Operations Research and Engineering Letters 2023-03-13T20:58:10+00:00 Dragan Pamučar Open Journal Systems <p><em>Operations Research and Engineering Letters </em><em>(OREL)</em> publishes high-quality scientific papers that contribute significantly to the field of operational research and engineering science. The material published is of high quality and relevance, written in a manner that makes it accessible to all of this wide-ranging readership. Preference will be given to papers with decision-making implications to the practice of management and engineering.</p> <p><em>Aims and Scope</em></p> <p>The principal aim of the journal is to bring together the latest research and development in various fields of operational research. We would like to highlight that papers should refer to <a href="" target="_blank" rel="noopener">Aims and scope</a>, but they are not limited to.</p> <p><em>Publication Frequency </em></p> <p>One issue per year is published online, but processed and accepted papers, with full bibliographic data, are added to the issue continuously over the whole year.</p> <p><em>Open Access</em></p> <p>This is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author.</p> <p><em>Publication fee</em></p> <p>There is no submission charge or publication fee. Publication in OREL is free of charge for the authors.</p> <p><em>Publisher</em></p> <p><a href="" target="_blank" rel="noopener">Faculty of Civil Engineering Management, University “Union–Nikola Tesla”, Belgrade, Serbia</a></p> A Novel Ann Technique for Fast Prediction of Structural Behavior 2023-01-31T09:02:48+00:00 Filip Đorđević <p>In recent decades, different concepts of machine learning (ML) have found applications in solving many engineering problems. Less time consumption in performing analyses, better optimization of the quality-price ratio and maintaining high model accuracy are just some ML advantages compared to traditional modeling procedures. There are currently a significant number of pre-trained machine learning models based on classification or regression tasks. However, there is a tendency to improve them through the implementation of the transfer learning (TL) approach. This article proposes an upgrade of the existing, pre-trained artificial neural network (ANN) model for the evaluation of the ultimate compressive strength of square concrete-filled steel tubular (CFST) columns. The aim of the improved TL model is to adapt to the problem of predicting the axial capacity of rectangular CFST columns in a more optimal way. The attractiveness of the TL is reflected through the possibility of overcoming certain shortcomings of classical models. Quick adaptation to the problem with small modifications of the existing surrogate model, better overcoming of potential overfitting even with a small dataset, and improved convergence towards the required solutions are some of the advanced TL strategies. The robustness of the proposed model was demonstrated through verification with experimental solutions and validation with the Eurocode 4 (EC4) design code. The application of such innovative paradigms can also be ensured for other research fields in a similar manner.</p> 2023-01-31T00:00:00+00:00 Copyright (c) 2023 Filip Đorđević Creation of a Genetic Algorithm to Locate the Optimal Position of Columns in a Regular Building 2023-01-31T09:13:29+00:00 Jorge Teixeira João Pedro Martins João Correia <p>The construction of buildings needs to consider a considerable number of variables and design rules to verify the structural integrity of the building. These rules require to consider the actions in the environment of the construction, the purpose of the building and the construction materials. The growing demand for taller and efficient buildings (safety rules and structural rules stricter) and the increasing prices of the construction’s materials lead the engineers to find better ways to optimize the building for its propose and still complies all the structural rules. Thus, the use of optimization algorithms to accomplish a certain goal be usen more often. So, in this work we will use a Genetic Algorithm (GA) to determine a better position of columns in a regular and orthogonal building which the chosen goal is smaller. To accomplish this, we will use two different goals (weight and cost), two structural typologies (concrete and steel typology) and two different column positions methods. The experimental results indicate that it is possible to find good solutions but additional studies into the GA should be performed to increase the performance of the algorithm.</p> 2023-01-31T00:00:00+00:00 Copyright (c) 2023 Jorge Teixeira, João Pedro Martins, João Correia Digital Twin Technology and Its Application in the Different Technical Disciplines With Reference to Construction 2023-03-13T20:58:10+00:00 Predrag Jovanović <p>The paper presents a complete analysis of digital twin technology from a detailed explanation of what digital twin technology is, through an explanation of which technical disciplines it can be used and what are the benefits of using digital twin technology with examples from projects.</p> 2023-03-13T00:00:00+00:00 Copyright (c) 2023 Predrag Jovanović