arXiv subjects

Filippo Masi

Publications and source records attributed to Filippo Masi.

9 recordsLinked to original sources

arXiv2026

Learning inelastic constitutive models from stress-strain data under hard thermodynamic constraints

Machine learning approaches informed by physics have offered new insights into the discovery of constitutive models from data, helping overcome some limitations of traditional constitutive modelling while reducing the cost of otherwise computationally intensive simulations. Yet, many existing methods either enforce only part of the relevant physical and thermodynamic structure, or achieve thermodynamic consistency within specific constitutive classes, leaving open questions about their applicability across a broad range of material behaviours and their ability to generalise to unseen loading paths when limited data are available. This work establishes a thermodynamics-constrained learning framework for inelastic constitutive models from macroscopic stress-strain data. The framework is based on non-equilibrium thermodynamics and parameterises both the free energy and the generalised transport operator governing the evolution of the material state, while enforcing material objectivity, energy balance, and non-negative dissipation as hard, scalable constraints. Analytical benchmarks involving simple stress-strain loading paths demonstrate that the method learns thermodynamically consistent and robust constitutive models for a range of inelastic materials of increasing complexity. At inference, the resulting models generalise to more demanding, unobserved paths and identify interpretable internal variables that capture path-dependent behaviours. The framework is then applied to granular media, prototypical heterogeneous and history-dependent materials. Trained on numerically simulated experiments based on the discrete element method, the method identifies admissible constitutive equations and predicts the response under cyclic loading, including the emergence of hysteresis absent from the training data, relying solely on macroscopic stress-strain histories.

cond-mat.mtrl-sci↗

arXiv2024

Hydrodynamics of fault gouges from constitutive modelling to the physics of friction

The development of rate- and state-dependent friction laws offered important insights into the key physical mechanisms of the frictional behaviour of fault gouges and their seismic cycle. However, past approaches were specifically tailored to address the problem of fault shearing, leaving questions about their ability to comprehensively represent the gouge material under general loading conditions. This work establishes an alternative approach for developing a physical friction law for fault gouges that is grounded on the rigour of the hydrodynamic procedure with two-scale temperatures through Terracotta, a thoroughly robust constitutive model for clay in triaxial loading conditions. By specifying the model for direct shearing, the approach yields an alternative friction law that readily captures the frictional dynamics of fault gouges, including explicit dependencies on gouge layer thickness, normal stress, and solid fraction. Validated against available laboratory experiments, the friction law retains the original predictive capabilities of Terracotta in triaxial conditions and explains the rate-and-state, dilatational behaviour of fault gouges in direct shear conditions. Finally, when the Terracotta friction law is connected to a spring-dashpot representation of the host rock, the combined model predicts an elastic buildup precursor to the onset of and subsequent seismicity, with results closely reflecting experimental evidence and field observations. While this study focuses on clay-rich gouges, the approach and findings are expected to offer much wider implications to a variety of materials.

physics.geo-ph↗

arXiv2024

Measurement, self-similarity, and TNT equivalence of blasts from exploding wires

Reduced-scale experiments offer a controlled and safe environment for studying the effects of blasts on structures. Traditionally, these experiments rely on the detonation of solid or gaseous explosive mixtures, with only limited understanding of alternative explosive sources. This paper presents a detailed investigation of the blasts produced by exploding aluminum wires for generating shock waves of controlled energy levels. We meticulously design the experiments to ensure a precise quantification of the underlying uncertainties and conduct comprehensive parametric studies. We draw practical relationships of the blast intensity with respect to the stand-off distance and the stored energy levels. The analysis demonstrates self-similarity of blasts with respect to the conventional concept of the scaled distance, a desirable degree of sphericity of the generated shock waves, and high repeatability. Finally, we quantify the equivalence of the reduced-scale blasts from exploding wires with high explosives, including TNT. The present experimental setup and study demonstrate the high degree of robustness and effectiveness of exploding aluminum wires as a tool for controlled blast generation and reduced-scale structural testing.

