Hasil untuk "Structural engineering (General)"

Junjie Shi, Weisong Sun, Zhenpeng Chen et al.

The rapid development of large language models is transforming software development. Beyond serving as code auto-completion tools in integrated development environments, large language models increasingly function as foundation models within coding agents in vibe-coding scenarios. In such settings, prompts play a central role in agent-based intelligent software development, as they not only guide the behavior of large language models but also serve as carriers of user requirements. Under the dominant conversational paradigm, prompts are typically divided into system prompts and user prompts. System prompts provide high-level instructions to steer model behavior and establish conversational context, while user prompts represent inputs and requirements provided by human users. Despite their importance, designing effective prompts remains challenging, as it requires expertise in both prompt engineering and software engineering, particularly requirements engineering. To reduce the burden of manual prompt construction, numerous automated prompt engineering methods have been proposed. However, most existing approaches neglect the methodological principles of requirements engineering, limiting their ability to generate artifacts that conform to formal requirement specifications in realistic software development scenarios. To address this gap, we propose REprompt, a multi-agent prompt optimization framework guided by requirements engineering. Experiment results demonstrate that REprompt effectively optimizes both system and user prompts by grounding prompt generation in requirements engineering principles.

Aminath Shaznie, Aishath Sinaau, Aishath Waheeda et al.

While circular economy offers a promising solution for issues in sustainability, this area remains unexplored in the context of Maldives. Hence, this paper explores the circular economy's complex environment, looking at its benefits, challenges, and implications for sustainable development. A concurrent mixed method design was used in the study, involving the simultaneous collection of quantitative and qualitative data, separate analysis, and integration during the interpretation phase. A total of 203 participants took part in the online survey and 17 participants took part in the interview. The quantitative data was analyzed by using descriptive statistical analysis and thematic analysis was used to analyze the qualitative data. As per the quantitative findings, the biggest challenges were identified as poor accountability of government organizations (3.97) and lack of infrastructure (3.97). Qualitative findings showed benefits such as reduced waste generation, enhanced resilience and sustainability, resource conservation, climate mitigation, and economic diversification. However, there are challenges to be overcome at both the micro and macroeconomic levels that were identified from this research. The findings of the research provide insight into circular economy benefits and challenges in Maldives, that could lead the government to draft effective policies and regulations. Moreover, the study’s findings contribute to the extant literature on the benefits and challenges of transitioning to a circular economy from a linear economy.

Mootez Saad, José Antonio Hernández López, Boqi Chen et al.

Large Language Models have significantly advanced the field of code generation, demonstrating the ability to produce functionally correct code snippets. However, advancements in generative AI for code overlook foundational Software Engineering (SE) principles such as modularity, and single responsibility, and concepts such as cohesion and coupling which are critical for creating maintainable, scalable, and robust software systems. These concepts are missing in pipelines that start with pre-training and end with the evaluation using benchmarks. This vision paper argues for the integration of SE knowledge into LLMs to enhance their capability to understand, analyze, and generate code and other SE artifacts following established SE knowledge. The aim is to propose a new direction where LLMs can move beyond mere functional accuracy to perform generative tasks that require adherence to SE principles and best practices. In addition, given the interactive nature of these conversational models, we propose using Bloom's Taxonomy as a framework to assess the extent to which they internalize SE knowledge. The proposed evaluation framework offers a sound and more comprehensive evaluation technique compared to existing approaches such as linear probing. Software engineering native generative models will not only overcome the shortcomings present in current models but also pave the way for the next generation of generative models capable of handling real-world software engineering.

Xingang Guo, Yaxin Li, Xiangyi Kong et al.

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).

