| [1] |   | Probabilistic modeling of chloride diffusion in repaired reinforced concrete structures, Quynh Chau Truong, Charbel Pierre El Soueidy, Emilio Bastidas-Arteaga, ISBN:[9780443134715, 9780443134708], Elsevier (2024), doi:10.1016/B978-0-443-13470-8.00008-3
| The durability of reinforced concrete (RC) structures is a crucial aspect of infrastructure maintenance, as it directly affects the safety and serviceability of these structures. Therefore, it is of utmost importance to understand the degradation processes and develop appropriate repair strategies. However, probabilistic modeling of degradation processes in repaired concrete structures is still at...
|
|
| [2] |   | Introduction to Reinforcement Learning with Applications in Geomechanics, Alexandros Stathas, Diego Gutierrez-Oribio, Ioannis Stefanou, ISBN:[9781789451931, 9781394325665], Wiley (2024), doi:10.1002/9781394325665.ch4
| Reinforcement learning (RL) is a subfield of machine learning (ML) that focuses on the development of software agents that are capable of making optimal decisions in dynamic and uncertain environments. This chapter introduces the fundamental components of RL and their relations to the Bellman equations and presents an example on policy iteration: a miner trapped inside a trembling mine finding his...
|
|
| [3] |  | Preface, Ioannis Stefanou, Felix Darve, ISBN:[9781789451931, 9781394325665], [9781789451924, 9781394325634] (2024)
| ...
|
|
| [4] | | Fundamentals of fluid flow in fibrous preforms, Christophe Binetruy, Sebastien Comas-Cardona, ISBN:[9780443215797, 9780443215780], Elsevier (2024), doi:10.1016/B978-0-443-21578-0.00021-4
| This chapter presents the main flow phenomena involved in the processes of structural fiber reinforced polymer matrix composites and proposes a mathematical description derived directly from the main physical principles adapted to heterogeneous and anisotropic fibrous media. We are interested in fiber preforms with long or continuous fibers, which may or may not be oriented, and low viscosity resi...
|
|
| [5] | | Machine Learning in Geomechanics 1: Overview of Machine Learning, Unsupervised Learning, Regression, Classification and Artificial Neural Networks, Ioannis Stefanou, Felix Darve, ISBN:[9781789451924, 9781394325634], Wiley (2024), doi:10.1002/9781394325634
| Machine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in m...
|
|
| [6] | | Overview of Machine Learning in Geomechanics, Ioannis Stefanou, ISBN:[9781789451924, 9781394325634], Wiley (2024), doi:10.1002/9781394325634.ch1
| Machine learning (ML) is an evolving field of knowledge and involves a plethora of methods and combinations of those. ML methods are classified into different categories: supervised learning versus unsupervised learning, batch learning versus online learning, and instance based learning versus model based learning. This chapter discusses the applications of ML in geomechanics, including constituti...
|
|
| [7] | | Physics-Informed and Thermodynamics-Based Neural Networks, Filippo Masi, Ioannis Stefanou, ISBN:[9781789451931, 9781394325665], Wiley (2024), doi:10.1002/9781394325665.ch3
| This chapter offers a comprehensive introduction to the integration of prior knowledge arising from physics and thermodynamics into deep learning algorithms. By means of motivating examples, it explore the capabilities and strengths of (i) physics informed neural networks (PINNs) for the discovery, driven by data, of partial differential equations and (ii) thermodynamics based artificial neural ne...
|
|
| [8] | | Machine Learning in Geomechanics 2: Data-Driven Modeling, Bayesian Inference, Physics- and Thermodynamics-based Artificial Neural Networks and Reinforcement Learning, Ioannis Stefanou, Felix Darve, ISBN:[9781789451931, 9781394325665], Wiley (2024), doi:10.1002/9781394325665
| Machine learning has led to incredible achievements in many different fields of science and technology. These varied methods of machine learning all offer powerful new tools to scientists and engineers and open new paths in geomechanics. The two volumes of Machine Learning in Geomechanics aim to demystify machine learning. They present the main methods and provide examples of its applications in m...
|
|
| [9] | | Extended finite element (XFEM) and thick level set (TLS) methods, Nicolas Moes, ISBN:[9781789450811, 9781394340507], Wiley (2024), doi:10.1002/9781394340507.ch6
| The extended finite element method (XFEM) is an extension to the finite element method, which can be used for monitoring crack propagation through a mesh without having to modify it. The XFEM method also allows us to take into account a cohesive zone at the tip of the crack. The thick level sets (TLS) method places around the crack a band in which the material softens and deformation are localized...
|
|
| [10] |   | Advanced Chemical and Creep Modeling for Alkali-Aggregate Reaction in Concrete, Fernando A. N. Silva, Rodrigo F. Roma, Khaled Bourbatache, Mahfoud Tahlaiti, Joao M. P. Q. Delgado, Antonio C. Azevedo, ISBN:978-3-031-53979-4, Springer Nature Switzerland (2024), doi:10.1007/978-3-031-53980-0
| This book presents the numerical results of the use of the chemical model to analyse the advancement of the reaction and the mechanical model to simulate creep and shrinkage phenomena in COMSOL Multiphysics®, as a way to reassess concrete structures suffering from those mechanisms. Both models were implemented separately to evaluate their responses and compare them with the theoretical results and...
|
|