Hasil untuk "Structural engineering (General)"

Jiahong Xiang, Wenxiao He, Xihua Wang et al.

The Rust programming language presents a steep learning curve and significant coding challenges, making the automation of issue resolution essential for its broader adoption. Recently, LLM-powered code agents have shown remarkable success in resolving complex software engineering tasks, yet their application to Rust has been limited by the absence of a large-scale, repository-level benchmark. To bridge this gap, we introduce Rust-SWE-bench, a benchmark comprising 500 real-world, repository-level software engineering tasks from 34 diverse and popular Rust repositories. We then perform a comprehensive study on Rust-SWE-bench with four representative agents and four state-of-the-art LLMs to establish a foundational understanding of their capabilities and limitations in the Rust ecosystem. Our extensive study reveals that while ReAct-style agents are promising, i.e., resolving up to 21.2% of issues, they are limited by two primary challenges: comprehending repository-wide code structure and complying with Rust's strict type and trait semantics. We also find that issue reproduction is rather critical for task resolution. Inspired by these findings, we propose RUSTFORGER, a novel agentic approach that integrates an automated test environment setup with a Rust metaprogramming-driven dynamic tracing strategy to facilitate reliable issue reproduction and dynamic analysis. The evaluation shows that RUSTFORGER using Claude-Sonnet-3.7 significantly outperforms all baselines, resolving 28.6% of tasks on Rust-SWE-bench, i.e., a 34.9% improvement over the strongest baseline, and, in aggregate, uniquely solves 46 tasks that no other agent could solve across all adopted advanced LLMs.

Oguntuyi Samson Dare, Mahlobo Mandlenkosi G.R., Nyembwe Kasongo et al.

Hybrid Additive Manufacturing (HAM) of Al-Si-Mg alloys results in distinct microstructural variations at the built-substrate interface, influencing electrochemical behavior and overall performance. This study investigates the characteristics and corrosion response of as-received built-substrate HAM Al-Si-Mg alloys through microstructural and electrochemical analysis. Thorough microstructural evaluation was carried out using scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS), to determine grain morphology and phase distribution in the built and substrate zones. Open circuit potential (OCP), cyclic potentiodynamic polarization (CPP) and electrochemical impedance spectroscopy (EIS) were employed to assess corrosion resistance and pitting susceptibility in natural seawater. The findings provide insights into the role of interfacial microstructure on corrosion susceptibility, contributing to the optimization of HAM-processed Al-Si-Mg alloys for enhanced performance in engineering applications. The outcome revealed that the substrate area showed a refined microstructure with enhanced corrosion resistance in contrast to the built. Also, this study gives a vital understanding of the corrosion behaviour of each region of HAM Al-Si-Mg alloys, which is important for service reliability and structural integrity in aerospace and marine applications.

Nikita Kondratev, Alexey Shveykin, Kirill Romanov et al.

Additive manufacturing is extensively used for the production of complex-shaped parts from metals and polymers. Because of the influence of various physical factors, the structure of materials processed under different processing conditions may vary considerably, altering their properties. Application of a multi-level crystal plasticity approach helps explicitly describe the structure of materials at different scale levels. It makes possible the evaluation of basic mechanical properties of the samples fabricated by layer-wise laser melting. In this work, a two-level statistical constitutive model is suggested for calculation the main mechanical characteristics (elastic modulus, offset yield stress) of the AISI 316L stainless steel samples produced by selective laser melting. The model explicitly considers the grain structure and texture of metal parts, twinning defects, and residual stresses in the as-build structure of materials. Under predefined loading conditions, the primary mechanism of inelastic deformation is through intra-granular slips of edge dislocations. Thus, the mechanism of material hardening has been described by a modified form of the Hall-Petch law, where the boundaries of grains and original twins are counted as effective barriers to dislocations. All model parameters are properly identified based on own experimental data and the data found in literature. The elastic moduli and yield stresses are calculated at different residual deformations and are found to be in fair agreement with experiments.

Benjamin Neale, Sakdirat Kaewunruen, Dan Li et al.