Everton Cavalcante, Thais Batista, Flavio Oquendo

Modern systems are increasingly connected and more integrated with other existing systems, giving rise to \textit{systems-of-systems} (SoS). An SoS consists of a set of independent, heterogeneous systems that interact to provide new functionalities and accomplish global missions through emergent behavior manifested at runtime. The distinctive characteristics of SoS, when contrasted to traditional systems, pose significant research challenges within Software Engineering. These challenges motivate the need for a paradigm shift and the exploration of novel approaches for designing, developing, deploying, and evolving these systems. The \textit{International Workshop on Software Engineering for Systems-of-Systems} (SESoS) series started in 2013 to fill a gap in scientific forums addressing SoS from the Software Engineering perspective, becoming the first venue for this purpose. This article presents a study aimed at outlining the evolution and future trajectory of Software Engineering for SoS based on the examination of 57 papers spanning the 11 editions of the SESoS workshop (2013-2023). The study combined scoping review and scientometric analysis methods to categorize and analyze the research contributions concerning temporal and geographic distribution, topics of interest, research methodologies employed, application domains, and research impact. Based on such a comprehensive overview, this article discusses current and future directions in Software Engineering for SoS.

Siddhant Dutta, Iago Leal de Freitas, Pedro Maciel Xavier et al.

Federated Learning (FL) is a decentralized machine learning approach that has gained attention for its potential to enable collaborative model training across clients while protecting data privacy, making it an attractive solution for the chemical industry. This work aims to provide the chemical engineering community with an accessible introduction to the discipline. Supported by a hands-on tutorial and a comprehensive collection of examples, it explores the application of FL in tasks such as manufacturing optimization, multimodal data integration, and drug discovery while addressing the unique challenges of protecting proprietary information and managing distributed datasets. The tutorial was built using key frameworks such as $\texttt{Flower}$ and $\texttt{TensorFlow Federated}$ and was designed to provide chemical engineers with the right tools to adopt FL in their specific needs. We compare the performance of FL against centralized learning across three different datasets relevant to chemical engineering applications, demonstrating that FL will often maintain or improve classification performance, particularly for complex and heterogeneous data. We conclude with an outlook on the open challenges in federated learning to be tackled and current approaches designed to remediate and improve this framework.

Xinhao He, Dan Li

To control structural responses under various actions, the growing use of supplementary damping systems in modern civil engineering structures necessitates inspecting and evaluating their operational performance postinstallation. However, due to the dispersed placement and complex nonlinearities of these devices, difficulties arise in determining minimal sensor configuration. This is inherently connected to a pivotal challenge: establishing a reliable input-output mapping, which comprises both the mathematical model and sensor arrangements. Prior work indicates this can be achieved through theoretical observability analysis or Lie symmetries analysis, both of which provide different perspectives on the existence of a way to access the solutions of a system identification problem uniquely (at least locally). The present study introduces a unified framework, enhanced by algorithm realization as an application guide, for analyzing the observability and Lie symmetries of a given input-output mapping. We demonstrate its implementation via examples of a building structure with various damping systems under different conditions such as seismic loads, wind loads, and operational vibrations. Finally, we present a case study for an isolation building with an inerter damper and minimal sensor arrangement under seismic action. The results demonstrate that the unscented Kalman filter, a system identification method, can precisely estimate structural responses and assess damping device performance once a reliable input-output mapping is established.

Eid Abdallah, Ihab El Aghoury, Sherif Mohamed Ibrahim et al.

Circular Hollow Section (CHS) columns are usually used in high-rise buildings. International design specifications usually overestimate the design of the beam-to-CHS column branch plate connections. This paper numerically investigates the behavior and strength of beam-to-CHS column branch plate connections using a finite element (FE) model. The FE model is verified using different experimental results available in previous literature. A parametric study is carried out on specimens subjected to eccentric shear loading on the branch plate with a constant column length and end conditions. The parameters included the CHS column diameter, wall thickness, branch plate length and the effect of load eccentricity. A notable effect of these parameters is observed on the connection capacity. The mode of failure of the studied specimens ranged from chord plastification for small thicknesses followed by chord face punching to excessive column ovalization at the vicinity of the connection followed by either chord punching or tearing out in the weld. For CHS columns with large thicknesses, punching failure in the column face or premature weld failure is observed. Finally, the results of the parametric study indicated a prominent effect of load eccentricity on the in-plane bending capacity. Therefore, a proposed improvement to the current design guidelines for the strength of CHS column branch plate connection subjected to in-plane bending is presented in this paper.

