The Newmark’s numerical method of computing deflections, moments and buckling loads of isolated columns is extended for the analysis of elastic buckling loads and buckling modes of prismatic and non-prismatic single story, multi-span frames with combinations of hinged and fixed columns. Step-by-step description of the developed procedure is presented, using statics equilibrium, slope deflection equations and boundary conditions. The elastic line of the buckling mode is determined as a major part of the solution, and the numerical procedure is used to calculate the buckling loads and the columns’ effective length factor for multi-span frames. The most favorable variation of cross-section of tapered frame columns is calculated, giving the maximum possible elastic critical load of the frame for constant columns’ volume.
Architectural engineering. Structural engineering of buildings, Structural engineering (General)
Oleksandr Kosenkov, Ehsan Zabardast, Jannik Fischbach
et al.
Implementing privacy by design (PbD) according to the General Data Protection Regulation (GDPR) is met with a growing number of requirements engineering (RE) approaches. However, the question of which RE method for PbD fits best the goals of organisations remains a challenge. We report our endeavor to close this gap by synthesizing a goal-centric approach for PbD methods assessment. We used literature review, interviews, and validation with practitioners to achieve the goal of our study. As practitioners do not approach PbD systematically, we suggest that RE methods for PbD should be assessed against organisational goals, rather than process characteristics only. We hope that, when further developed, the goal-centric approach could support the development, selection, and tailoring of RE practices for PbD.
Carla Grandón-Soliz, A. Sandoli, Giovanni Fabbrocino
Weathering steel has a fascinating history that dates back to the 1930s, and its evolution has left an indelible mark on various industries, from railways to architecture. Thanks to its high corrosion resistance with respect to conventional steel, weathering steel has assumed key roles in structural applications (buildings, bridges, railways, etc.), non-structural elements (facades, decorative elements), and installations at archaeological sites (retrofitting, sculptures), especially when exposed to aggressive environments. This paper is aimed at providing a state-of-the-art overview of the application of weathering steel in architecture and engineering applications, focusing on the development of scientific and technical knowledge on the subject and on future directions arising from current utilization. An evolution timeline of weathering steel-based constructions and their structural/typological classification is illustrated and discussed. In such a context, pros and cons related to maintenance aspects of weathering steel structures are also discussed, accounting for costs relative to structural and nonstructural maintenance and those related to environmental sustainability with respect to other traditional constructions. From a structural design point of view, the rules and recommendations provided by the main national and international standards—concerning material properties and types, design, and checks on structural members made with weathering steel—are analyzed, critically discussed, and compared, also with the aim of identifying possible gaps in comparison with other construction materials.
Material efficiency has become a central objective in contemporary building design, driven by urgent environmental imperatives and the growing need to reduce resource consumption. This systematic review examines the role of advanced structural modelling techniques in the development of load-optimised, materially efficient structures. Emphasising the synergy between structural form and force flow, the study investigates the application of computational tools, including finite element analysis (FEA), topology optimisation, parametric design, and AI-driven modelling strategies. These approaches enable designers to align structural geometry with internal stress patterns, reducing excess material use without sacrificing safety or performance. The review synthesises recent innovations in form-finding methods, geometry-informed optimisation, and performance-based design workflows that collectively support material minimisation strategies. Special attention is given to how these tools are implemented in various structural typologies, including shell structures, high-rise systems, and freeform architecture, demonstrating the practical viability and environmental benefits of computationally guided design. In addition to technical advances, the review identifies key challenges facing the broader adoption of these methods. These include limitations in computational accuracy, difficulties in scaling up optimization techniques, and the persistent divide between architectural and engineering practices. The analysis highlights the importance of interdisciplinary collaboration and robust feedback loops between digital modelling, structural analysis, and material behaviour. Ultimately, the findings advocate for a paradigm in which structural mechanics serves not only as a tool for verification but also as a generative driver of form. By leveraging emerging modelling techniques, the construction industry can move toward a more sustainable trajectory—one where resource efficiency, structural integrity, and architectural expression coexist harmoniously. This systematic review contributes to ongoing discourse on how digital technologies and structural intelligence can inform the design of buildings that are not only innovative and efficient but also environmentally responsible.
