Hasil untuk "Architectural engineering. Structural engineering of buildings"

Isaac Nsor, Bennetta Koomson, Patrick Boakye

A Jatropha curcas-based bio-lubricant was developed and evaluated for automotive applications through acid esterification, base-catalyzed transesterification, in-situ peracetic acid epoxidation, and multi-additive blending. The molecular transformations were confirmed by GC–MS and FTIR, and physical properties were assessed using ASTM/ISO methods. Performance was further evaluated through a 12,000 km in-service motorcycle engine test and XRF wear analysis. Transesterification decreased kinematic viscosity at 40 °C from 36.6 to 16.7 cSt, and subsequent epoxidation and additive incorporation increased the viscosity to 47.2 cSt (40 °C) and 15.2 cSt (100 °C), meeting ISO VG-46 requirements. The resultant composition (JOA) showed density (888 kg m⁻³), viscosity index (227), pour point (−20 °C), flash point (230 °C), free fatty acid content (0.1%), and acid value < 0.5 mg KOH g⁻¹. The predominant components of JOA were determined by GC–MS, and ester and epoxide functionalities were confirmed by FTIR. During operation, the viscosity index decreased to 192.9, while maintaining acceptable viscosity retention and thermal stability. XRF analysis indicated controlled wear, with metal concentrations remaining within reported OEM guideline ranges. These results demonstrate the feasibility of using Jatropha curcas oil as a sustainable base stock for automotive lubricants.

Jordi Cabot

There is a pressing need for better development methods and tools to keep up with the growing demand and increasing complexity of new software systems. New types of user interfaces, the need for intelligent components, sustainability concerns, etc. bring new challenges that we need to handle. In the last years, model-driven engineering (MDE), including its latest incarnation, i.e. low/no-code development, has been key to improving the quality and productivity of software development, but models themselves are becoming increasingly complex to specify and manage. At the same time, we are witnessing the growing popularity of vibe coding approaches that rely on Large Language Models (LLMs) to transform natural language descriptions into running code at the expense of potential code vulnerabilities, scalability issues and maintainability concerns. While many may think vibe coding will replace model-based engineering, in this paper we argue that, in fact, the two approaches can complement each other and provide altogether different development paths for different types of software systems, development scenarios, and user profiles. In this sense, we introduce the concept of \textit{vibe-driven model-based engineering} as a novel approach to integrate the best of both worlds (AI and MDE) to accelerate the development of reliable complex systems. We outline the key concepts of this new approach and highlight the opportunities and open challenges it presents for the future of software development.

Deirdre O'Regan, Anil C. Kokaram

The rapid rise of LLMs over the last few years has promoted growing experimentation with LLM-driven AI tutors. However, the details of implementation, as well as the benefit in a teaching environment, are still in the early days of exploration. This article addresses these issues in the context of implementation of an AI Teaching Assistant (AI-TA) using Retrieval Augmented Generation (RAG) for Trinity College Dublin's Master's Motion Picture Engineering (MPE) course. We provide details of our implementation (including the prompt to the LLM, and code), and highlight how we designed and tuned our RAG pipeline to meet course needs. We describe our survey instrument and report on the impact of the AI-TA through a number of quantitative metrics. The scale of our experiment (43 students, 296 sessions, 1,889 queries over 7 weeks) was sufficient to have confidence in our findings. Unlike previous studies, we experimented with allowing the use of the AI-TA in open-book examinations. Statistical analysis across three exams showed no performance differences regardless of AI-TA access (p > 0.05), demonstrating that thoughtfully designed assessments can maintain academic validity. Student feedback revealed that the AI-TA was beneficial (mean = 4.22/5), while students had mixed feelings about preferring it over human tutoring (mean = 2.78/5).

Tong Duy Son, Zhihao Liu, Piero Brigida et al.