Chuan Lin, Yun Zou, Xiaohe Lai et al.

The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam’s structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement.

Surajit Kumar Paul

In many engineering structural applications, including car body structures, sheet metal formed components are used. A systematic investigation of the impact of forming strain on fatigue life is necessary for an accurate prediction of a formed component's fatigue life. The present work aims to determine the impact of diverse types of tensile pre-straining on high cycle fatigue (HCF), low cycle fatigue (LCF) and notch fatigue performance of automotive grade dual phase steels. In all examined pre-straining conditions, HCF life improves, LCF life deteriorates, and little change in notch fatigue life are observed. The most significant improvement in HCF life and deterioration in LCF life are observed for equi-biaxial and orthogonal tensile pre-straining conditions due to the rotation of maximum shear stress plane and the rotation of the deformation concentrated region around the hard martensite. As the strength improves during pre-straining, there is a corresponding increase in stress concentration around a notch, and as a result, no significant change in notch fatigue life is observed.

Maurits Dijkstra, Punto Bawono, Isabel Houtkamp et al.

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. Some structural properties of proteins that are closely linked to their function may be easier (or much faster) to predict from sequence than the complete tertiary structure; for example, secondary structure, surface accessibility, flexibility, disorder, interface regions or hydrophobic patches. Serving as building blocks for the native protein fold, these structural properties also contain important structural and functional information not apparent from the amino acid sequence. Here, we will first give an introduction into the application of machine learning for structural property prediction, and explain the concepts of cross-validation and benchmarking. Next, we will review various methods that incorporate knowledge of these concepts to predict those structural properties, such as secondary structure, surface accessibility, disorder and flexibility, and aggregation.

Ana – Maria TOMA, Irina IGNATESCU-MANEA, Oana NECULAI

This article describes a fun way to teach AutoCAD, including some of the commands in creating a fun, animated landscape. In this way, the students are able to pay attention to the presentation because the idea catches their eye, the drawing being fun, easy to fallow and giving them interesting ways to express themselves through AutoCAD 3D. The idea is to teach the students some of the commands, linking them together, by creating a story like animation, which takes the viewer from basics to more advance features of AutoCAD, both 2D and 3D.

Alex J. Li, Vikram Sundar, Gevorg Grigoryan et al.

Computational protein design has the potential to deliver novel molecular structures, binders, and catalysts for myriad applications. Recent neural graph-based models that use backbone coordinate-derived features show exceptional performance on native sequence recovery tasks and are promising frameworks for design. A statistical framework for modeling protein sequence landscapes using Tertiary Motifs (TERMs), compact units of recurring structure in proteins, has also demonstrated good performance on protein design tasks. In this work, we investigate the use of TERM-derived data as features in neural protein design frameworks. Our graph-based architecture, TERMinator, incorporates TERM-based and coordinate-based information and outputs a Potts model over sequence space. TERMinator outperforms state-of-the-art models on native sequence recovery tasks, suggesting that utilizing TERM-based and coordinate-based features together is beneficial for protein design.

L. A. Bull, D. Di Francesco, M. Dhada et al.

A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilising an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different sub-groups, representing (i) use-type, (ii) component, or (iii) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet and power prediction in a wind farm. In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when sub-fleets share correlated information at different levels of the hierarchy. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e. parameter). Both case studies demonstrate the wide applicability to practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.