Sufficiency policies aim to decrease and avoid the demand for energy, materials, land and water. This research examines the acceptability of sufficiency consumption policies among Finnish households and how Finnish households experience sufficiency as a guiding principle specifically for sustainable energy consumption. The research questions are: (1) In what ways do the Finnish households find the proposed sufficiency policies acceptable? (2) How do the Finnish households justify their position towards the sufficiency policies? (3) How do the circumstances of households influence the acceptance of sufficiency measures? Qualitative semi-structured household interviews (n = 39) were conducted in 2023. Participants were asked to evaluate acceptability and feasibility of sufficiency policy measures. The results show that the principle of sufficiency was generally well-received by the households and the policies were generally found acceptable under certain conditions. Exceptions were the two hardest policies: higher income tax and restricting the size of living space. However, the acceptability of the different policy measures varied notably between individuals, which suggests that it would be important to tailor policies to different contexts and target groups. Policy relevance The central findings of this study are: (1) there are notable differences between individuals on what is considered acceptable and easy to comply with; (2) the level of freedom of choice is crucial for policy acceptance: new sufficiency policies should be designed to include voluntary aspects and the possibility to choose which measures to implement and how; and (3) crises awareness as well as perceived fairness increase the tendency to accept even relatively hard sufficiency policy measures.
Architectural engineering. Structural engineering of buildings
Allysson Allex Araújo, Marcos Kalinowski, Maria Teresa Baldassarre
In recent years, Software Engineering (SE) scholars and practitioners have emphasized the importance of integrating soft skills into SE education. However, teaching and learning soft skills are complex, as they cannot be acquired passively through raw knowledge acquisition. On the other hand, hackathons have attracted increasing attention due to their experiential, collaborative, and intensive nature, which certain tasks could be similar to real-world software development. This paper aims to discuss the idea of hackathons as an educational strategy for shaping SE students' soft skills in practice. Initially, we overview the existing literature on soft skills and hackathons in SE education. Then, we report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. Lastly, our findings and new directions are analyzed through the lens of Self-Determination Theory, which offers a psychological lens to understand human behavior. This paper contributes to academia by advocating the potential of hackathons in SE education and proposing concrete plans for future research within SDT. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.
Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential to keep development time and costs at acceptable levels. In this work, an innovative self-learning calibration method is presented based on in-cylinder pressure curve shaping. This method combines Principal Component Decomposition with constrained Bayesian Optimization. To realize maximal thermal engine efficiency, the optimization problem aims at minimizing the difference between the actual in-cylinder pressure curve and an Idealized Thermodynamic Cycle. By continuously updating a Gaussian Process Regression model of the pressure's Principal Components weights using measurements of the actual operating conditions, the mean in-cylinder pressure curve as well as its uncertainty bounds are learned. This information drives the optimization of calibration parameters, which are automatically adapted while dealing with the risks and uncertainties associated with operational safety and combustion stability. This data-driven method does not require prior knowledge of the system. The proposed method is successfully demonstrated in simulation using a Reactivity Controlled Compression Ignition engine model. The difference between the Gross Indicated Efficiency of the optimal solution found and the true optimum is 0.017%. For this complex engine, the optimal solution was found after 64.4s, which is relatively fast compared to conventional calibration methods.
