ANN-aided incremental multiscale-remodelling-based finite strain poroelasticity

Mechanical modelling of poroelastic media under finite strain is usually carried out via phenomenological models neglecting complex micro-macro scales interdependency. One reason is that the mathematical two-scale analysis is only straightforward assuming infinitesimal strain theory. Exploiting the potential of ANNs for fast and reliable upscaling and localisation procedures, we propose an incremental numerical approach that considers rearrangement of the cell properties based on its current deformation, which leads to the remodelling of the macroscopic model after each time increment. This computational framework is valid for finite strain and large deformation problems while it ensures infinitesimal strain increments within time steps. The full effects of the interdependency between the properties and response of macro and micro scales are considered for the first time providing more accurate predictive analysis of fluid-saturated porous media which is studied via a numerical consolidation example. Furthermore, the (nonlinear) deviation from Darcy's law is captured in fluid filtration numerical analyses. Finally, the brain tissue mechanical response under uniaxial cyclic test is simulated and studied

Finite strain porohyperelasticity: An asymptotic multiscale ALE-FSI approach supported by ANNs

The governing equations and numerical solution strategy to solve porohyperelstic problems as multiscale multiphysics media are provided in this contribution. The problem starts from formulating and non-dimensionalising a Fluid-Solid Interaction (FSI) problem using Arbitrary Lagrangian-Eulerian (ALE) technique at the pore level. The resultant ALE-FSI coupled systems of PDEs are expanded and analysed using the asymptotic homogenisation technique which yields three partially novel systems of PDEs, one governing the macroscopic/effective problem supplied by two microscale problems (fluid and solid). The latter two provide the microscopic response fields whose average value is required in real-time/online form to determine the macroscale response. This is possible efficiently by training an Artificial Neural Network (ANN) as a surrogate for the Direct Numerical Solution (DNS) of the microscale solid problem. The present methodology allows to solve finite strain (multiscale) porohyperelastic problems accurately using direct derivative of the strain energy, for the first time. Furthermore, a simple real-time output density check is introduced to achieve an optimal and reliable training dataset from DNS. A Representative Volume Element (RVE) is adopted which is followed by performing a microscale (RVE) sensitivity analysis and a multiscale confined consolidation simulation showing the importance of employing the present method when dealing with finite strain poroelastic/porohyperelastic problems

The model of workforce agility dependent on drivers, strategies, practices, and results

For the time being, organizations throughout the world are in an environment that is constantly changing in various aspects including technological developments, customers’ preferences, emerging markets, and globalization. In this environment, the concept of agility, especially workforce agility is a valuable tool for organizations and can assist them considerably to cope with this situation. Given a substantial number of scholars have studied the concept of agility from the technical point of view up until now, and have neglected the human resource aspect, this study aims to, first of all, investigate the concept of workforce agility through a model incorporating drivers, practices, strategies advocating and stimulating the implementation of this concept and examine the relationships between these variables and second of all determine the results of implementing workforce agility system. The sample of the present study was selected from the staff and managers of the Ports and Maritime Organization of Iran, who had a bachelor's degree or higher and were working in the field of human resource management. Besides, we used a questionnaire to evaluate the variable. According to the findings, except for the relationship between drivers and implementation results all other relationships between drivers, practices, strategies, and results have been supported

El modelo de agilidad de la fuerza laboral dependiente de los conductores, estrategias, prácticas y resultados

For the time being, organizations throughout the world are in an environment that is constantly changing in various aspects including technological developments, customers’ preferences, emerging markets, and globalization. In this environment, the concept of agility, especially workforce agility is a valuable tool for organizations and can assist them considerably to cope with this situation. Given a substantial number of scholars have studied the concept of agility from the technical point of view up until now, and have neglected the human resource aspect, this study aims to, first of all, investigate the concept of workforce agility through a model incorporating drivers, practices, strategies advocating and stimulating the implementation of this concept and examine the relationships between these variables and second of all determine the results of implementing workforce agility system. The sample of the present study was selected from the staff and managers of the Ports and Maritime Organization of Iran, who had a bachelor's degree or higher and were working in the field of human resource management. Besides, we used a questionnaire to evaluate the variable. According to the findings, except for the relationship between drivers and implementation results all other relationships between drivers, practices, strategies, and results have been supported.         Por el momento, las organizaciones de todo el mundo se encuentran en un entorno que cambia constantemente en varios aspectos, incluidos los desarrollos tecnológicos, las preferencias de los clientes, los mercados emergentes y la globalización. En este entorno, el concepto de agilidad, especialmente la agilidad de la fuerza laboral, es una herramienta valiosa para las organizaciones y puede ayudarlas considerablemente a hacer frente a esta situación. Dado que un número sustancial de académicos han estudiado el concepto de agilidad desde el punto de vista técnico hasta ahora, y han descuidado el aspecto de recursos humanos, este estudio tiene como objetivo, en primer lugar, investigar el concepto de agilidad de la fuerza laboral a través de un modelo que incorpora impulsores. , prácticas, estrategias que propugnan y estimulan la implementación de este concepto y examinan las relaciones entre estas variables y, en segundo lugar, determinan los resultados de la implementación del sistema de agilidad laboral. La muestra del presente estudio fue seleccionada entre el personal y los gerentes de la Organización de Puertos y Marítima de Irán, que tenían una licenciatura o un título superior y trabajaban en el campo de la gestión de recursos humanos. Además, utilizamos un cuestionario para evaluar la variable. Según los hallazgos, a excepción de la relación entre los impulsores y los resultados de la implementación, se han respaldado todas las demás relaciones entre los impulsores, las prácticas, las estrategias y los resultados

