Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks

In recent years, a lot of progress has been made in the field of material modeling with artificial neural networks (ANNs). However, the following drawbacks persist to this day: ANNs need a large amount of data for the training process. This is not realistic, if real world experiments are intended to be used as data basis. Additionally, the application of ANN material models in finite element (FE) calculations is challenging because local material instabilities can lead to divergence within the solution algorithm. In this paper, we extend the approach of constrained neural network training from [28] to elasto-plastic material behavior, modeled by an incrementally defined feedforward neural network. Purely stress and strain dependent equality and inequality constraints are introduced, including material stability, stationarity, normalization, symmetry and the prevention of energy production. In the Appendices, we provide a comprehensive framework on how to implement these constraints in a gradient based optimization algorithm. We show, that ANN material models with training enhanced by physical constraints leads to a broader capture of the material behavior that underlies the given training data. This is especially the case, if a limited amount of data is available, which is important for a practical application. Furthermore, we show that these ANN models are superior to classically trained ANNs in FE computations when it comes to convergence behavior, stability, and physical interpretation of the results

Artificial neural network surrogate modeling for uncertainty quantification and structural optimization of reinforced concrete structures

Optimization approaches are important to design sustainable structures. In structural mechanics, different design objectives can be defined, for example, to minimize the required construction material or to maximize the structural durability. In this paper, the durability of a reinforced concrete (RC) structure is assessed by advanced finite element (FE) models to simulate the cracking behavior and the chloride transport process. The corrosion initiation time is used as durability measure to be maximized within an optimization approach, where the concrete cover is defined as design variable. The variability of structural loads and material parameters and unavoidable construction imprecision leads to a probabilistic reliability and durability assessment, where aleatory as well as epistemic uncertainties are quantified by random variables, intervals and probability-boxes. The FE simulation models cannot directly be applied to structural analyses and optimizations with polymorphic uncertain parameters and design variables because of the high computational demand of the multi-loop algorithm (Monte Carlo simulation, interval analysis, global optimization). In this paper, a new surrogate modeling strategy is presented, where artificial neural networks are trained sequentially to speed-up the coupled mechanical and transport simulation FE models. The new approach is applied to the uncertainty quantification and the structural durability optimization of a RC structure

The pandemic and the question of national belonging: Exposure to covid-19 threat and conceptions of nationhood

Drawing on the behavioural immune system hypothesis, we argue that the prevalence of the Covid-19 pandemic threat in an individual's respective environment relates to exclusive, ethnic conceptions of nationhood. Referring to the affective intelligence theory, we maintain that specific negative emotions are prompted by the perception of being exposed to a pandemic threat, and these emotional states in turn structure political preferences regarding national belonging. Using an original survey in six European countries during the first peak of the pandemic in late April and early May 2020, we analyze both the impact of individual Covid-19 experiences and the contextual exposure to a pandemic threat through hierarchical analyses of 105 European regions. Our empirical analysis shows that exposure to the pandemic is linked to stronger ethnic national identities for both levels of analysis. We also find that anger substantially mediates this relationship and has primacy over feelings of fear. Taken together, our results indicate that the behavioural immune system appears as a pervasive obstacle to inclusive orientations

A simulation-based software to support the real-time operational parameters selection of tunnel boring machines

With the fact that the main operational parameters of the construction process in mechanized tunneling are currently selected based on monitoring data and engineering experience without exploiting the advantages of computer methods, the focus of this work is to develop a simulation-based real-time assistant system to support the selection of operational parameters. The choice of an appropriate set of these parameters (i.e., the face support pressure, the grouting pressure, and the advance speed) during the operation of tunnel boring machines (TBM) is determined by evaluating different tunneling-induced soil-structure interactions such as the surface settlement, the associated risks on existing structures and the tunnel lining behavior. To evaluate soil-structure behavior, an advanced process-oriented numerical simulation model based on the finite cell method is utilized. To enable the real-time prediction capability of the simulation model for a practical application during the advancement of TBMs, surrogate models based on the Proper Orthogonal Decomposition and Radial Basis Functions (POD-RBF) are adopted. The proposed approach is demonstrated through several synthetic numerical examples inspired by the data of real tunnel projects. The developed methods are integrated into a user-friendly application called SMART to serve as a support platform for tunnel engineers at construction sites. Corresponding to each user adjustment of the input parameters, i.e., each TBM driving scenario, approximately two million outputs of soil-structure interactions are quickly predicted and visualized in seconds, which can provide the site engineers with a rough estimation of the impacts of the chosen scenario on structural responses of the tunnel and above ground structures

Real-Time Reliability Analysis in Mechanized Tunneling

Real-time reliability analyses of mechanized tunneling processes can help to reduce the risk of tunneling induced damages and failures. In order to support the machine driver to steer the tunnel boring machine, fast simulation models are required. In this work, polymorphic uncertainty modeling approaches are combined with numerical surrogate models to provide reliability measures in real-time during tunnel construction. Based on a finite element simulation model of the mechanized tunneling process, deterministic and fuzzy surrogate models are created step by step to approximate the tunneling induced time variant settlement field and finally to compute fuzzy probability boxes of the settlements in a few minutes.Financial support was provided by the German Research Foundation (DFG) in the framework of project C1 of the Collaborative Research Center SFB 837 "Interaction Modeling in Mechanized Tunneling" and by the Mercator Research Center Ruhr (MERCUR) within the project Fusion of Machine Learning and Numerical Simulation for Real- Time Steering in Mechanized Tunneling . This support is gratefully acknowledged