Engineering workflows such as design optimization, simulation-based diagnosis, control tuning, and model-based systems engineering (MBSE) are iterative, constraint-driven, and shaped by prior decisions. Yet many AI methods still treat these activities as isolated tasks rather than as parts of a broader workflow. This paper presents Agentic Engineering Intelligence (AEI), an industrial vision framework that models engineering workflows as constrained, history-aware sequential decision processes in which AI agents support engineer-supervised interventions over engineering toolchains. AEI links an offline phase for engineering data processing and workflow-memory construction with an online phase for workflow-state estimation, retrieval, and decision support. A control-theoretic interpretation is also possible, in which engineering objectives act as reference signals, agents act as workflow controllers, and toolchains provide feedback for intervention selection. Representative automotive use cases in suspension design, reinforcement learning tuning, multimodal engineering knowledge reuse, aerodynamic exploration, and MBSE show how diverse workflows can be expressed within a common formulation. Overall, the paper positions engineering AI as a problem of process-level intelligence and outlines a practical roadmap for future empirical validation in industrial settings.

Narmin Abouelkhier, Abdul Rauf, Laurens Luyten et al.

Pauline Heger, Christoph Bieber, Mennatullah Hendawy et al.

This study analyses a real-world experiment (RWE) on heat transition in the urban context of a city–university partnership in Bochum, Germany, highlighting the theoretical foundations, methodological design and practical challenges of transdisciplinary research. Informed by concepts of transdisciplinarity and RWEs, the study situates RWEs as hybrid infrastructures linking scientific enquiry with societal transformation. Transdisciplinarity is conceptualised as a reflexive and integrative principle that fosters reciprocal learning between academic, municipal and civic actors. The research applies the Three-Circle Model of actor involvement as a heuristic to map the evolving, and over time changing, roles of stakeholders. The study employs participatory observation, ethnographic field notes, document analysis and informal interviews. The findings reveal how RWEs function as relational infrastructures that balance experimental rigor with public accessibility, while navigating challenges such as power asymmetries, participation myths and institutional constraints. By embedding agile methods and low-threshold participation formats into everyday urban settings, RWEs emerge as sites of co-creation, democratic innovation and iterative learning. The study offers practical guidance for designing inclusive, transdisciplinary experiments that bridge theory and practice in urban transformation. Practice relevance The organisation of an RWE on heat transition in a German city offers insights for practitioners and policymakers engaged in urban transformation. The project demonstrates how academic institutions, municipal administration and citizens can co-develop participatory formats that translate complex topics—e.g. district heating—into accessible, low-threshold engagement opportunities. By scrutinising stakeholder roles and involvement, the analysis shows how responsibilities and influence may shift over time, highlighting the importance of flexible communication structures. Key lessons include the value of short-term, event-based participation to stimulate citizen engagement, the need to balance methodology with accessibility, and the potential of RWEs to foster collaboration across fragmented structures in public administration. For policymakers, the study describes RWEs as effective tools for strengthening public acceptance, testing innovative governance models and stabilising contested transitions. Practitioners gain practical guidance on designing inclusive, adaptive processes that align research with municipal needs.

Stefania Mornati, Laura Greco, Francesco Spada

The paper presents two emblematic examples located at the extremes of the most significant phases of building industrialization in Italy. The first example, in Campania, is the Pozzi Ginori complex in Sparanise (1960-1963) by Luigi Figini and Gino Pollini; the second, in Lazio, is the IBM Italia factory in Santa Palomba (1979-1984) by Marco Zanuso. These industrial complexes both belonged to a program for the industrial development of the most disadvantaged socio-economic areas in Italy. These two industrial plants, resulting from studies by well-known designers, represent the transition from formal and technological experimentation to an adaptation to standard production. They involve the whole project and the construction site and are characterized by the concentration of various innovative aspects, such as the treatment of finishing materials and a sophisticated relationship between building typology and landscape context.

Sumiko Ebara

Abstract Most of the dual‐purpose buildings built by the Incorporated Church Building Society in the 19th century were mission buildings. In the 20th century, many consecrated churches were constructed as dual‐purpose buildings. Installing a stage for secular use became common. However, reserving at least one‐third of the total floor to be used exclusively for worship was recommended. In addition, several floor plans emerged that allowed the worship floor to be extended but not vice versa. A wider range of floor plan variations was found that explicitly identified worship as the primary use.

