Application of a Machine Learning Algorithm for the Structural Optimization of Circular Arches with Different Cross-Sections

Arches are employed for bridges. This particular type of structures, characterized by a very old use tradition, is nowadays, widely exploited because of its strength, resilience, cost-effectiveness and charm. In recent years, a more conscious design approach that focuses on a more proper use of the building materials combined with the increasing of the computational capability of the modern computers, has led the research in the civil engineering field to the study of optimization algorithms applications aimed at the definition of the best design parameters. In this paper, a differential formulation and a MATLAB code for the calculation of the internal stresses in the arch structure are proposed. Then, the application of a machine learning algorithm, the genetic algorithm, for the calculation of the geometrical parameters, that allows to minimize the quantity of material that constitute the arch structures, is implemented. In this phase, the method used to calculate the stresses has been considered as a constraint function to reduce the range of the solutions to the only ones able to bear the design loads with the smallest volume. In particular, some case studies with different cross-sections are reported to prove the validity of the method and to compare the obtained results in terms of optimization effectiveness

Design of bending-active tied arches by using a multi-objective optimization method

[EN] The design of bending-active structures is a challenging problem, due to the high non-linearity of the activation process, the coupling between member sizing, structural shape and the deformability and buckling sensitivity inherent in the resulting lightweight configurations. Due to the large number of form-finding variables, the choice of member sizing is one of the main difficulties at the conceptual phase. In this paper, authors propose a design tool to generate efficient structural configurations for braced bending-active tied arches using multi objective optimization strategies. Initially, a non-linear FE analysis is performed for each plausible configuration and at each generation of the optimization algorithm. In a second step, a genetic algorithm classifies the solutions and establishes new structural configurations according to best performance. Solutions are evaluated in terms of stresses in the active member and cables, and maximum deflections, as required by design codes for pedestrian bridges. Results are given in terms of non-dimensional parameters, in order to make them applicable to a wide variety of scalesThe authors gratefully acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through grant BIA2015-69330-P (MINECO), the European Union programme through grant ERASMUS Traineeships 2017 - E+ and the support from CALTER Ingenieria and SOFiSTiK AG for providing a software licenseBessini, J.; Shepherd, P.; Monleón Cremades, S.; Lazaro, C. (2020). Design of bending-active tied arches by using a multi-objective optimization method. Structures. 27:2319-2328. https://doi.org/10.1016/j.istruc.2020.07.045S231923282

The role of different sliding resistances in limit analysis of hemispherical masonry domes

A limit analysis method for masonry domes composed of interlocking blocks with non-isotropic sliding resistance is under development. This paper reports the first two steps of that work. It first introduces a revision to an existing limit analysis approach using the membrane theory with finite hoop stresses to find the minimum thickness of a hemispherical dome under its own weight and composed of conventional blocks with finite isotropic friction. The coordinates of an initial axisymmetric membrane surface are the optimization variables. During the optimization, the membrane satisfies the equilibrium conditions and meets the sliding constraints where intersects the block interfaces. The results of the revised procedure are compared to those obtained by other approaches finding the thinnest dome. A heuristic method using convex contact model is then introduced to find the sliding resistance of the corrugated interlocking interfaces. Sliding of such interfaces is constrained by the Coulomb’s friction law and by the shear resistance of the locks keeping the blocks together along two orthogonal directions. The role of these two different sliding resistances is discussed and the heuristic method is applied to the revised limit analysis method

When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness

Once cognition is recognized as having a 'dual' information source, the information theory chain rule implies that isolating coresident information sources from crosstalk requires more metabolic free energy than permitting correlation. This provides conditions for an evolutionary exaptation leading to the rapid, shifting global neural broadcasts of consciousness. The argument is quite analogous to the well-studied exaptation of noise to trigger stochastic resonance amplification in neurons and neuronal subsystems. Astrobiological implications are obvious

Analysis and Optimum Design of Curved Roof Structures

Curved steel buildings are frequently designed to supply the users of the structure with ordinary light with a sense of capaciousness as well as grandness in public facilities such as stations, buying malls, leisure centres and airports. This paper presents a method for analysis and optimum design of 2D and 3D curved roof trusses subjected to static loading and specified set of constraints. Here the optimization refers to minimization of total weight of curved roof structures such that they can resist applied forces (stress constraint) and don’t exceed certain deformations (displacement constraints). The finite element formulations is developed and implemented for the static analysis of curved roof trusses to determine the stresses and displacements. The use of a reliable and competitive procedure for finding the optimum solutions for problems involving continuous design variables based on genetic algorithms is demonstrated and used in this study .The performance of genetic algorithms is affected by various factors such as coefficients and constants, genetic operators, parameters and some strategies. Member grouping and initial population strategies are also important factors. Optimization is an automated design procedure in which the computers are utilized to obtain the best results. The numerical methods of structural optimization with applications of computers automatically generate a near optimal design (converge to solve) in interactive manner. A program was modified and used to automate analysis and optimization of the structure written in FORTRAN language based Finite Element analysis and Genetic Algorithm optimization technique. The developed method is tested on several examples and compared with previous researches or SAP2000 results. It is concluded that this method can serve as a useful tool in engineering design and optimization of curved roofs

MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models

Deep learning has been widely used in various applications from different fields such as computer vision, natural language processing, etc. However, the training models are often manually developed via many costly experiments. This manual work usually requires substantial computing resources, time, and experience. To simplify the use of deep learning and alleviate human effort, automated deep learning has emerged as a potential tool that releases the burden for both users and researchers. Generally, an automatic approach should support the diversity of model selection and the evaluation should allow users to decide upon their demands. To that end, we propose a multi-objective end-to-end self-optimized approach for constructing deep learning models automatically. Experimental results on well-known datasets such as MNIST, Fashion, and Cifar10 show that our algorithm can discover various competitive models compared with the state-of-the-art approach. In addition, our approach also introduces multi-objective trade-off solutions for both accuracy and uncertainty metrics for users to make better decisions

Structural optimization in steel structures, algorithms and applications

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