Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite

Le, Thinh-Tien; Guilleminot, Johann; Soize, Christian

doi:10.1016/j.cma.2015.10.006

Cited by 64 publications

(41 citation statements)

References 70 publications

(88 reference statements)

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“…The main idea of the Monte Carlo method is to repeat realizations randomly in the input space and then calculate the corresponding output through the simulation model [33,34]. Therefore, this numerical technique exhibits a high ability in parallel computing [35][36][37][38]. A concept of using the Monte Carlo method is presented in Figure 4, involving a two-dimensional input space with a typical probability distribution.…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Pham

Nguyen-Thoi

et al. 2020

Sustainability

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Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model. Sustainability 2020, 12, 2339 2 of 29 the laboratory, including direct shear test, triaxial compression tests, or unconfined compression tests which might increase the cost and prolong the time of completing the projects. Moreover, the test accuracy depends significantly on the instruments, the meticulous procedures, and the expertise of the experimenters [1]. Therefore, the development of new advanced techniques for quick and accurate prediction of shear strength of soil is essential and practical.Traditionally, the shear strength of soil is often predicted by using traditional formula-based methods. Garven and Vanapalli [2] summarized and evaluated nineteen empirical techniques that are available for the prediction of the shear strength of unsaturated soils. Out of these, six techniques used tool of the soil-water retention curve (SWRC) and the remainder thirteen procedures are based on mathematical formulations. In these empirical techniques, various parameters of soil were used to correlate with the shear strength in unsaturated soils such as the texture of soil surface, pore size distribution, residual suction. In another study, Sheng et al. [3] proposed different empirical equations for the prediction of shear strength of unsaturated soils using different approaches, which are based on the independent stress, Bishop's stress, and constitutive models. Vanapalli and Fredlund [4] compared different empirical approaches for the prediction of shear strength of unsaturated soils. Various parameters used for forming the correlation equations such as particle gain distribution, liquid limit, plasticity indices, water con...

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Section: Monte Carlo Simulationsmentioning

confidence: 99%

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Pham

Nguyen-Thoi

et al. 2020

Sustainability

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“…On the other hand, lower RMSE and MAE indicate a better model [85,86]. The formulas are defined as follow [87][88][89][90]:…”

Section: Performance Indicatorsmentioning

confidence: 99%

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

et al. 2019

Applied Sciences

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The principal purpose of this work is to develop three hybrid machine learning (ML) algorithms, namely ANFIS-RCSA, ANFIS-CA, and ANFIS-SFLA which are a combination of adaptive neuro-fuzzy inference system (ANFIS) with metaheuristic optimization techniques such as real-coded simulated annealing (RCSA), cultural algorithm (CA) and shuffled frog leaping algorithm (SFLA), respectively, to predict the critical buckling load of I-shaped cellular steel beams with circular openings. For this purpose, the existing database of buckling tests on I-shaped steel beams were extracted from the available literature and used to generate the datasets for modeling. Eight inputs, considered as independent variables, including the beam length, beam end-opening distance, opening diameter, inter-opening distance, section height, web thickness, flange width, and flange thickness, as well as one output of the critical buckling load of cellular steel beams considered as a dependent variable, were used in the datasets. Three quality assessment criteria, namely correlation coefficient (R), root mean squared error (RMSE) and mean absolute error (MAE) were employed for assessment of three developed hybrid ML models. The obtained results indicate that all three hybrid ML models have a strong ability to predict the buckling load of steel beams with circular openings, but ANFIS-SFLA (R = 0.960, RMSE = 0.040 and MAE = 0.017) exhibits the best effectiveness as compared with other hybrid models. In addition, sensitivity analysis was investigated and compared with linear statistical correlation between inputs and output to validate the importance of input variables in the models. The sensitivity results show that the most influenced variable affecting beam buckling capacity is the beam length, following by the flange width, the flange thickness, and the web thickness, respectively. This study shows that the hybrid ML techniques could help in establishing a robust numerical tool for beam buckling analysis. The proposed methodology is also promising to predict other types of failure, as well as other types of perforated beams.

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“…This section is devoted to the stochastic modeling and to the inverse identification presented in [119,120] related to the random field associated with the elastic properties in the interphase region between the polymer and a silicon nano-inclusion inserted in the polymer, for a polymer system reinforced by a Silica nanoscopic inclusion. This application gives an interesting illustration of the use of − the advanced prior stochastic model of the apparent elasticity random field with a given symmetry class, presented in Section 10.7.3.…”

Section: Stochastic Continuum Modeling Of Random Interphases From Atomentioning

confidence: 99%

Uncertainty Quantification

Soize

2017

Interdisciplinary Applied Mathematics

106

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Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite

Cited by 64 publications

References 70 publications

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination

Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams

Uncertainty Quantification

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