physics.class-ph↗

arXiv2024

Scaling laws for the rigid-body response of masonry structures under blast loads

The response of masonry structures to explosions can be hardly investigated relying only on numerical and analytical tools. Experimental tests are of paramount importance for improving the current comprehension and validate existing models. However, experiments involving blast scenarios are, at present, partial and limited in number, compared to tests under different dynamic conditions, such as earthquakes. The reason lies on the fact that full-scale blast experiments present many difficulties, mainly due to the nature of the loading action. Experiments in reduced-scale offer instead greater flexibility. Nevertheless, appropriate scaling laws for the response of masonry structures under blast excitations are needed before performing such tests. We propose here new scaling laws for the dynamic, rigid-body response and failure modes of masonry structures under blast loads. This work takes its roots from previous studies, where closed-form solutions for the rocking response of slender blocks due to explosions have been derived and validated against numerical and experimental tests. The proposed scaling laws are here validated with detailed numerical simulations accounting for combined rocking, up-lifting, and sliding mechanisms of monolithic structures. Then, the application to multi-drum stone columns is considered. In particular, we show that, whilst the presence of complex behaviors, such as wobbling and impacts, similarity is assured. The developments demonstrate their applicability in the design of reduced-scale experiments of masonry structures.

math.NA↗

arXiv2024

miniBLAST: a novel experimental setup for laboratory testing of structures under blast loads

We present a novel experimental setup called miniBLAST, which enables systematic and repeatable laboratory tests of structures subjected to blast loads. The explosive source is based on the discharge of high electrical loads on a thin conductor, producing repeatable blast-type shock waves of controlled intensity. Conducting blast experiments under safe laboratory conditions offers significant advantages over large-scale experiments, which are expensive, require specialized personnel, are limited in number, and face repeatability issues. In this work, we provide a comprehensive description of the setup's design rationale and technical characteristics. Moreover, we place particular emphasis on the installation phases, safety, and metrology. The explosive source is analyzed and the signature of blast shock waves is retrieved. Finally, we present an example of a masonry wall subjected to blast loads of varying intensity. We then report its dynamic response in three dimensions and at different time instances. This new experimental setup offers a cost-effective, safe, and repeatable method to study structural dynamics under blast loads, with results that can be upscaled to real structures. It also aids in evaluating numerical models and lays the groundwork for further investigations into blast effects and mitigation.

physics.class-ph↗

arXiv2023

Neural integration for constitutive equations using small data

Data-driven models based on deep learning algorithms intend to overcome the limitations of traditional constitutive modelling by directly learning from data. However, the need for extensive data that collate the full state of the material is hindered by traditional experimental observations, which typically provide only small data - sparse and partial material state observations. To address this issue, we develop a novel deep learning algorithm referred to as Neural Integration for Constitutive Equations to discover constitutive models at the material point level from scarce and incomplete observations. It builds upon the solution of the initial value problem describing the time evolution of the material state, unlike the majority of data-driven approaches for constitutive modelling that require large data of increments of state variables. Numerical benchmarks demonstrate that the method can learn accurate, consistent, and robust constitutive models from incomplete, sparse, and noisy data collecting simple conventional experimental protocols.

cond-mat.mtrl-sci↗

arXiv2023

Evolution TANN and the identification of internal variables and evolution equations in solid mechanics

