Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks

A weakened form of Ilyushin's postulate and the structure of self-consistent Eulerian finite elastoplasticity

This paper introduces novel approaches for the estimation of the traffic stream density. First, an artificial neural network (ANN) data-driven approach is developed to estimate the level of market penetration (LMP) of connected vehicles at two fixed locations. Then, the estimated values are used as inputs to a Kalman filter (KF) approach to estimate the vehicle count between these two locations. Second, three data-driven approaches are developed to directly estimate the vehicle count using only connected vehicle data, an ANN, a k-nearest neighbor (k-NN), and a random forest (RF). A congested signalized roadway in downtown Blacksburg, Virginia, is used to test and compare the performance of the estimation approaches. Results demonstrate that the ANN approach produces reasonable errors in estimating the LMPs; however, integrating the ANN with the KF results in larger errors than the errors produced from using the KF with a predefined fixed average value obtained from historical data. The results also demonstrate that the data-driven approaches provide accurate vehicle count estimates, with the ANN being the most accurate of the three approaches. Lastly, the paper compares the three developed data-driven approaches with model-driven approaches (i.e., KF), showing that the ANN outperforms all other approaches. However, taking into consideration that the difference is not large, the computational time needed to train the ANN, the large amount of data needed, and the uncertainty in the performance when new traffic behaviors are observed (e.g., incidents), the use of the KF approach is recommended in the estimation of traffic stream density due to its simplicity and applicability in the field.

The description of monotonic and cyclic behavior of material is possible by generalizing the internal stress concept by means of a set of internal variables. In this paper the classical isotropic and kinematic hardening rules are briefly discussed, using present plastic strain tensor and cumulated plastic strain as hardening variables. Some additional internal variables are then proposed, giving rise to many possibilities. What is called the “nonlinear kinematic hardening” leads to a natural description of the nonlinear plastic behavior under cyclic loading, but is connected to other concepts such as the Mroz’s model, limited to only two surfaces, and similarities with other approaches are pointed out in the context of a generalization of this rule to viscoplasticity.

Critical state model for structured soil

The evolutive masing model and its application to cyclic plasticity and ageing

Cyclic loading response and associated yield criteria for soft railway subgrade – Theoretical and experimental perspectives

Elastoplastic modeling of cyclic behavior of natural structured clay with large number of cycles

Experiments conducted in the context of the strain-space formulation of plasticity

Elevated temperature fatigue crack growth in alloy 718. I. Effects of mechanical variables: Ghonem, H., Nicholas, T. and Pineau, A. Fatigue Fract Eng mater Struct (May 1993) 16 (5), 565–576

Artificial Neural Networks (ANNs) models play an increasingly significant role in accurate time series prediction tools. However, an accurate time series forecasting using ANN requires an optimal model. Hence, great forecasting methods have been developed from optimized ANN models. Most of them focus more on input variables selection and preprocessing, topologies selection, optimum configuration and its associated parameters regardless of their input variables disposition. This paper provides an investigation of the effects of input variables disposition on ANNs models on training and forecasting performances. After investigation, a new ANNs optimization approach is proposed, consisting of finding optimal input variables disposition from the possible combinations. Therefore, a modified Back-Propagation neural networks training algorithm is presented in this paper. This proposed approach is applied to optimize the feed-forward and recurrent neural networks architectures; both built using traditional techniques, and pursuing to forecast the wind speed. Furthermore, the proposed approach is tested in a collaborative optimization method with single-objective optimization technique. Thus, Genetic Algorithm Back-Propagation neural networks aim to improve the forecasting accuracy relative to traditional methods was proposed. The experiment results demonstrate the requirement to take into consideration the input variables disposition to build a more optimal ANN model. They reveal that each proposed model is superior to its old considered model in terms of forecasting accuracy and thus show that the proposed optimization approach can be useful for time series forecasting accuracy improvement.

Cyclic behaviour and plastic strain memory effect of 55NiCrMoV7 steel under low cycle fatigue

Granular materials subjected to compressive cyclic loading of constant amplitude when modeled using an isotropic hardening, elastoplastic Drucker-Prager (DP) model do not accumulate significant plastic strain beyond the first loading cycle. This can lead to predictions of permanent deformations in airfield asphalt pavements that do not compare well with experimental observations. Accordingly, this paper proposes a modification to the existing DP model as a means of accounting for this shortcoming. A new parameter, γ, is introduced, which causes the yield surface to progressively evolve and shrink in size during successive load cycles to account for microphysical restructuring of the granular material during constant amplitude cyclic loading. The constitutive model is implemented within the finite element code ABAQUS through the user material subroutine UMAT. The model is subsequently tested via 2D finite element analysis on pavement sections by applying it to the granular base and modeling all other layers as linear elastic. Validation is undertaken by comparing rutting with field experimental values from an accelerated pavement testing program. Increased equivalent plastic strain and vertical plastic strains are observed with this modification, thereby resulting in rutting values that are significantly closer to the experimental data than those obtained with the classical DP model.

An a priori evaluation of a principal component and artificial neural network based combustion model in diesel engine conditions

The pharmaceutical industry is striving to develop innovative and promising tools, increasingly embracing new data-driven approaches, to understand, improve and accelerate the drug product development process. While extended release (ER) oral formulations offer a number of advantages, including maintenance of therapeutic drug levels, a reduction in dosing frequency, and minimization of side effects, achieving consistent drug release profiles remains a significant challenge. As a critical attribute for drug absorption into systemic circulation, in vitro dissolution testing represents a time-consuming and complex method for the evaluation of such formulations. The main objective of this study was to develop a model for predicting drug dissolution in the quality by design (QbD)-based development of ER oral hydrophilic matrix tablets comprising polyethylene oxide (PEO). Two main modeling approaches are conducted and compared: (i) model screening to fit and compare multiple predictive machine learning (ML) models and then deploy the best model, in this case, artificial neural networks (ANN), and (ii) functional data analysis (FDA) combined with the design of experiments (DoE) that fit a smoothing model to each dissolution curve as a continuous function. A dataset comprising 91 ER matrix tablet formulations was analyzed, with the dissolution data split into training, validation, and test sets (70%, 20%, and 10%, respectively). The results demonstrated that both ANN and functional DoE (FDOE) models achieved high similarity with the experimental dissolution profiles, as indicated by f2 values ranging from 48 to 88 for the FDOE and 52 to 88 for ANN. This work highlights the potential of integrating advanced data-driven modeling techniques into ER drug development to enhance dissolution prediction accuracy and streamline the formulation process, thus reducing time and costs.

Plastic deformation behavior of Cu thin films during fatigue testing