Inverse modeling of in-situ experiments is already a standardized approach for identifying various types of material parameters. In this contribution we are focused on the single ring (hereafter SR) infiltration experiment, which is a standard and robust dynamic field experiment. The steady state part of this experiment is traditionally used for the identification of saturated hydraulic conductivity. We explore here the possibility of extending the applicability of this experiment for evaluating the hydraulic parameters for unsaturated conditions from an unsteady part of this experiment for the top soil layer using inverse analyses of the governing flow motion equation. The problem of SR infiltration is governed by the quasilinear Richards equation. We present a new scanning methodology to avoid convergence issues with the nonlinear operator, originating from difficult combinations of input parameters, which can be hard to avoid when automatically analyzing a broad parameter space. We validated our methodology with virtual infiltration problems for clay and sand, and applied it on real-world SR infiltration data. To evaluate non-uniqueness, local optima were identified and mapped using a modified genetic algorithm with niching. Our results show the existence of multimodality in, both, the benchmark problems and the real-world problem. This is an important finding as local optima can be identified, which are not necessarily physical and also for systems that do not exhibit multimodal grain size distributions. The identified local optima were distinct and showed different retention and hydraulic conductivity curves. The most physical set of SHP could be identified with the knowledge of the saturated water content.

The concept of multi-spiral reinforcement for concrete columns offers almost arbitrary shape of the cross- section but more importantly superior structural performance compared to conventional reinforcement. The enhanced strength and ductility stems from the passive confinement produced by the transverse reinforcement. Increase in lateral confinement allows to utilize the potential of concrete more efficiently but on the other hand demands more steel. The objective of this paper is to identify, by means of the nonlinear finite element simulations, the relationship between the carrying capacity in uniaxial compression and the amount of transverse reinforcement. This will help to find an optimum balance between the cost and structural performance.

Modularity is appealing for solving many problems in optimization. It brings the benefits of manufacturability and reconfigurability to structural optimization, and enables a trade-off between the computational performance of a periodic unit cell (PUC) and the efficacy of non-uniform designs in multi-scale material optimization. Here, we introduce a novel strategy for concurrent minimum-compliance design of truss modules topologies and their macroscopic assembly encoded using Wang tiling, a formalism providing independent control over the number of modules and their interfaces. We tackle the emerging bilevel optimization problem with a combination of meta-heuristics and mathematical programming. At the upper level, we employ a genetic algorithm to optimize module assemblies. For each assembly, we obtain optimal module topologies as a solution to a convex second-order conic program that exploits the underlying modularity, incorporating stress constraints, multiple load cases, and reuse of module(s) for various structures. Merits of the proposed strategy are illustrated with three representative examples, clearly demonstrating that the best designs obtained by our method exhibited decreased compliance: by 56 up to 69 % compared with the PUC designs.

This paper aims at a reduction of periodicity artefacts during a generation of random heterogeneous material models. The traditional concept of the Periodic Unit Cell is compared with a novel approach of the stochastic Wang tiling. Since modelled structures consist of hard circular/spherical particles in a matrix, the algorithm for placement of inclusions is based on the modified molecular dynamics. We introduce two types of Wang tile boundary conditions to decrease periodicity artefacts. Tested samples for 2D applications form sets of both monodisperse and polydisperse microstructures. The overall volume fractions of these samples are approximately 0.2, 0.4, and 0.6, respectively. The generated sets are analysed both visually and statistically via a two-point probability function. An extension of the stochastic Wang tiling enables to create 3D structures, as well. Therefore, artificial periodicity is also investigated on a 3D sample consisting of spherical particles of identical radii distributed in a continuous phase.

Fueled by their excellent stiffness-to-weight ratio and the availability of mature manufacturing technologies, filament wound carbon fiber reinforced polymers represent ideal materials for thin-walled laminate structures. However, their strong anisotropy reduces structural resistance to wall instabilities under shear and buckling. Increasing laminate thickness degrades weight and structural efficiencies and the application of a dense internal core is often uneconomical and labor-intensive. In this contribution, we introduce a convex linear semidefinite programming formulation for truss topology optimization to design an efficient non-uniform lattice-like internal structure. The internal structure not only reduces the effect of wall instabilities, mirrored in the increase of the fundamental free-vibration eigenfrequency, but also keeps weight low, secures manufacturability using conventional three-dimensional printers, and withstands the loads induced during the production process. We showcase a fully-automatic procedure in detail for the design, prototype manufacturing, and verification of a simply-supported composite machine tool component, including validation with roving hammer tests. The results confirm that the 3D-printed optimized internal structure almost doubles the fundamental free-vibration eigenfrequency, allowing to increase working frequency of the machine tool, even though the ratio between elastic properties of the carbon composite and the ABS polymer used for 3D printing exceeds two orders of magnitude.

