Structural optimisation problem in support to building retrofitting decision

Various analysis methods, either linear elastic or non-linear, static or dynamic, are available for the performance analysis of existing buildings. Despite its advantages, it must be admitted that non-linear time history analysis can frequently become overly complex and impractical for general use as a first assessment. Simplified models, as the Capacity Spectrum Method, are frequently not able to accurately assess irregular structures. Considering these limitations, it is proposed and evaluated a simplified MDOF non-linear dynamic model, accounting for non-linear storey behaviour and storey damping. Based on the MDOF non-linear dynamic model, were developed optimization algorithms for the redesign of existing non-seismically designed structures. The optimization procedure searches for the optimum storey strengthening distribution (strength, stiffness or damping) in order to meet specific performance requirements, in terms of maximum inter-storey drift for a given seismic demand level. Numerical examples are presented in order to illustrate the capability of methodology

Simplified numerical approach for incremental sheet metal forming process

The current work presents a finite element approach for numerical simulation of the incremental sheet metal forming (ISF) process, called here ‘‘ISF-SAM’’ (for ISF-Simplified Analysis Modelling). The main goal of the study is to develop a simplified FE model sufficiently accurate to simulate the ISF process and quite efficient in terms of CPU time. Some assumptions have been adopted regarding the constitutive strains/stresses equations and the tool/sheet contact conditions. A simplified contact procedure was proposed to predict nodes in contact with the tool and to estimate their imposed displacements. A Discrete Kirchhoff Triangle shell element called DKT12, taking into account membrane and bending effects, has been used to mesh the sheet. An elasto-plastic constitutive model with isotropic hardening behaviour and a static scheme have been adopted to solve the nonlinear equilibrium equations. Satisfactory results have been obtained on two applications and a good correlation has been shown compared to experimental and numerical results, and at the same time a reduction of CPU time more than 60% has been observed. The bending phenomenon studied through the second application and the obtained results show the reliability of the DKT12 element

Network Flow Algorithms for Structured Sparsity

We consider a class of learning problems that involve a structured sparsity-inducing norm defined as the sum of $\ell_\infty$-norms over groups of variables. Whereas a lot of effort has been put in developing fast optimization methods when the groups are disjoint or embedded in a specific hierarchical structure, we address here the case of general overlapping groups. To this end, we show that the corresponding optimization problem is related to network flow optimization. More precisely, the proximal problem associated with the norm we consider is dual to a quadratic min-cost flow problem. We propose an efficient procedure which computes its solution exactly in polynomial time. Our algorithm scales up to millions of variables, and opens up a whole new range of applications for structured sparse models. We present several experiments on image and video data, demonstrating the applicability and scalability of our approach for various problems.Comment: accepted for publication in Adv. Neural Information Processing Systems, 201

Half a billion simulations: evolutionary algorithms and distributed computing for calibrating the SimpopLocal geographical model

Multi-agent geographical models integrate very large numbers of spatial interactions. In order to validate those models large amount of computing is necessary for their simulation and calibration. Here a new data processing chain including an automated calibration procedure is experimented on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimise three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. The model is thus a useful tool for further multiple applications on empirical historical situations