Gaussian Process Regression-Based Material Model for Stochastic Structural Analysis

Chen, Baixi; Shen, Luming; Zhang, Hao

doi:10.1061/ajrua6.0001138

Cited by 14 publications

(5 citation statements)

References 38 publications

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“…GPR has several advantages, including the fact that its kernel functions make it very effective at modeling nonlinear data. Additionally, the primary benefit of GPR is that it offers an accurate answer to the supplied data 34,35 . The most significant of them is that it can solve supervised learning problems more precisely and performs well on tiny datasets.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the primary benefit of GPR is that it offers an accurate answer to the supplied data. 34,35 The most significant of them is that it can solve supervised learning problems more precisely and performs well on tiny datasets. The GPR model can offer uncertainty metrics for forecasts and create predictions that use previous information (kernels).…”

Section: Algorithmsmentioning

confidence: 99%

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Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Roshanfar,

Ghiami Azad,

Forouzanfar

2023

Fatigue Fract Eng Mat Struct

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Fatigue limit states often govern the design of shear connectors in steel‐concrete composite bridges. AASHTO LRFD bridge design specifications provides a linear equation in a semi‐logarithmic S‐N curve for predicting the fatigue life of shear connectors. However, this equation can be too conservative in some cases, as supported by the available experimental data. In this paper, artificial intelligence (AI) was incorporated into the prediction of the fatigue life of shear connectors. Six different machine learning (ML) algorithms were considered for this purpose. The predictions of ML algorithms were compared both with the available experimental data and the equation provided by AASHTO. The results showed that the fatigue life predicted by ML methods is more accurate than that predicted by the current equation of AASHTO. The results of this study showed that AI can be a proper alternative to the existing methods for predicting the fatigue life of shear connectors.

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Section: Methodsmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Roshanfar,

Ghiami Azad,

Forouzanfar

2023

Fatigue Fract Eng Mat Struct

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“…Graf et al (2012) used the fuzzy neural network to describe the uncertain stress-strain trends successfully based on the material data. Chen et al (2022aChen et al ( , 2021Chen et al ( , 2023) introduced a Bayesian-based ML algorithm, that is, the Gaussian process regression (GPR) model, for quantifying the material uncertainty directly from the experimental data. They achieved good application in both the metal and the rock.…”

Section: Data Availabilitymentioning

confidence: 99%

“…Chen et al. (2022a, 2021, 2023) introduced a Bayesian-based ML algorithm, that is, the Gaussian process regression (GPR) model, for quantifying the material uncertainty directly from the experimental data. They achieved good application in both the metal and the rock.…”

Section: Introductionmentioning

confidence: 99%

Manufacturing-induced stochastic constitutive behaviors of additive manufactured specimens: testing, data-driven modeling, and optimization

Chen,

Mao,

Lin

et al. 2024

RPJ

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Purpose Fused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing process. Design/methodology/approach By conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing parameters. Findings As indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R2 over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 °C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the experiments. Practical implications The developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing technologies. Originality/value Stochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

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“…By keeping the advantage of machine learning and taking the material uncertainty into account, a Bayesian based machine learning algorithm, Gaussian process regression (GPR), has been adopted to model material behavior 17 . Different from other machine learning methods only providing the deterministic estimation, the GPR model can capture both the underlying relation and corresponding uncertainty of the data simultaneously via the Bayesian approach 18 .…”

Section: Introductionmentioning

confidence: 99%

Heteroscedastic sparse Gaussian process regression-based stochastic material model for plastic structural analysis

Chen

Shen

Zhang

2022

Sci Rep

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Describing the material flow stress and the associated uncertainty is essential for the plastic stochastic structural analysis. In this context, a data-driven approach-heteroscedastic sparse Gaussian process regression (HSGPR) with enhanced efficiency is introduced to model the material flow stress. Different from other machine learning approaches, e.g. artificial neural network (ANN), which only estimate the deterministic flow stress, the HSGPR model can capture the flow stress and its uncertainty simultaneously from the dataset. For validating the proposed model, the experimental data of the Al 6061 alloy is used here. Without setting a priori assumption on the mathematical expression, the proposed HSGPR-based flow stress model can produce a better prediction of the experimental stress data than the ANN model, the conventional GPR model, and Johnson Cook model at elevated temperatures. After the HSGPR-based flow stress model is implemented into finite element analysis, two numerical examples with synthetic material properties are performed to demonstrate the model’s capability in stochastic plastic structural analysis. The results have shown that with sufficient data, the distribution of the structural load carrying capacity at elevated temperatures and the variation of load–displacement curves during the loading and unloading processes can be accurately predicted by the HSGPR-based flow stress model.

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Gaussian Process Regression-Based Material Model for Stochastic Structural Analysis

Cited by 14 publications

References 38 publications

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Predicting fatigue life of shear connectors in steel‐concrete composite bridges using artificial intelligence techniques

Manufacturing-induced stochastic constitutive behaviors of additive manufactured specimens: testing, data-driven modeling, and optimization

Heteroscedastic sparse Gaussian process regression-based stochastic material model for plastic structural analysis

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