Artificial Neural Networks In Continuum Micromechanics

Artificial Neural Networks in Continuum Micromechanics

Bridge Maintenance, Safety, Management, Digitalization and Sustainability

Bridge Maintenance, Safety, Management, Digitalization and Sustainability collects the lectures and technical papers presented at the 12th International Conference on Bridge Maintenance, Safety and Management (IABMAS 2024, Copenhagen, Denmark, 24-28 June 2024). This Open Access book contains 480 contributions, including the T.Y. Lin Lecture, 9 Keynote Lectures, and 470 technical papers from 44 countries. The contributions are presented bring together academic and technological developments in Bridge Maintenance, Safety, Management, Digitalization and Sustainability, to solve new and old problems with innovative solutions. Major topics include: advanced bridge design, construction and maintenance approaches, safety, reliability and risk evaluation, life-cycle management, life-cycle resilience, sustainability, standardization, analytical models, bridge management systems, service life prediction, structural health monitoring, non-destructive testing and field testing, robustness and redundancy, durability enhancement, repair and rehabilitation, fatigue and corrosion, extreme loads, needs of bridge owners, whole life costing and investment for the future, financial planning and application of information and computer technology, extensive data analysis and artificial intelligence for bridges, among others. Bridge Maintenance, Safety, Management, Digitalization and Sustainability provides an up-to-date overview of the field of bridge engineering and significant contributions to making more rational decisions on bridge safety, maintenance, management, life-cycle, resilience, sustainability, and bridge innovations to enhance society’s welfare. The Editors hope that this book will serve as a valuable reference to all concerned with bridge structure and infrastructure systems, including engineers, researchers, academics, and students from all areas of bridge engineering.

Deep material networks for efficient scale-bridging in thermomechanical simulations of solids

We investigate deep material networks (DMN). We lay the mathematical foundation of DMNs and present a novel DMN formulation, which is characterized by a reduced number of degrees of freedom. We present a efficient solution technique for nonlinear DMNs to accelerate complex two-scale simulations with minimal computational effort. A new interpolation technique is presented enabling the consideration of fluctuating microstructure characteristics in macroscopic simulations.