Topology Optimisation in Structural Steel Design for Additive Manufacturing

Topology Optimisation is a broad concept deemed to encapsulate different processes for computationally determining structural materials optimal layouts. Among such techniques, Discrete Optimisation has a consistent record in Civil and Structural Engineering. In contrast, the Optimisation of Continua recently emerged as a critical asset for fostering the employment of Additive Manufacturing, as one can observe in several other industrial fields. With the purpose of filling the need for a systematic review both on the Topology Optimisation recent applications in structural steel design and on its emerging advances that can be brought from other industrial fields, this article critically analyses scientific publications from the year 2015 to 2020. Over six hundred documents, including Research, Review and Conference articles, added to Research Projects and Patents, attained from different sources were found significant after eligibility verifications and therefore, herein depicted. The discussion focused on Topology Optimisation recent approaches, methods, and fields of application and deepened the analysis of structural steel design and design for Additive Manufacturing. Significant findings can be found in summarising the state-of-the-art in profuse tables, identifying the recent developments and research trends, as well as discussing the path for disseminating Topology Optimisation in steel construction.

Methods of the automated aircraft configuration

Aircraft are great and comprehensive technical systems, which are characterised by the great configuration density. This article presents formation of the geometrical conceptual design of the aircraft on the basis of the formal heuristic procedures within the framework of the infrastructural constraints. In addition, this article includes description of influence of the aerodynamic configuration and the volume-weight configuration upon formation of the geometrical conceptual design of the aircraft, as well as description of other mass/inertia characteristics of the aggregates, which are to be installed within the specific aircraft. This article also states that in the case of the "very strict" infrastructural constraints, it is necessary to solve "the inverse" problem of configuration, that is the problem, where initial data for formation of the geometrical conceptual design of the aircraft are determined by the necessary configuration space, which is determined by the infrastructural constraints. The article presents the project problem (in aggregate) of finding rational values for parameters of the geometrical conceptual design of the aircraft as the problem of the multiple criteria discrete optimisation. This article states that it is possible to formulate this problem as the search of the vector of structural parameters (that is, search of the multitude of the admissible alternatives of the drawing and designing solutions).

An Optimisation Model to Consider the NIMBY Syndrome within the Landfill Siting Problem

This paper proposes a discrete optimisation model and a heuristic algorithm to solve the landfill siting problem over large areas. Besides waste transport costs and plant construction and maintenance costs, usually considered in such problems, the objective function includes economic compensation for residents in the areas affected by the landfill, to combat the NIMBY (Not In My Back Yard) syndrome or, at least, reduce its adverse effects. The proposed methodology is applied to a real-scale case study, the region of Campania, Italy, where waste management is a thorny problem. Numerical results show that the proposed algorithm may be used to obtain a solution to the problem, albeit sub-optimal, with acceptable computing times, and the proposed model tends to locate landfills in sparsely populated sites.

Comparative Document Summarisation via Classification

Thispaperconsidersextractivesummarisationinacomparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13months.Weobserve thatgradient-based optimisationoutperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.