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Matteo Bruggi

Researcher at Polytechnic University of Milan

Publications -  97
Citations -  1820

Matteo Bruggi is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Topology optimization & Finite element method. The author has an hindex of 21, co-authored 88 publications receiving 1441 citations. Previous affiliations of Matteo Bruggi include University of Milan & Instituto Politécnico Nacional.

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On an alternative approach to stress constraints relaxation in topology optimization

TL;DR: The aim of the work is to analyze the performances of an alternative methodology to the ε-relaxation introduced in Cheng and Guo (Struct Optim 13:258–266, 1997), which handles the well-known stress singularity problem.
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Topology optimization for minimum weight with compliance and stress constraints

TL;DR: In this paper, a formulation for the topology optimization of elastic structures that aims at minimizing the structural weight subject to compliance and local stress constraints is presented, where the global constraint provides the expected stiffness to the optimal design while a selected set of local enforcements require feasibility with respect to the assigned strength of material.
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A mixed FEM approach to stress-constrained topology optimization

TL;DR: In this article, the authors present an alternative topology optimization formulation capable of handling the presence of stress constraints in a straightforward fashion, which adopts a mixed finite-element discretization scheme wherein not only displacements but also stresses are the variables entering the formulation.
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Generating strut-and-tie patterns for reinforced concrete structures using topology optimization

TL;DR: In this article, a simple implementation for minimum compliance optimization relying on the finite element library and analysis capabilities of a commercial FEM-code and its Application Programming Interface is proposed for the generation of truss-like designs to derive preliminary strut-and-tie models not only in the established bidimensional context but also in a 3D environment.
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A fully adaptive topology optimization algorithm with goal-oriented error control

TL;DR: Numerical simulations show that ATOPT algorithm achieves layouts that are in full agreement with standard results obtained on uniform fine grids, while remarkably reducing the number of design variables and the computational cost of the optimization procedure.