This study aims to define specific transferable engineering capabilities needed for the implementation (design and practices) of circular economy (CE) within a smart city setting. We conducted a critical literature review of over 100 studies on the core values of CE and smart cities to investigate the knowledge gap in this topic and understand what specific skillset is employed by industry experts that can be harnessed on a wider scale, which can allow for the optimization of CE. There is a lack of research on the skillsets needed to implement a circular economy in any setting, and there are very few studies on circularity practices in a smart city setting. Primary data collection allows us to bridge this knowledge gap, yielding new findings that do not already exist concerning the skillset employed by experts in the field, which can positively impact the smart city settings in which a circular economy is implemented. We conducted a qualitative analysis based on expert interviews of 21 participants who have experience in the circular economy. This information will benefit the industry by informing businesses and councils about the key skillsets and capabilities to look out for when employing people to implement any aspect of circular practices in a smart city setting, with an emphasis on enhancing efficiency, achieving deliverables, and thinking systemically to address complex challenges they may face during the implementation. We also investigated the implementation of CE in smart cities to provide a well-rounded view of the different achievements and challenges faced during the process. This mainly focuses on the work of governance in smart circular cities, a factor that has many important implications and externalities in different sectors. This study describes the methodology adopted to formulate a detailed questionnaire for expert interviews with respect to the skill gap and capabilities necessary for working in the industry, the results of which aid discussions regarding the different challenges faced in CE implementation. Our findings reveal that background knowledge in engineering and sustainability is the most ‘highly critical’ hard skill according to the experts, while communication and stakeholder engagement are the essential soft skills required to ensure the success of a circular economy within smart city settings.

Thiago Barradas, Aline Paes, Vânia de Oliveira Neves

The effective execution of tests for REST APIs remains a considerable challenge for development teams, driven by the inherent complexity of distributed systems, the multitude of possible scenarios, and the limited time available for test design. Exhaustive testing of all input combinations is impractical, often resulting in undetected failures, high manual effort, and limited test coverage. To address these issues, we introduce RestTSLLM, an approach that uses Test Specification Language (TSL) in conjunction with Large Language Models (LLMs) to automate the generation of test cases for REST APIs. The approach targets two core challenges: the creation of test scenarios and the definition of appropriate input data. The proposed solution integrates prompt engineering techniques with an automated pipeline to evaluate various LLMs on their ability to generate tests from OpenAPI specifications. The evaluation focused on metrics such as success rate, test coverage, and mutation score, enabling a systematic comparison of model performance. The results indicate that the best-performing LLMs - Claude 3.5 Sonnet (Anthropic), Deepseek R1 (Deepseek), Qwen 2.5 32b (Alibaba), and Sabia 3 (Maritaca) - consistently produced robust and contextually coherent REST API tests. Among them, Claude 3.5 Sonnet outperformed all other models across every metric, emerging in this study as the most suitable model for this task. These findings highlight the potential of LLMs to automate the generation of tests based on API specifications.

Hao Li, Haoxiang Zhang, Ahmed E. Hassan

The future of software engineering--SE 3.0--is unfolding with the rise of AI teammates: autonomous, goal-driven systems collaborating with human developers. Among these, autonomous coding agents are especially transformative, now actively initiating, reviewing, and evolving code at scale. This paper introduces AIDev, the first large-scale dataset capturing how such agents operate in the wild. Spanning over 456,000 pull requests by five leading agents--OpenAI Codex, Devin, GitHub Copilot, Cursor, and Claude Code--across 61,000 repositories and 47,000 developers, AIDev provides an unprecedented empirical foundation for studying autonomous teammates in software development. Unlike prior work that has largely theorized the rise of AI-native software engineering, AIDev offers structured, open data to support research in benchmarking, agent readiness, optimization, collaboration modeling, and AI governance. The dataset includes rich metadata on PRs, authorship, review timelines, code changes, and integration outcomes--enabling exploration beyond synthetic benchmarks like SWE-bench. For instance, although agents often outperform humans in speed, their PRs are accepted less frequently, revealing a trust and utility gap. Furthermore, while agents accelerate code submission--one developer submitted as many PRs in three days as they had in three years--these are structurally simpler (via code complexity metrics). We envision AIDev as a living resource: extensible, analyzable, and ready for the SE and AI communities. Grounding SE 3.0 in real-world evidence, AIDev enables a new generation of research into AI-native workflows and supports building the next wave of symbiotic human-AI collaboration. The dataset is publicly available at https://github.com/SAILResearch/AI_Teammates_in_SE3. > AI Agent, Agentic AI, Coding Agent, Agentic Coding, Software Engineering Agent