Armin Moin

This doctoral dissertation proposes a novel approach to enhance the development of smart services for the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS). The proposed approach offers abstraction and automation to the software engineering processes, as well as the Data Analytics (DA) and Machine Learning (ML) practices. This is realized in an integrated and seamless manner. We implement and validate the proposed approach by extending an open source modeling tool, called ThingML. ThingML is a domain-specific language and modeling tool with code generation for the IoT/CPS domain. Neither ThingML nor any other IoT/CPS modeling tool supports DA/ML at the modeling level. Therefore, as the primary contribution of the doctoral dissertation, we add the necessary syntax and semantics concerning DA/ML methods and techniques to the modeling language of ThingML. Moreover, we support the APIs of several ML libraries and frameworks for the automated generation of the source code of the target software in Python and Java. Our approach enables platform-independent, as well as platform-specific models. Further, we assist in carrying out semiautomated DA/ML tasks by offering Automated ML (AutoML), in the background (in expert mode), and through model-checking constraints and hints at design-time. Finally, we consider three use case scenarios from the domains of network security, smart energy systems and energy exchange markets.

Meriem ZOUTAT, Mohammed MEKKI, Sidi mohammed ELACHACHI

The present study consists in describing and applying a new approach called N2-SSI Multi-modal to study the effect of higher modes on the nonlinear seismic response of soil structure interaction (SSI) systems. The two essential parameters in performance-based design considered in this study are lateral displacement and inter-story drift.  The proposed method consists in applying N2-SSI method (which takes into account only one vibration mode) to a considerable number of vibration modes. The results found were compared with those obtained by other methods to assess its reliability. We also showed through this approach, that it was not reasonable to take a single seismic behaviour factor for all modes.

Nicolas Anquetil, Anne Etien, Mahugnon H. Houekpetodji et al.

Advanced reverse engineering tools are required to cope with the complexity of software systems and the specific requirements of numerous different tasks (re-architecturing, migration, evolution). Consequently, reverse engineering tools should adapt to a wide range of situations. Yet, because they require a large infrastructure investment, being able to reuse these tools is key. Moose is a reverse engineering environment answering these requirements. While Moose started as a research project 20 years ago, it is also used in industrial projects, exposing itself to all these difficulties. In this paper we present ModMoose, the new version of Moose. ModMoose revolves around a new meta-model, modular and extensible; a new toolset of generic tools (query module, visualization engine, ...); and an open architecture supporting the synchronization and interaction of tools per task. With ModMoose, tool developers can develop specific meta-models by reusing existing elementary concepts, and dedicated reverse engineering tools that can interact with the existing ones.

K. Y. Ng, E. Frisk, M. Krysander

This paper presents a method to enhance fault isolation without adding physical sensors on a turbocharged spark ignited petrol engine system by designing additional residuals from an initial observer-based residuals setup. The best candidates from all potential additional residuals are selected using the concept of sequential residual generation to ensure best fault isolation performance for the least number of additional residuals required. A simulation testbed is used to generate realistic engine data for the design of the additional residuals and the fault isolation performance is verified using structural analysis method.

Yong Liu, Haichao Wang, Keke Yang et al.

A binder plays an important role in lithium-ion batteries (LIBs), especially for the electrode materials which have large volume expansion during charge and discharge. In this work, we designed a cross-linked polymeric binder with an esterification reaction of Sodium Carboxymethyl Cellulose (CMC) and Fumaric Acid (FA), and successfully used it in an Sb₂O₃ anode for LIBs. Compared with conventional binder polyvinylidene fluoride (PVDF) and CMC, the new cross-linked binder improves the electrochemical stability of the Sb₂O₃anode. Specifically, with CMC-FA binder, the battery could deliver ~611.4 mAh g^−1 after 200 cycles under the current density of 0.2 A g^−1, while with PVDF or CMC binder, the battery degraded to 265.1 and 322.3 mAh g^−1, respectively. The improved cycling performance is mainly due to that the cross-linked CMC-FA network could not only efficiently improve the contact between Sb₂O₃ and conductive agent, but can also buffer the large volume charge of the electrode during repeated charge/discharge cycles.