With rapid advances in design methods and structural analysis techniques, computational generative design strategies have been adopted more widely in the field of architecture and engineering. As a performance-based design technique to find out the most efficient structural form, topology optimization provides a powerful tool for designers to explore lightweight and elegant structures. Building on this background, this study proposes an innovative pedestrian bridge design, which covers the process from conceptualization to detailed design implementation. This pedestrian bridge, with a main span of 152 m, needs to meet some unique architectural requirements, while addressing multiple engineering challenges. Aiming to reduce the depth of the girder but still meeting the load-carrying capacity requirements, the superstructure of this bridge adopts a variable-depth spinal-shaped girder in the center of its deck, thus forming an elegant curving facade, from which one pathway cantilevers on either side. At one end of the bridge, given considerable elevation difference between the bridge deck and the ground, a two-level Fibonacci-type spiral-shaped bicycle ramp is provided. The superstructure is supported by a series of organic tree-shaped branching piers resulting from the topology optimization. The ingenious design for the elegant profile of the bicycle ramp generates an enjoyable and dynamic crossing experience, with scenic views in all directions. By virtue of technological innovation, the pedestrian bridge is expected to create an iconic, cost-effective, and low-maintenance solution. A brief overview of the theoretical background of the bi-directional evolutionary structure optimization (BESO) and the multi-material BESO approach is also offered in this paper, while the construction requirements and challenges, conceptual development process, form-finding strategy, detailed design, and construction method of the bridge are presented.
Hanne Rangnes Seeberg, Sverre Magnus Haakonsen, Marcin Luczkowski
Facing increasing sustainability demands, the construction industry is at a turning point where the implementation of circular economy (CE) strategies plays an essential role in driving the necessary transformation aimed at reducing the environmental impact. To facilitate this shift, structural engineering must effectively integrate circular principles into building design. With the exponential growth of research articles within this field, it is crucial to map the evolution of the research area. The objective of this study is to detail the trends with, challenges to, and research contributions, integration, and material applications of CE principles within structural engineering. Consequently, a systematic mapping of the CE within the field of structural engineering has been conducted in this study. Initially, the mapping process began with the identification of relevant keywords, followed by searches across four databases. Each resulting article was carefully screened against content criteria, culminating in 91 publications that were thoroughly evaluated. The publications were then categorized and analyzed based on attributes such as research type, circular design, materials, and applications. The results are presented through informative figures and tables. The analysis of the research indicates a predominant focus on technical solutions for structural systems, with demountable connections designed to facilitate the future reuse of materials representing more than half of the literature reviewed. A significant portion of the literature also addresses designing from reclaimed elements; these articles reflect a transformation in engineering approaches, incorporating computational design and innovative methodologies. The focus on steel as a structural material is prominent in the reviewed literature. However, there is an increasing focus on timber, which signals a definitive shift toward sustainable structural systems. Recurring challenges identified in the literature regarding the transition to a circular economy (CE) in the construction industry include the need for industry-wide adoption, precise standardization, the integration of digital tools, and the overcoming of related obstacles in policy and market acceptances. Furthermore, the literature demonstrates a significant research gap: the absence of a comprehensive digital framework enabling an effective digital circular structural design workflow.
The optimal structural design is imperative in order to minimize material consumption and reduce the environmental impacts of construction. Given the complexity in the formulation of structural design problems, the process of optimization is commonly performed using artificial intelligence (AI) global optimization, such as the genetic algorithm (GA). However, the integration of AI-based optimization, together with visual programming (VP), in building information modeling (BIM) projects warrants further investigation. This study proposes a workflow by combining structure analysis, VP, BIM, and GA to optimize trusses. The methodology encompasses several steps, including the following: (i) generation of parametric trusses in Dynamo VP; (ii) performing finite element modeling (FEM) using Robot Structural Analysis (RSA); (iii) retrieving and evaluating the FEM results interchangeably between Dynamo and RSA; (iv) finding the best solution using GA; and (v) importing the optimized model into Revit, enabling the user to perform simulations and engineering analysis, such as life cycle assessment (LCA) and quantity surveying. This methodology provides a new interoperable framework with minimal interference with existing supply-chain processes, and it will be flexible to technology literacy and allow architectural, engineering and construction (AEC) professionals to employ VP, global optimization, and FEM in BIM-based projects by leveraging open-sourced software and tools, together with commonly used design software. The feasibility of the proposed workflow was tested on benchmark problems and compared with the open literature. The outcomes of this study offer insight into the opportunities and limitations of combining VP, GA, FEA, and BIM for structural optimization applications, particularly to enhance structural efficiency and sustainability in construction. Despite the success of this study in developing a workable, user-friendly, and interoperable framework for the utilization of VP, GA, FEM, and BIM for structural optimization, the results obtained could be improved by (i) increasing the callback function speed between Dynamo and RSA through specialized application programming interface (API); and (ii) fine-tuning the GA parameters or utilizing other advanced global optimization and supervised learning techniques for the optimization.