Miguel Goulão, Vasco Amaral, Marjan Mernik

Model-driven engineering (MDE) is believed to have a significant impact in software quality. However, researchers and practitioners may have a hard time locating consolidated evidence on this impact, as the available information is scattered in several different publications. Our goal is to aggregate consolidated findings on quality in MDE, facilitating the work of researchers and practitioners in learning about the coverage and main findings of existing work as well as identifying relatively unexplored niches of research that need further attention. We performed a tertiary study on quality in MDE, in order to gain a better understanding of its most prominent findings and existing challenges, as reported in the literature. We identified 22 systematic literature reviews and mapping studies and the most relevant quality attributes addressed by each of those studies, in the context of MDE. Maintainability is clearly the most often studied and reported quality attribute impacted by MDE. Eighty out of 83 research questions in the selected secondary studies have a structure that is more often associated with mapping existing research than with answering more concrete research questions (e.g., comparing two alternative MDE approaches with respect to their impact on a specific quality attribute). We briefly outline the main contributions of each of the selected literature reviews. In the collected studies, we observed a broad coverage of software product quality, although frequently accompanied by notes on how much more empirical research is needed to further validate existing claims. Relatively, little attention seems to be devoted to the impact of MDE on the quality in use of products developed using MDE.

Fikret Enes Altunok Politecnico di Torino, Italy, Giorgio De Pasquale

The escalating importance of lightweight design in engineering demands innovative strategies to tackle this challenge. Traditionally, the joining of these materials involves rivets, bolts, or adhesives. However, contemporary manufacturing techniques, such as 3D printing, present the potential to fabricate joints without the necessity for additional binding mechanisms. This paper delves into a promising initiative concerning the joining of multimaterial systems, specifically composites and metals. The fabrication of the metal component of the joint through additive manufacturing (AM) enables the manipulation of surface geometry by incorporating patterned anchors. This, in turn, facilitates the direct co-curing of the composite onto the modified metallic surface. The primary objective is to enhance mechanical interlocking without relying on traditional fastening elements or adhesives. The study evaluates various anchor geometries to assess their efficacy in increasing the overall joint strength. This assessment employs the cohesive zone modeling (CZM) method to simulate joint specimens, followed by comparative analyses to quantify the strengths of the joints

Daren Peng, Rhys Jones, Andrew S. M. Ang

This paper mainly studies the fatigue cracks growth of fillet weld specimens in a fashion that is consistent with that used to assess the fatigue performance of complex aerospace structures under operational flight loads. The fatigue test loads were determined using the overall finite element analysis results of the hopper wagon. The actual applied test loads were monitored using strain gauges. The residual stress in the critical region was determined by combining the stress field of the welded specimen obtained by a thermal imager under cyclic loading with the results of the three-dimensional finite element analysis of the specimen. During the fatigue test, a digital camera (with microscope lens) was used in conjunction with infrared measurement technology to obtain the crack growth information. As in prior studies, the three dimensional finite element alternating technique was used to calculate the stress intensity factor in the critical area of the crack in the fillet weld specimen. The Hartman–Schijve crack growth equation was then used, in conjunction with the calculated stress intensity factor solutions, to compute the crack growth history in a fatigue test of a critical welded component in a hopper wagon. The resultant computed crack growth histories are relatively consistent with the test results.