In this contribution, we design a minimum-weight truss reinforcement of a thin-walled composite beam, such that the fundamental free-vibrations eigenfrequency of the beam is increased to a specific value. The reinforcement structure is designed using techniques of topology optimization and produced using additive manufacturing, in order to achieve economical design and minimize manual interventions in the fabrication process. Finally, an experimental validation of theoretical outcomes is performed on an additively manufactured prototype.

This contribution deals with a laser dieless drawing method in which tubes (AZ31 magnesium alloy) with small dimensions are produced from previously extruded tubes with a larger diameter. The process involves local laser heating of a tube with concurrent drawing and rotation of a sample. The control parameters need to be optimized in order to get a good quality tube with low damage and regular diameter. Here, a previously developed finite element (FE) model is used for the numerical optimization. The optimization itself is multi-objective, maximizing the uniformity of the resulting tube but concurrently, minimizing its critical deformation. Since one run of the FE code lasts for several days, an approximation by a Radial Basis Function meta-model (surrogate) is used. Results reveal that there is only a very limited number of combinations of parameters leading to a uniformly shaped tubes. Finally, tubes produced with optimized parameters are analyzed from the microstructural point of view.

A semidefinite programming formulation of truss topology optimization has been employed to design a minimum-weight internal structure of a wound composite beam, with the objective to increase its fundamental free-vibration eigenfrequency and guarantee small deflections in the compression molding load case concurrently. The optimized design was successfully manufactured by winding the additively-manufactured internal structure by a carbon/epoxy composite. Preliminary numerical results confirm the potential of the method.

Sept. 17 (Mon) 9:00-10:00, plenary 10:0010:20 10:20-12:20 5 rooms 12:2013:40 13:40-15:40 5 rooms 15:4016:00 16:00-18:00 5 rooms Welcome by TUT president, Keynote 3 coffee break K34, K1-5 C1-6 E1-6 D8, F1-5 A1-6 lunch K6-11 C7-12 E7-12 F6-11 A7-12 coffee break K12-17 C13-18 E13-18 F12-17 A13-19(18:20) Sept. 18 (Tue) 9:00-10:00, plenary 10:0010:20 10:20-12:20 5 rooms 12:2013:40 13:40-15:40 5 rooms 15:4016:00 16:00-18:00 3 rooms Keynote 1, 5 coffee break K18-23 C19-24 E19-24 F18-23 D1-6 lunch K24-29 C25-29, B1 E25,26, J1-4 H1-6 D7, D9-13 coffee break K30-33 J5-9

Small probability of failure characterizes a good structural design. Prediction of such a structural safety is time consuming considering that sampling methods such as Monte Carlo method or Latin Hypercube sampling are used. Therefore, more specialized methods are developed. A Subset simulation is one of the new techniques based on modifying the failure event as an intersection of nested intermediate events that are easier to solve. This paper deals with a parameter study of the Subset simulation with modified Metropolis algorithm for Markov chain Monte Carlo using distinct proposal distributions. Different setting is then compared on reliability assessment benchmarks, namely on two mathematical functions with different failure probabilities and on a 23-bar planar truss bridge.

Meta-modeling also known as Surrogate Modeling is one of the commonly used tools foranalysis of complex systems' behavior. The meta-model is constructed based on training data whichconsist of the training points generated via Design of Experiments (DoE) and responses of the originalmodel evaluated in these training points. The positioning of the points is crucial for the approximationquality of the meta-model. Therefore it is appropriate to assess the DoE's quality not only usingthe common geometrical or statistical criteria but also from the point of view of its actual particularpurpose. Such testing is able to recognize the appropriate set of training points and also the possibleability of the individual meta-models for actual problem.

The steel-concrete composite road bridges are one of the often designed bridge solutions for mid-span road bridges not only in the Czech Republic, but also around the world. However, due to the relatively high purchase price of the superstructure it does rarely win the public tenders, where the legislation still chooses the best solution only according to construction cost and ignores all other sustainability markers. Therefore, the need for the optimization of this type of structures arises significantly on the market. For that, the newly developed optimization tool in the MATLAB programming environment can help the designers and project owners to optimize their structures, or verify the existing design. The tool is described in the paper and the verification shown on the planned bridge structure.

Surrogate modeling (Meta-modeling) is a commonly used approach for analysis of complex systems' behavior. Time and computing demands of analytical models describing such systems are usually very high and in cases of need of multiple evaluations (for example in Monte Carlo based reliability analysis) they cannot be used. Instead, a model of the original model called surrogate model can be used. The purpose of the surrogate model is to approximate an original model's response in an arbitrary point of the design domain while constructed on a very limited and thus computationally cheap training data. The training data consist of the Design of Experiments (DoE) and corresponding responses of the original model. The choice of the DoE is crucial for the quality of the surrogate's approximation and therefore the LHS design is often used for its convenient properties. The contribution proposes a procedure of shifting of a part of the design of experiments in cases where the area of interest is located after some original model's evaluations were performed. The goal is clear: to use the already computed training data while not deteriorate the quality of the DoE.

