftekhar Azam ◽  
Daniel Linzell
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Operational Conditions ◽  
Damage Scenarios ◽  
Structural Health ◽  
Damage Indices ◽  
Wide Range ◽  
Artificial Neural ◽  
Output Only ◽  
Proper Orthogonal

This study presents a new scheme for autonomous health monitoring of railroad infrastructure using a continuous stream of structural health monitoring data. The study utilized measured strains from an optimized sensor set deployed on a double track, steel, railway, truss bridge located in central Nebraska. The most common failure mode for the superstructure of this structural system is the stringer-to-floor beam connection failure, which was the focus of this study. However, the proposed methodology could be used to assess the condition of a wide range of structural elements and details. The damage feature adopted in this framework was the variations of Proper Orthogonal Modes (POMs) of the measured structural response. To automatically detect the occurrence, location, and intensity of deficiencies from the POMs, Artificial Neural Networks (ANN) were adopted. POM variations, which are traditionally input (load) dependent, were ultimately utilized as damage indicators. To alleviate the variability of POMs due to non-stationarity of the train loads, a preset windowing of measured output was completed in conjunction with automated peak-picking. Furthermore, input variability necessitated implementing ANNs to help decouple POM changes due to load variations from those caused by deficiencies, changes that would render the proposed framework input independent; a significant advancement. Damage “scenarios” were artificially introduced into select output (strain) datasets recorded while monitoring train passes across the selected bridge. This information, in turn, was used to train ANNs using MATLAB’s Neural Net Toolbox. Trained ANNs were tested against monitored loading events and artificial damage scenarios. Applicability of the proposed, output-only framework was investigated via studies of the bridge under operational conditions. To account for the effects of potential deficiencies at the stringer-to-floor beam connections, measured signal amplitudes were artificially decreased at select locations. Finally, to validate the applicability of the proposed method using low-cost measurement devices, the measured signals were corrupted by high levels of white, Gaussian noises featuring spatial correlations. It was concluded that the proposed framework could successfully identify 20 damage indices, which were artificially imposed on measured signals under operational conditions.

Probability-Based Structural Health Monitoring Through Markov Chain Monte Carlo Sampling

2016 ◽  
Vol 16 (07) ◽  
pp. 1550039 ◽  
Author(s):  
P. J. Li ◽  
D. W. Xu ◽  
J. Zhang
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Parameters ◽  
Fe Model ◽  
Property A ◽  
Stochastic Sampling ◽  
Structural Health

The classical nonuniqueness problem exists due to uncertainty in the finite element (FE) calibration field. Namely, multiple models with different intrinsic parameters may all fit the observed data well, thus the selected single “best” model probably is not the truly best model to reflect the structural intrinsic property. A probability-based method using a population of FE models, not the single “best” method, is proposed to deal with the nonuniqueness problem. In this method, the Markov Chain Monte Carlo (MCMC) technique is first performed to sample the key structural parameters representing the main sources of uncertainty. Then a FE model population is generated using the samples, and the posterior probability of each model is evaluated by calculating the correlation between the simulation results and measurements through the Bayesian theorem. Finally, all the FE models from the stochastic sampling with their posterior probabilities are used for structural identification (St-Id) and performance evaluation. The advantage of the proposed method is that it not only identifies the magnitudes of structural parameters, but also generates their probability distributions for subsequent probability-based reliability analysis and risk evaluation. The feature provided by the stochastic sampling and statistical techniques makes the proposed method suitable for dealing with uncertainty. The example of the Phase I IASC-ASCE benchmark structure investigated demonstrates the effectiveness of the proposed method for probability-based structural health monitoring.

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