In the last two decades, several researchers provided snapshots of the "current" state and evolution of empirical research in requirements engineering (RE) through literature reviews. However, these literature reviews were not sustainable, as none built on or updated previous works due to the unavailability of the extracted and analyzed data. KG-EmpiRE is a Knowledge Graph (KG) of empirical research in RE based on scientific data extracted from currently 680 papers published in the IEEE International Requirements Engineering Conference (1994-2022). KG-EmpiRE is maintained in the Open Research Knowledge Graph (ORKG), making all data openly and long-term available according to the FAIR data principles. Our long-term goal is to constantly maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. Besides KG-EmpiRE, we provide its analysis with all supplementary materials in a repository. This repository contains all files with instructions for replicating and (re-)using the analysis locally or via executable environments and for repeating the research approach. Since its first release based on 199 papers (2014-2022), KG-EmpiRE and its analysis have been updated twice, currently covering over 650 papers. KG-EmpiRE and its analysis demonstrate how innovative infrastructures, such as the ORKG, can be leveraged to make data from literature reviews FAIR, openly available, and maintainable for the research community in the long term. In this way, we can enable replicable, (re-)usable, and thus sustainable literature reviews to ensure the quality, reliability, and timeliness of their research results.
Natural Language Processing (NLP) is now a cornerstone of requirements automation. One compelling factor behind the growing adoption of NLP in Requirements Engineering (RE) is the prevalent use of natural language (NL) for specifying requirements in industry. NLP techniques are commonly used for automatically classifying requirements, extracting important information, e.g., domain models and glossary terms, and performing quality assurance tasks, such as ambiguity handling and completeness checking. With so many different NLP solution strategies available and the possibility of applying machine learning alongside, it can be challenging to choose the right strategy for a specific RE task and to evaluate the resulting solution in an empirically rigorous manner. In this chapter, we present guidelines for the selection of NLP techniques as well as for their evaluation in the context of RE. In particular, we discuss how to choose among different strategies such as traditional NLP, feature-based machine learning, and language-model-based methods. Our ultimate hope for this chapter is to serve as a stepping stone, assisting newcomers to NLP4RE in quickly initiating themselves into the NLP technologies most pertinent to the RE field.
Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, the scope of knowledge engineering, as apparent from its target tasks and use cases, has been shifting, together with its paradigms such as expert systems, semantic web, and language modeling. The intended use cases and supported user requirements between these paradigms have not been analyzed globally, as new paradigms often satisfy prior pain points while possibly introducing new ones. The recent abstraction of systemic patterns into a boxology provides an opening for aligning the requirements and use cases of knowledge engineering with the systems, components, and software that can satisfy them best. This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering by leveraging the software engineering methodology of creating reference architectures. We describe how a reference architecture can be iteratively designed and implemented to associate user needs with recurring systemic patterns, building on top of existing knowledge engineering workflows and boxologies. We provide a six-step roadmap that can enable the development of such an architecture, providing an initial design and outcome of the definition of architectural scope, selection of information sources, and analysis. We expect that following through on this vision will lead to well-grounded reference architectures for knowledge engineering, will advance the ongoing initiatives of organizing the neurosymbolic knowledge engineering space, and will build new links to the software architectures and data science communities.
