mparison of model-based identification methods for reserve-capacity assessment of existing bridges

2018 ◽  
Author(s):  
Marco Proverbio ◽  
François-Xavier Favre ◽  
Ian F. C. Smith
Keyword(s):  
Parameter Identification ◽  
Structural Identification ◽  
Domain Model ◽  
Reserve Capacity ◽  
Steel Bridge ◽  
Model Based ◽  
Wrong Identification ◽  
Alternative Methodology ◽  
Existing Bridges ◽  
Residual Minimization

The goal of model-based structural identification is to find suitable values of parameters that affect structure behaviour. To this end, measurements are often compared with predictions of finiteelement models. Although residual minimization (RM) is a prominent methodology for structural identification, it provides wrong parameter identification when flawed model classes are adopted. Error-domain model falsification (EDMF) is an alternative methodology that helps identify candidate models – models that are compatible with behaviour measurements – among an initial model population. This study focuses on the comparison between RM and EDMF for the structural identification of a steel bridge in Exeter (UK). Advantages and limitations of both methodologies are discussed with reference to parameter identification and prognosis tasks such as quantification of reserve capacity. Results show that the employment of RM may lead to wrong identification and unsafe estimations of reserve capacity.

Methodology for a Partly Automated Parameter Identification for the Validation of Multi-Domain Models

2014 ◽  
Author(s):  
D. Kruse ◽  
C. Schweers ◽  
A. Trächtler
Keyword(s):  
Parameter Identification ◽  
Domain Model ◽  
Domain Expert ◽  
Model Based ◽  
New Approaches ◽  
Domain Models ◽  
Identification Tool ◽  
The Way

The paper presents a methodology for a partly automated parameter identification that is to validate multi-domain models. To this end an identification tool under MATLAB has been developed. It enables a partly automated procedure that uses established methods to identify parameters from complex, nonlinear multi-domain models. In order to integrate such multi-domain models into the tool, an interface based on the Functional Mock-up Interface (FMI) standard can be used. The interface makes the required identification parameters from the multi-domain model automatically available to the identification tool. Additionally a guideline is developed which describes the way in which the respective domain expert has to mark the required identification parameters during modeling. The needs for this methodology as well as its application are shown by a practical example from the industry, using Dymola, the FMI-standard, and MATLAB. The practical example deals with the model-based development of a new washing procedure. The paper presents a partly automated parameter identification for the validation of the absorption part of the multi-domain model. Besides, new approaches to the modelling of this kind of absorption effects will be detailed.

scholarly journals Existing Steel Railway Bridges Evaluation

2016 ◽  
Vol 12 (2) ◽  
pp. 103-110 ◽  
Author(s):  
Josef Vičan ◽  
Jozef Gocál ◽  
Jaroslav Odrobiňák ◽  
Peter Koteš
Keyword(s):  
Carrying Capacity ◽  
Global Analysis ◽  
Evaluation Process ◽  
Steel Bridge ◽  
Load Carrying Capacity ◽  
Railway Bridges ◽  
Load Carrying ◽  
Bridge Superstructure ◽  
Existing Bridges ◽  
Capacity Determination

Abstract The article describes general principles and basis of evaluation of existing railway bridges based on the concept of load-carrying capacity determination. Compared to the design of a new bridge, the modified reliability level for existing bridges evaluation should be considered due to implementation of the additional data related to bridge condition and behaviour obtained from regular inspections. Based on those data respecting the bridge remaining lifetime, a modification of partial safety factors for actions and materials could be respected in the bridge evaluation process. A great attention is also paid to the specific problems of determination of load-caring capacity of steel railway bridges in service. Recommendation for global analysis and methodology for existing steel bridge superstructure load-carrying capacity determination are described too.

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

Model-Based Testing of Embedded Systems Exemplified for the Automotive Domain

2010 ◽  
pp. 377-413 ◽  
Author(s):  
Justyna Zander ◽  
Ina Schieferdecker
Keyword(s):  
Embedded Systems ◽  
Embedded System ◽  
Reference Signal ◽  
Test Methods ◽  
Domain Model ◽  
Test Method ◽  
Model Based ◽  
Test Models ◽  
Black Box Testing ◽  
Weak Points

The purpose of this chapter is to introduce the test methods applied for embedded systems addressing selected problems in the automotive domain. Model-based test approaches are reviewed and categorized. Weak points are identified and a novel test method is proposed. It is called model-in-the-loop for embedded system test (MiLEST) and is realized in MATLAB®/Simulink®/Stateflow® environment. Its main contribution refers to functional black-box testing based on the system and test models. It is contrasted with the test methods currently applied in the industry that form dedicated solutions, usually specialized in a concrete testing context. The developed signal-feature-oriented paradigm developed herewith allows the abstract description of signals and their properties. It addresses the problem of missing reference signal flows and allows for a systematic and automatic test data selection. Processing of both discrete and continuous signals is possible, so that the hybrid behavior of embedded systems can be handled.

scholarly journals Model-Based Condition Monitoring of a Vanadium Redox Flow Battery

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 3005 ◽  
Author(s):  
Meng ◽  
Xiong ◽  
Lim
Keyword(s):  
Parameter Identification ◽  
Real Time ◽  
Vanadium Redox Flow Battery ◽  
Redox Flow Battery ◽  
Flow Battery ◽  
Model Parameter ◽  
Model Based ◽  
Model Parameter Identification ◽  
Vanadium Redox ◽  
Capacity Decay

The safe, efficient and durable utilization of a vanadium redox flow battery (VRB) requires accurate monitoring of its state of charge (SOC) and capacity decay. This paper focuses on the unbiased model parameter identification and model-based monitoring of both the SOC and capacity decay of a VRB. Specifically, a first-order resistor-capacitance (RC) model was used to simulate the dynamics of the VRB. A recursive total least squares (RTLS) method was exploited to attenuate the impact of external disturbances and accurately track the change of model parameters in realtime. The RTLS-based identification method was further integrated with an H-infinity filter (HIF)-based state estimator to monitor the SOC and capacity decay of the VRB in real-time. Experiments were carried out to validate the proposed method. The results suggested that the proposed method can achieve unbiased model parameter identification when unexpected noises corrupt the current and voltage measurements. SOC and capacity decay can also be estimated accurately in real-time without requiring additional open-circuit cells.

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