A hybrid MGA-MSGD ANN training approach for approximate solution of linear elliptic PDEs

We introduce a hybrid "Modified Genetic Algorithm-Multilevel Stochastic Gradient Descent" (MGA-MSGD) training algorithm that considerably improves accuracy and efficiency of solving 3D mechanical problems described, in strong-form, by PDEs via ANNs (Artificial Neural Networks). This presented approach allows the selection of a number of locations of interest at which the state variables are expected to fulfil the governing equations associated with a physical problem. Unlike classical PDE approximation methods such as finite differences or the finite element method, there is no need to establish and reconstruct the physical field quantity throughout the computational domain in order to predict the mechanical response at specific locations of interest. The basic idea of MGA-MSGD is the manipulation of the learnable parameters’ components responsible for the error explosion so that we can train the network with relatively larger learning rates which avoids trapping in local minima. The proposed training approach is less sensitive to the learning rate value, training points density and distribution, and the random initial parameters. The distance function to minimise is where we introduce the PDEs including any physical laws and conditions (so-called, Physics Informed ANN). The Genetic algorithm is modified to be suitable for this type of ANN in which a Coarse-level Stochastic Gradient Descent (CSGD) is exploited to make the decision of the offspring qualification. Employing the presented approach, a considerable improvement in both accuracy and efficiency, compared with standard training algorithms such classical SGD and Adam optimiser, is observed. The local displacement accuracy is studied and ensured by introducing the results of Finite Element Method (FEM) at sufficiently fine mesh as the reference displacements. A slightly more complex problem is solved ensuring the feasibility of the methodolog

Finite strain poro-hyperelasticity: an asymptotic multi-scale ALE-FSI approach supported by ANNs

This contribution introduces and discusses a formulation of poro-hyperelasticity at finite strains. The prediction of the time-dependent response of such media requires consideration of their characteristic multi-scale and multi-physics parameters. In the present work this is achieved by formulating a non-dimensionalised fluid–solid interaction problem (FSI) at the pore level using an arbitrary Lagrange–Euler description (ALE). The resulting coupled systems of PDEs on the reference configuration are expanded and analysed using the asymptotic homogenisation technique. This approach yields three partially novel systems of PDEs: the macroscopic/effective problem and two supplementary microscale problems (fluid and solid). The latter two provide the microscopic response fields whose average value is required in real-time/online form to determine the macroscale response (a concurrent multi-scale approach). In order to overcome the computational challenges related to the above multi-scale closure, this work introduces a surrogate approach for replacing the direct numerical simulation with an artificial neural network. This methodology allows for solving finite strain (multi-scale) porohyperelastic problems accurately using direct automated differentiation through the strain energy. Optimal and reliable training data sets are produced from direct numerical simulations of the fully-resolved problem by including a simple real-time output density check for adaptive sampling step refinement. The data-driven approach is complemented by a sensitivity analysis of the RVE response. The significance of the presented approach for finite strain poro-elasticity/poro-hyperelasticity is shown in the numerical benchmark of a multi-scale confined consolidation problem. Finally, to show the robustness of the method, the system response is dimensionalised using characteristic values of soil and brain mechanics scenarios

Operational Strategies for Establishing Disaster-Resilient Schools: A Qualitative Study

Introduction: Resilient schools can warranty students’ health and survival at disasters. It is obligatory that schools be prepared for natural challenges through local programs. Considering the great population of students, disaster-resilient schools can be a safe and suitable environment for students at the time of disaster. Objective: This study aims to identify certain operational strategies for establishing schools resilient to natural disasters. Method: This qualitative study was based on conventional content analysis. Using purposive sampling method, 24 experts in the fields of health in disasters, construction engineering, psychology, teaching, and administrative management participated in the study. Maximum variation sampling continued until data saturation was achieved. The data collected via unstructured interviews were analyzed with Graneheim and Lundmen’s conventional content analysis. Results: Content analysis resulted in four main categories as operational strategies for establishing disaster-resilient schools including: 1) “construction and non-construction optimization”, with four subcategories of construct risk management, optimization of construct architecture and physical structure, correct construct localization, and promotion of non-construct safety, 2) “promotion of organizational coordination and interactions” with two subcategories, namely improvement  in intra-organizational communication and improvement  in extra-organizational communication, 3) “improvement  in education” with three subcategories of holding educational courses for families and students, holding educational courses for managers and personnel, and holding simulated exercises, and 4) “process promotion” with four subcategories of increased preparedness, correct planning, creation of organizational structure, and rehabilitation facilitation. Conclusion: Various factors affecting schools’ response to disasters form operational strategies to establish disaster-resilient schools. These strategies influence pre- and post-disaster preparedness. Awareness of these components followed by preparedness prior to disasters can save students’ lives, improve school performance after disasters, and aid in establishing disaster-resilient schools as safe lodgings