This paper deals with double-looped reliability-based design optimization (RBDO), in which the system reliability is assessed within the inner loop and a designing process is performed in the outer loop. A common approach expressed as single-objective optimization is transformed into a multi-objective case providing results as an approximation of the Pareto front composed of the compromising solutions between cost and reliability. The double-loop formulation of RBDO provides the most accurate approximation of the Pareto front but is computationally demanding even if advanced simulation techniques are used for rare failure events. Nowadays, a Subset simulation is a popular method to obtain an estimate of small failure probabilities. Despite the reduction in evaluation time using a Subset simulation when compared to a crude Monte Carlo method, the computational effort is still high with a complex model as a performance function, e.g. a finite element model. The computational model can be replaced by its surrogate in order to reduce the computational costs. This meta-model fits the responses evaluated by the original model for the predetermined data, so called a Design of Experiment (DoE). Since the design variables change with every iteration and a meta-model is utilized for a reliability assessment, the meta-model is trained only in the vicinity of the relevant design variable which makes the meta-model computationally faster and more precise. The DoE is updated by selected points from subset simulation samples with respect to two criteria: first, beneficial samples are located in the vicinity of the limit state, which divides the space into a safe region and a failure domain, and second, these samples should also be placed in the sparsest position of the DoE. The described method is illustrated on a classical RBDO benchmark with two objective functions; the first objective is a cost function to be minimized, the second objective is a structural reliability expressed by a reliability index to be maximized. The quality is assessed by comparison to an asymptotic sampling and a Monte Carlo simulation all with responses obtained by an original model and local meta-models.

Surrogate modeling (Meta-modeling) is an often used tool for analysis of behavior of complex systems which are usually described by computationally demanding models. Surrogate models provide an approximation of the original model's response in a fraction of time and therefore are suitable when multiple evaluations are needed Many types of meta-models exist and each suits another type of problem. On the other hand it is not always possible to select the right meta-model in advance. Therefore parallel construction of several meta-models and their subsequent comparison and combining can be utilized with advantage. A typical method called PRESS weighted average surrogate which uses the prediction sum of squares obtained by cross-validation for computation of the weights for linear combination of individual surrogates is discussed in this contribution and illustrated on several 2-dimensional benchmark examples using a group of different meta-models.

This contribution focuses on a double-looped reliability-based design optimization, in which the reliability of the system is evaluated in the inner loop and the designing process is performed in the outer loop. The double-looped formulation provides the most accurate results but it is computationally demanding especially if advanced simulation techniques are used for rare failure events. The selection of the method for the reliability assessment is therefore crucial to obtain the best results with the lowest possible computational efforts. A quasi-Monte Carlo simulation, an Asymptotic sampling and a Subset simulation are therefore utilized in the inner loop and the results are compared for two reliability-based design optimization benchmarks.

Meta-modeling is a frequently used tool for analysis of systems' behavior. An original model of the system is often complex and its evaluation is expensive and time-consuming. Therefore it is desirable to execute the original model as few times as possible. A special case is when many evaluation of the model with different input parameters are necessary. Proposed solution is a use of the meta-model, in our case Radial Basis Function Network (RBFN) tool is presented. Here, the output of the meta-model is constructed as a linear combination of the radial basis functions. For good approximation a shape parameter of the radial basis functions has to be set properly. This paper describes a tuning of the shape parameters for several benchmark examples.

This paper deals with a reconstruction of random media via multi-objective optimization. Two statistical descriptors, namely a two-point probability function and a two-point lineal path function, are repetitively evaluated for the original medium and the reconstructed image to appreciate the improvement in the optimization progress. Because of doubts of the weights setting in the weighted-sum method, purely multi-objective optimization routine Non-dominated Sorting Genetic Algorithm~II is utilized. Three operators are compared for creating new offspring populations that satisfy a prescribed volume fraction constraint. The main contribution is in the testing of the proposed methodology on several benchmark images.

Truss topology weight optimization problem with discrete cross-sections can be formulated as a mixed-integer linear program (MILP), which is solvable to global optimality. It is however very difficult to obtain proven globally optimal solution, as there usually exist a very large number of possible combinations. This contribution implements several types of additional cuts and solves the problem using a commercial branch-and-bound software Gurobi, hence making it possible to obtain a guaranteed globally optimal solution. Such solution can then be used as a lower bound for sizing optimization.