C. Bellini, V. Di Cocco, F. Iacoviello et al.

Three key qualities should define the structures used in the aviation industry: they should be light, rigid, and strong. These goals can be met by selecting lightweight, high-performance materials like titanium alloy or composites, but careful structural design is also crucial to improve mechanical performance. The structures known as isogrids, which consist of a skin reinforced by a lattice frame, offer an effective way to meet the aforementioned specifications. The structural performances of isogrid-stiffened cylinders composed of various materials were compared in the current work. The structures under investigation were composed of titanium alloy, carbon fibre composite material, or a combination of both. A FEM model was proposed and validated by comparison with experimental results obtained from a composite material structure, and then it was used to simulate the behaviour of all the other structures. While there was some variation in the strength of the parts, it was discovered that the stiffness was almost uniform throughout all of the structures that were examined. But when the weight of the various constructions was taken into account, some very intriguing findings emerged: the composite material-only structure proved to be the most effective because it had the highest specific performances

Ana-Maria GRAUR, Carmen MARZA, Georgiana CORSIUC

Buildings that have adaptive shading systems, have the ability to adapt to the intensity of natural light to filter hierarchically, in real time, to prevent visual and thermal discomfort generated by the light inside the spaces, have the major advantage of reducing energy consumption from buildings and CO2 emissions, with a beneficial impact on the environment. The purpose of this work is to contribute to the development of these systems by classifying the concepts of adaptive facade and developing a new system inspired by nature, from biological or botanical systems, capable of responding to environmental change.

S. M. Smith, A. J. Hughes, T. A. Dardeno et al.

Population-based structural health monitoring (PBSHM), aims to share information between members of a population. An offshore wind (OW) farm could be considered as a population of nominally-identical wind-turbine structures. However, benign variations exist among members, such as geometry, sea-bed conditions and temperature differences. These factors could influence structural properties and therefore the dynamic response, making it more difficult to detect structural problems via traditional SHM techniques. This paper explores the use of a hierarchical Bayesian model to infer expected soil stiffness distributions at both population and local levels, as a basis to perform anomaly detection, in the form of scour, for new and existing turbines. To do this, observations of natural frequency will be generated as though they are from a small population of wind turbines. Differences between individual observations will be introduced by postulating distributions over the soil stiffness and measurement noise, as well as reducing soil depth (to represent scour), in the case of anomaly detection.

Andreas Vogelsang

Generative LLMs, such as GPT, have the potential to revolutionize Requirements Engineering (RE) by automating tasks in new ways. This column explores the novelties and introduces the importance of precise prompts for effective interactions. Human evaluation and prompt engineering are essential in leveraging LLM capabilities.

Changhao Li, Haoling Li, Mengqi Xue et al.

Structural pruning has emerged as a promising approach for producing more efficient models. Nevertheless, the community suffers from a lack of standardized benchmarks and metrics, leaving the progress in this area not fully comprehended. To fill this gap, we present the first comprehensive benchmark, termed PruningBench, for structural pruning. PruningBench showcases the following three characteristics: 1) PruningBench employs a unified and consistent framework for evaluating the effectiveness of diverse structural pruning techniques; 2) PruningBench systematically evaluates 16 existing pruning methods, encompassing a wide array of models (e.g., CNNs and ViTs) and tasks (e.g., classification and detection); 3) PruningBench provides easily implementable interfaces to facilitate the implementation of future pruning methods, and enables the subsequent researchers to incorporate their work into our leaderboards. We provide an online pruning platform for customizing pruning tasks and reproducing all results in this paper. Leaderboard results can also be available.

Philipp Rennen, Stefan Gantner, Gido Dielemans et al.

The research project presented here aims to develop a design-informed manufacturing process for complex concrete shell structures in additive manufacturing and thus overcome limitations of traditional construction methods such as formwork- and labor intensity. To achieve this, an effort was made to merge the two technologies of CNC knitted stay-in-place formwork, known as KnitCrete, and robotically applied shotcrete, known as Shotcrete 3D Printing (SC3DP), and thereby reduce their respective limitations. The proposed workflow unites both digital fabrication methods into a seamless process that additionally integrates computational form finding, robotically applied fiber reinforcement, CNC post processing and geometric quality verification to ensure precision and efficiency. As part of a cross-university, research-based teaching format, this concept was implemented in the construction of a full-scale pedestrian bridge, which served as a demonstrator to evaluate the capabilities and limitations of the process. While overcoming some challenges during the process, the successful prove of concept shows a significant leap in digital fabrication of complex concrete geometry, reducing reliance on labor-intensive methods. The results shown in this paper make this fabrication approach a promising starting point for further developments in additive manufacturing in the construction sector.

