Identification of structural parameters and boundary conditions using a minimum number of measurement points

Karimpour, Ali; Rahmatalla, Salam

doi:10.1007/s11709-020-0686-4

Cited by 5 publications

(5 citation statements)

References 45 publications

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“…In the context of little difference in the calculation results of combination modes 1, 4, 5, to simplify the calculation process, the first combination with equal weigh factors (each takes 0.1) was adopted in this article. The further discussion of weight factor combination given by Karimpour and Rahmatalla 61 is of great reference value. The optimal convergence curve is depicted in Figure 9(b), indicating that the computation has gradually converged, and that the global optimal search capability can be further improved.…”

Section: Resultsmentioning

confidence: 94%

Optimal placement method of multi-objective and multi-type sensors for courtyard-style timber historical buildings based on Meta-genetic algorithm

Zhang

Chun

Leng

et al. 2023

Structural Health Monitoring

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Optimal sensor placement for timber architecture heritage poses a significant challenge due to the unique structural types and complex monitoring purposes. In this study, a three-stage method is proposed, taking a courtyard-style heritage, built 133 years ago, as an example. First, a finite element model that accounted for the parameter randomness and initial damage was constructed using a genetic algorithm (GA) and experimental results. Second, a new weighted fitness function of logarithmic type was developed for multi-type sensors and multi-objective monitoring. Third, a novel genetic algorithm, Meta-GA, was proposed, introducing competition group mechanisms and gene libraries to improve optimal capability while maintaining computational efficiency. The Meta-GA is then compared to the other two optimization modes using seven indexes. Finally, damage detection capability was tested for the proposed three schemes at noise levels of 0%, 5%, and 10%. The results reveal that the proposed three-stage method with Meta-GA can provide the best solution.

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Section: Resultsmentioning

confidence: 94%

Optimal placement method of multi-objective and multi-type sensors for courtyard-style timber historical buildings based on Meta-genetic algorithm

Zhang

Chun

Leng

et al. 2023

Structural Health Monitoring

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“…After simulating the initial bridge FEM based on the asbuilt blueprint, it is required to perform structural model updating to validate it; model updating techniques are used to make a fair representative of the real bridge from its in situ information [29][30][31][32]. For bridge structures, the most important parameters that must be updated are boundary conditions and material properties using many available advanced techniques [33][34][35][36].…”

Section: Stage (1): Construct Calibrated Femmentioning

confidence: 99%

“…To successfully implement the WOP for real-world bridge LR, the first and essential practical step is to perform the structural model updating process to construct a fair representation of the bridge by its FEM as explained in the WOP first stage. Although, in general, any available robust model updating technique can be exploited to perform WOP's first stage, detailed descriptions of the model updating techniques used in this article are available in the references [29,30]. After simulating the initial FEM based on the as-built blueprints of a bridge, the analyst must calibrate its FEM either with in situ NDT data or visual inspections based on AASHTO guidelines for field inspection.…”

Section: Recommendation For Practical Implementationmentioning

confidence: 99%

Finite Element Model‐Based Weight‐Over Process Philosophy for Bridge Loading Capacity Evaluation and Rating Factor Estimation

Karimpour

Rahmatalla

Ghadikolaee

2021

Advances in Civil Engineering

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Existing rating methods estimate bridge loading capacity and demand from secondary actions due to live loads in the primary structural components. In these methods, uniaxial yielding stress is traditionally used to detect component capacity using either stress quantities or shear-moment actions to compute the capacity demand of the bridge. These approximations can lead to uncertainties in load capacity estimation. This article presents the weight-over process (WOP), a novel computer-aided approach to bridge loading capacity evaluation based on tonnage and rating factor estimation. WOP is expected to capture different forms of failure in a more general manner than existing methods. In WOP, a bridge finite element model (FEM) is discretized into many sections and element sets, each containing a single material type, and each assigned a suitable 3D failure criterion. Then, factored gross vehicle weights (GVWs) are incrementally imposed on the bridge FEM with those predefined ultimate unfavored loading scenarios in a manner similar to proof load testing. WOP code runs nonlinear analysis at each increment until a stopping criterion is met. Two representative bridges were selected to confirm WOP’s feasibility and efficacy. The results showed that WOP-predicted values at the interior girders were between those of the conventional AASHTO and the nondestructive testing (NDT) strain measurement methods. That may put WOP in a favorable zone as a new method that is less conservative than AASHTO but more conservative than real NDT testing.

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“…However, a primary challenge in the solution procedure is the requirement for repetitive evaluations of the simulation model (e.g. finite element model), which can incur substantial computational costs, particularly when the model is intricate and time-consuming (Biegler et al, 2011;Yan and et al, 2014;Karimpour and Rahmatalla, 2020;Vu-Bac et al, 2016). For instance, in dam structure applications, Ji et al (2020) used the deep neural network (DNN) as the surrogate model to accelerate the optimization process of the deep Q-network (DQN) algorithm for mechanical parameter identification of hydraulic engineering.…”

Section: Introductionmentioning

confidence: 99%

“…To alleviate this issue, surrogate models are widely applied in this field (Ghasemi et al. , 2014; Karimpour and Rahmatalla, 2020; Vu-Bac et al. , 2016).…”

Section: Introductionmentioning

confidence: 99%

An adaptive multi-output Gaussian process surrogate model for large-scale parameter estimation problems

Lyu,

Huang,

et al. 2024

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PurposeParameter estimation in complex engineering structures typically necessitates repeated calculations using simulation models, leading to significant computational costs. This paper aims to introduce an adaptive multi-output Gaussian process (MOGP) surrogate model for parameter estimation in time-consuming models.Design/methodology/approachThe MOGP surrogate model is established to replace the computationally expensive finite element method (FEM) analysis during the estimation process. We propose a novel adaptive sampling method for MOGP inspired by the traditional expected improvement (EI) method, aiming to reduce the number of required sample points for building the surrogate model. Two mathematical examples and an application in the back analysis of a concrete arch dam are tested to demonstrate the effectiveness of the proposed method.FindingsThe numerical results show that the proposed method requires a relatively small number of sample points to achieve accurate estimates. The proposed adaptive sampling method combined with the MOGP surrogate model shows an obvious advantage in parameter estimation problems involving expensive-to-evaluate models, particularly those with high-dimensional output.Originality/valueA novel adaptive sampling method for establishing the MOGP surrogate model is proposed to accelerate the procedure of solving large-scale parameter estimation problems. This modified adaptive sampling method, based on the traditional EI method, is better suited for multi-output problems, making it highly valuable for numerous practical engineering applications.

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Identification of structural parameters and boundary conditions using a minimum number of measurement points

Cited by 5 publications

References 45 publications

Optimal placement method of multi-objective and multi-type sensors for courtyard-style timber historical buildings based on Meta-genetic algorithm

Optimal placement method of multi-objective and multi-type sensors for courtyard-style timber historical buildings based on Meta-genetic algorithm

Finite Element Model‐Based Weight‐Over Process Philosophy for Bridge Loading Capacity Evaluation and Rating Factor Estimation

An adaptive multi-output Gaussian process surrogate model for large-scale parameter estimation problems

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