Yvonne Dittrich, Johan Bolmsten, Catherine Seidelin
Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings, and makes it possible to develop methods, tools and techniques with software engineering practitioners. However, as the research moves beyond the observational approach, it requires a different kind of interaction with the software development organisation. This makes action research a challenging endeavour, and it makes it difficult to teach action research through a course that goes beyond explaining the principles. This chapter is intended to support learning and teaching action research, by providing a rich set of examples, and identifying tools that we found helpful in our action research projects. The core of this chapter focusses on our interaction with the participating developers and domain experts, and the organisational setting. This chapter is structured around a set of challenges that reoccurred in the action research projects in which the authors participated. Each section is accompanied by a toolkit that presents related techniques and tools. The exercises are designed to explore the topics, and practise using the tools and techniques presented. We hope the material in this chapter encourages researchers who are new to action research to further explore this promising opportunity.
Gabriel C. Ullmann, Yann-Gaël Guéhéneuc, Fabio Petrillo
et al.
Game engines are tools to facilitate video game development. They provide graphics, sound, and physics simulation features, which would have to be otherwise implemented by developers. Even though essential for modern commercial video game development, game engines are complex and developers often struggle to understand their architecture, leading to maintainability and evolution issues that negatively affect video game productions. In this paper, we present the Subsystem-Dependency Recovery Approach (SyDRA), which helps game engine developers understand game engine architecture and therefore make informed game engine development choices. By applying this approach to 10 open-source game engines, we obtain architectural models that can be used to compare game engine architectures and identify and solve issues of excessive coupling and folder nesting. Through a controlled experiment, we show that the inspection of the architectural models derived from SyDRA enables developers to complete tasks related to architectural understanding and impact analysis in less time and with higher correctness than without these models.
Software practitioners discuss problems at work with peers, in-person and online. These discussions can be technical (e.g., how to fix a bug?) and social (e.g., how to assign work fairly?). While there is a growing body of knowledge exploring fairness problems and solutions in the human and social factors of software engineering, most focus has been on specific problems. This study provides fairness discussions by software practitioners on Stack Exchange sites. We present an exploratory study presenting the fairness experience of software practitioners and fairness expectations in software teams. We also want to identify the fairness aspects software practitioners talk about the most. For example, do they care more about fairness in income or how they are treated in the workplace? Our investigation of fairness discussions on eight Stack Exchange sites resulted in a list of 136 posts (28 questions and 108 answers) manually curated from 4,178 candidate posts. The study reveals that the majority of fairness discussions (24 posts) revolve around the topic of income suggesting that many software practitioners are highly interested in matters related to their pay and how it is fairly distributed. Further, we noted that while not discussed as often, discussions on fairness in recruitment tend to receive the highest number of views and scores. Interestingly, the study shows that unfairness experiences extend beyond the protected attributes. In this study, only 25 out of 136 posts mention protected attributes, with gender mainly being discussed.
Abstract. In the last years, the AEC (Architecture, Engineering and Construction) domain has exponentially increased the use of BIM and HBIM models for several applications, such as planning renovation and restoration, building maintenance, cost managing, or structural/energetic retrofit design. However, obtaining detailed as-built BIM models is a demanding and time-consuming process. Especially in historical contexts, many different and complex architectural elements need to be carefully and manually modelled. Meshes or surfaces and NURBS or polylines, derived from 3D reality-based data, are recently used as a reference for the HBIM accurate modelling. This work proposes a comprehensive and novel semi-automated approach to reconstruct architectural elements through the Visual Programming Language (VPL) Dynamo software and a Boundary-Representation method (B-rep), starting from 3D surveying data and point clouds classification. A wide package of scripts provides solutions for modelling complex shapes and transferring the obtained 3D models into BIM Authoring tools for a complete reconstruction phase. The presented procedure, useful for different BIM or HBIM applications, proved to reduce the modelling time significantly.