Kelin Fu, Tianyu Liu, Zeyu Shang et al.

Automated environment configuration is a critical bottleneck in scaling software engineering (SWE) automation. To provide a reliable evaluation standard for this task, we present Multi-Docker-Eval benchmark. It includes 40 real-world repositories spanning 9 programming languages and measures both success in achieving executable states and efficiency under realistic constraints. Our extensive evaluation of state-of-the-art LLMs and agent frameworks reveals key insights: (1) the overall success rate of current models is low (F2P at most 37.7%), with environment construction being the primary bottleneck; (2) model size and reasoning length are not decisive factors, and open-source models like DeepSeek-V3.1 and Kimi-K2 are competitive in both efficiency and effectiveness; (3) agent framework and programming language also have significantly influence on success rate. These findings provide actionable guidelines for building scalable, fully automated SWE pipelines.

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn et al.

Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retrieval mechanisms with generative capabilities, resulting in more accurate, contextually relevant responses that enhance user experience. In particular, Transurban, a road operation company, is replacing its rule-based virtual assistant (VA) with a RAG-based VA (RAGVA) to offer more flexible customer interactions and support a wider range of scenarios. In this paper, drawing from the experience at Transurban, we present a comprehensive step-by-step guide for building a conversational application and how to engineer a RAGVA. These guides aim to serve as references for future researchers and practitioners. While the engineering processes for traditional software applications are well-established, the development and evaluation of RAG-based applications are still in their early stages, with numerous emerging challenges remaining uncharted. To address this gap, we conduct a focus group study with Transurban practitioners regarding developing and evaluating their RAGVA. We identified eight challenges encountered by the engineering team and proposed eight future directions that should be explored to advance the development of RAG-based applications. This study contributes to the foundational understanding of a RAG-based conversational application and the emerging AI software engineering challenges it presents.

Kenji Suzuki

Abstract The outbreak of COVID‐19 became a major problem at various facilities, and aged‐care facilities were no exception. In the event of a person detected positive for the disease, outpatient facilities were suspended, but inpatient facilities could not be suspended, and it became necessary to deal with the residents and positive people. This study aimed to obtain knowledge that would lead to the prevention of the spread of COVID‐19. The effects of differences in facility types were first examined on the occurrence of COVID‐19 cases and then on the response after testing positive in nursing homes for the elderly.

Lola Burgueño, Davide Di Ruscio, Houari Sahraoui et al.

Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made in Artificial Intelligence (AI), questions arise about the future of MDE, such as how existing MDE techniques and technologies can be improved or how other activities that currently lack dedicated support can also be automated. However, at the same time, it has to be revisited where and how models should be used to keep the engineers in the loop for creating, operating, and maintaining complex systems. To trigger dedicated research on these open points, we discuss the history of automation in MDE and present perspectives on how automation in MDE can be further improved and which obstacles have to be overcome in both the medium and long-term.

Jefferson Seide Molleri, Kai Petersen

In the dynamic field of Software Engineering (SE), where practice is constantly evolving and adapting to new technologies, conducting research is a daunting quest. This poses a challenge for researchers: how to stay relevant and effective in their studies? Empirical Software Engineering (ESE) has emerged as a contending force aiming to critically evaluate and provide knowledge that informs practice in adopting new technologies. Empirical research requires a rigorous process of collecting and analyzing data to obtain evidence-based findings. Challenges to this process are numerous, and many researchers, novice and experienced, found difficulties due to many complexities involved in designing their research. The core of this chapter is to teach foundational skills in research design, essential for educating software engineers and researchers in ESE. It focuses on developing a well-structured research design, which includes defining a clear area of investigation, formulating relevant research questions, and choosing appropriate methodologies. While the primary focus is on research design, this chapter also covers aspects of research scoping and selecting research methods. This approach prepares students to handle the complexities of the ever-changing technological landscape in SE, making it a critical component of their educational curriculum.

Rongqing Li, Jiaqi Yu, Changsheng Li et al.

Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (e.g., the number of convolutional layers) of a target black-box model can be exposed through a sequence of queries. There is a crucial limitation: these works assume the training dataset of the target model is known beforehand and leverage this dataset for model attribute attack. However, it is difficult to access the training dataset of the target black-box model in reality. Therefore, whether the attributes of a target black-box model could be still revealed in this case is doubtful. In this paper, we investigate a new problem of black-box reverse engineering, without requiring the availability of the target model's training dataset. We put forward a general and principled framework DREAM, by casting this problem as out-of-distribution (OOD) generalization. In this way, we can learn a domain-agnostic meta-model to infer the attributes of the target black-box model with unknown training data. This makes our method one of the kinds that can gracefully apply to an arbitrary domain for model attribute reverse engineering with strong generalization ability. Extensive experimental results demonstrate the superiority of our proposed method over the baselines.

Roberto Rossi

Multimodal GPTs represent a watershed in the interplay between Software Engineering and Generative Artificial Intelligence. GPT-4 accepts image and text inputs, rather than simply natural language. We investigate relevant use cases stemming from these enhanced capabilities of GPT-4. To the best of our knowledge, no other work has investigated similar use cases involving Software Engineering tasks carried out via multimodal GPTs prompted with a mix of diagrams and natural language.

Ran Wei, Simon Foster, Haitao Mei et al.

Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created documents, which are evaluated by system stakeholders through lengthy and complicated processes. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. This becomes increasingly important, as systems becomes more complex, it is a challenge to manage their development life-cycles, including coordination of development, verification and validation activities, and change impact analysis in inter-connected system assurance artifacts. Moreover, there is a need for assurance cases that support evolution during the operational life of the system, to enable continuous assurance in the face of an uncertain environment, as Robotics and Autonomous Systems (RAS) are adopted into society. In this paper, we contribute ACCESS - Assurance Case Centric Engineering of Safety-critical Systems, an engineering methodology, together with its tool support, for the development of safety critical systems around evolving model-based assurance cases. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts (e.g. system architectural models, system safety analysis, system behaviour models, etc.), and how formal methods can be integrated during the development process. We demonstrate how assurance cases can be automatically evaluated both at development and runtime. We apply our approach to a case study based on an Autonomous Underwater Vehicle (AUV).

L. Truong-Hong, Roderik Lindenbergh

Quy Thue Nguyen, Ramazan Livaoğlu

Soshi Nakamura, Yuta Hatanaka, Takuya Suzuki et al.

Abstract Effective stress analysis using liquefaction parameters is often employed for the numerical analysis of soil liquefaction. However, to the best of our knowledge, liquefaction parameters that simultaneously reproduce the liquefaction strength curve and effective stress path have not been reported to date. Herein, we propose a new method to estimate the model parameters used in effective stress analysis: the liquefaction strength and pore pressure curves obtained from laboratory tests are used as targets for inverse analysis. The results confirmed that the parameters obtained by our method could reproduce the liquefaction strength, pore pressure curves, and the effective stress path with high accuracy, which was difficult to achieve using conventional methods.

Adriana Marra, Giovanni Fabbrocino, Ilaria Trizio

The article deals with the definition and evaluation of a workflow to demonstrate that the parametric model of a historic building with a high level of digital maturity can be configured as a tool that fosters collaboration between the various professionals involved in the recovery and rehabilitation process of historic buildings. It also represents the starting point for developing structural models for quantitative analyses. The implementation of HBIM to the building aggregate called “La Giudea” in Santo Stefano di Sessanio (AQ), an artefact that is particularly representative of the historical building heritage of the small medieval village in the Abruzzo Inner Areas, has provided a model for managing the information deriving from the knowledge process. The model delivers to the use of the state of conservation, and it represents the base for the structural analysis of the asset and the identification of the measure and interventions for its preservation. The parametric model was developed in the design of strengthening and restoration works requiring interactions between digital environments. In such a framework, an investigation aimed at evaluating the interoperability of the digital model and particularly the vertical interoperability between different software packages has been explored.