J. L. Hernández-Pastora

In the standard Einstein's theory the exterior gravitational field of any static and axially symmetric stellar object can be described by means of a single function from which we obtain a metric into a four-dimensional space-time. In this work we present a generalization of those so called Weyl solutions to a space-time-matter metric in a five-dimensional manifold within a non-compactified Kaluza-Klein theory of gravity. The arising field equations reduce to those of vacuum Einstein's gravity when the metric function associated to the fifth dimension is considered to be constant. The calculation of the geodesics allows to identify the existence or not of different behaviours of test particles, in orbits on a constant plane, between the two metrics. In addition, static solutions on the hypersurface orthogonal to the added dimension but with time dependence in the five-dimensional metric are also obtained. The consequences on the variation of the rest mass, if the fifth dimension is identified with it, are studied.

Matthew Tyler, Hengyun Zhou, Leigh S. Martin et al.

We introduce a framework for designing Hamiltonian engineering pulse sequences that systematically accounts for the effects of higher-order contributions to the Floquet-Magnus expansion. Our techniques result in simple, intuitive decoupling rules, despite the higher-order contributions naively involving complicated, non-local-in-time commutators. We illustrate how these rules can be used to efficiently design improved Hamiltonian engineering pulse sequences for a wide variety of tasks, such as dynamical decoupling, quantum sensing, and quantum simulation.

Carlo A. Furia, Richard Torkar, Robert Feldt

There is abundant observational data in the software engineering domain, whereas running large-scale controlled experiments is often practically impossible. Thus, most empirical studies can only report statistical correlations -- instead of potentially more insightful and robust causal relations. To support analyzing purely observational data for causal relations, and to assess any differences between purely predictive and causal models of the same data, this paper discusses some novel techniques based on structural causal models (such as directed acyclic graphs of causal Bayesian networks). Using these techniques, one can rigorously express, and partially validate, causal hypotheses; and then use the causal information to guide the construction of a statistical model that captures genuine causal relations -- such that correlation does imply causation. We apply these ideas to analyzing public data about programmer performance in Code Jam, a large world-wide coding contest organized by Google every year. Specifically, we look at the impact of different programming languages on a participant's performance in the contest. While the overall effect associated with programming languages is weak compared to other variables -- regardless of whether we consider correlational or causal links -- we found considerable differences between a purely associational and a causal analysis of the very same data. The takeaway message is that even an imperfect causal analysis of observational data can help answer the salient research questions more precisely and more robustly than with just purely predictive techniques -- where genuine causal effects may be confounded.

Sean McGuire, Erin Shultz, Bimpe Ayoola et al.

Background: Sustainable software engineering (SSE) means creating software in a way that meets present needs without undermining our collective capacity to meet our future needs. It is typically conceptualized as several intersecting dimensions or ``pillars'' -- environmental, social, economic, technical and individual. However; these pillars are theoretically underdeveloped and require refinement. Objectives: The objective of this paper is to generate a better theory of SSE. Method: First, a scoping review was conducted to understand the state of research on SSE and identify existing models thereof. Next, a meta-synthesis of qualitative research on SSE was conducted to critique and improve the existing models identified. Results: 961 potentially relevant articles were extracted from five article databases. These articles were de-duplicated and then screened independently by two screeners, leaving 243 articles to examine. Of these, 109 were non-empirical, the most common empirical method was systematic review, and no randomized controlled experiments were found. Most papers focus on ecological sustainability (158) and the sustainability of software products (148) rather than processes. A meta-synthesis of 36 qualitative studies produced several key propositions, most notably, that sustainability is stratified (has different meanings at different levels of abstraction) and multisystemic (emerges from interactions among multiple social, technical, and sociotechnical systems). Conclusion: The academic literature on SSE is surprisingly non-empirical. More empirical evaluations of specific sustainability interventions are needed. The sustainability of software development products and processes should be conceptualized as multisystemic and stratified, and assessed accordingly.

Andy Zou, Long Phan, Sarah Chen et al.

In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.