Data-driven and deep learning approaches have demonstrated to have the potential of replacing classical constitutive models for complex materials. Yet, the necessity of structuring constitutive models with an incremental formulation has given rise to data-driven approaches where physical quantities, e.g. deformation, blend with artificial, non-physical ones, such as the increments in deformation and time. Neural networks and the consequent constitutive models depend, thus, on the particular incremental formulation, fail in identifying material representations locally in time, and suffer from poor generalization. Herein, we propose a new approach which allows, for the first time, to decouple the material representation from the incremental formulation. Inspired by the Thermodynamics-based Artificial Neural Networks (TANN) and the theory of the internal variables, the evolution TANN (eTANN) are continuous-time and, therefore, independent of the aforementioned artificial quantities. Key feature of the proposed approach is the identification of the evolution equations of the internal variables in the form of ordinary differential equations, rather than in an incremental discrete-time form. In this work, we focus attention to juxtapose and show how the various general notions of solid mechanics are implemented in eTANN. The capabilities as well as the scalability of the proposed approach are demonstrated through several applications involving a broad spectrum of complex material behaviors, from plasticity to damage and viscosity (and combination of them). Finally, we show that the proposed approach can be used to speed-up multiscale analyses, by virtue of asymptotic homogenization. eTANN provide excellent results compared to detailed fine-scale simulations and offer the possibility not only to describe the average macroscopic material behavior, but also micromechanical, complex mechanisms.

cs.CE↗

arXiv2022

Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)

The mechanical behavior of inelastic materials with microstructure is very complex and hard to grasp with heuristic, empirical constitutive models. For this purpose, multiscale, homogenization approaches are often used for performing reliable, accurate predictions of the macroscopic mechanical behavior of solids and structures. Nevertheless, the calculation cost of such approaches is extremely high and prohibitive for real-scale applications involving inelastic materials. Here, we propose the so-called Thermodynamics-based Artificial Neural Networks (TANN) for the constitutive modeling of materials with inelastic and complex microstructure. Our approach integrates thermodynamics-aware dimensionality reduction techniques and thermodynamics-based deep neural networks to identify, in an autonomous way, the constitutive laws and discover the internal state variables of complex inelastic materials. The efficiency and accuracy of TANN in predicting the average and local stress-strain response, the free-energy and the dissipation rate is demonstrated for both regular and perturbed two- and three-dimensional lattice microstructures in inelasticity. TANN manage to identify the internal state variables that characterize the inelastic deformation of the complex microstructural fields. These internal state variables are then used to reconstruct the microdeformation fields of the microstructure at a given state. Finally, a double-scale homogenization scheme (FEMxTANN) is used to solve a large scale boundary value problem. The high performance of the homogenized model using TANN is illustrated through detailed comparisons with microstructural calculations at large scale. An excellent agreement is shown for a variety of monotonous and cyclic stress-strain paths.

cond-mat.mtrl-sci↗

arXiv2020

Thermodynamics-based Artificial Neural Networks for constitutive modeling

Machine Learning methods and, in particular, Artificial Neural Networks (ANNs) have demonstrated promising capabilities in material constitutive modeling. One of the main drawbacks of such approaches is the lack of a rigorous frame based on the laws of physics. This may render physically inconsistent the predictions of a trained network, which can be even dangerous for real applications. Here we propose a new class of data-driven, physics-based, neural networks for constitutive modeling of strain rate independent processes at the material point level, which we define as Thermodynamics-based Artificial Neural Networks (TANNs). The two basic principles of thermodynamics are encoded in the network's architecture by taking advantage of automatic differentiation to compute the numerical derivatives of a network with respect to its inputs. In this way, derivatives of the free-energy, the dissipation rate and their relation with the stress and internal state variables are hardwired in the network. Consequently, our network does not have to identify the underlying pattern of thermodynamic laws during training, reducing the need of large data-sets. Moreover the training is more efficient and robust, and the predictions more accurate. Finally and more important, the predictions remain thermodynamically consistent, even for unseen data. Based on these features, TANNs are a starting point for data-driven, physics-based constitutive modeling with neural networks. We demonstrate the wide applicability of TANNs for modeling elasto-plastic materials, with strain hardening and strain softening. Detailed comparisons show that the predictions of TANNs outperform those of standard ANNs. TANNs ' architecture is general, enabling applications to materials with different or more complex behavior, without any modification.

cs.LG↗