Hasil untuk "Engineering (General). Civil engineering (General)"

Meenakshi Sudarvizhi Seenipeyathevar, Prasath Palaniappan, Vijayakumar Arumugam et al.

ABSTRACT This study deals with an integrated experimental‐machine learning framework for wear estimation in functionally graded composites made from recycled magnesium machining chips, using low‐cost ceramic fibers as reinforcement with the radial Modeling technique. The primary hurdle that is being addressed is the accurate prediction of wear behavior in spatially graded magnesium matrix composites, while simultaneously avoiding extensive experimental testing. Under varying degrees of applied loads (4.4 to 39 N), sliding speeds (0.45 to 4.5 m/s), and sliding distances (500 to 4500 m), the wear performance was experimentally assessed. Results demonstrate a hardness increment of 26.26% in the outer region compared to the inner region, while resistance to wear was enhanced by 19.8% in the outer zone due to the grading of ceramic fibers. A limited experimental dataset consisting of wear measurements from the inner, middle, and outer zones of the composite was utilized in developing and validating four machine‐learning models for wear rate prediction. The tree‐based ensemble methods significantly outperformed deep‐learning strategies, with the LightGBM model providing the best prediction performance across all zones and achieving optimization with a maximum tree depth of 5, 480 leaves, and a feature fraction of 0.05. Moreover, zone‐specific XGBoost models were also developed, employing customized learning rates and minimal loss reduction parameters in order to elevate prediction accuracy. The proposed machine‐learning framework thus provides a pathway for rapid and reliable wear rate estimation for ceramic fiber‐reinforced magnesium composites, significantly lessening experimental burden. Results highlight that recycled magnesium waste, when combined with ceramic reinforcement, can be effectively employed to produce sustainable and economically viable materials with improved wear resistance, particularly for automotive and industrial applications.

Daniel Rodriguez-Cardenas, Xiaochang Li, Marcos Macedo et al.

Large language models for code are advancing fast, yet our ability to evaluate them lags behind. Current benchmarks focus on narrow tasks and single metrics, which hide critical gaps in robustness, interpretability, fairness, efficiency, and real-world usability. They also suffer from inconsistent data engineering practices, limited software engineering context, and widespread contamination issues. To understand these problems and chart a path forward, we combined an in-depth survey of existing benchmarks with insights gathered from a dedicated community workshop. We identified three core barriers to reliable evaluation: the absence of software-engineering-rich datasets, overreliance on ML-centric metrics, and the lack of standardized, reproducible data pipelines. Building on these findings, we introduce BEHELM, a holistic benchmarking infrastructure that unifies software-scenario specification with multi-metric evaluation. BEHELM provides a structured way to assess models across tasks, languages, input and output granularities, and key quality dimensions. Our goal is to reduce the overhead currently required to construct benchmarks while enabling a fair, realistic, and future-proof assessment of LLMs in software engineering.

Paloma Guenes, Rafael Tomaz, Maria Teresa Baldassarre et al.

The Impostor Phenomenon (IP) impacts a significant portion of the Software Engineering workforce, yet it is often viewed primarily through an internal individual lens. In this position paper, we propose framing the prevalence of IP as a form of Human Debt and discuss the relation with the ICSE2026 Pre Survey on the Future of Software Engineering results. Similar to technical debt, which arises when short-term goals are prioritized over long-term structural integrity, Human Debt accumulates due to gaps in psychological safety and inclusive support within socio-technical ecosystems. We observe that this debt is not distributed equally, it weighs heavier on underrepresented engineers and researchers, who face compounded challenges within traditional hierarchical structures and academic environments. We propose cultural refactoring, transparency and active maintenance through allyship, suggesting that leaders and institutions must address the environmental factors that exacerbate these feelings, ensuring a sustainable ecosystem for all professionals.

Longfei Cui, Xinyu Niu, Haizhong Qian et al.

The extraction of shape features from vector elements is essential in cartography and geographic information science, supporting a range of intelligent processing tasks. Traditional methods rely on different machine learning algorithms tailored to specific types of line and polygon elements, limiting their general applicability. This study introduces a novel approach called “Pre-Trained Shape Feature Representations from Transformers (PSRT)”, which utilizes transformer encoders designed with three self-supervised pre-training tasks: coordinate masking prediction, coordinate offset correction, and coordinate sequence rearrangement. This approach enables the extraction of general shape features applicable to both line and polygon elements, generating high-dimensional embedded feature vectors. These vectors facilitate downstream tasks like shape classification, pattern recognition, and cartographic generalization. Our experimental results show that PSRT can extract vector shape features effectively without needing labeled samples and is adaptable to various types of vector features. Compared to the methods without pre-training, PSRT enhances training efficiency by over five times and improves accuracy by 5–10% in tasks such as line element matching and polygon shape classification. This innovative approach offers a more unified, efficient solution for processing vector shape data across different applications.

S. Kim, Y. Lee, Y. Lee et al.

This study presents a Historic Building Information Modeling (HBIM)-based approach for the digital restoration and documentation of lost wooden architectural heritage. The approach was applied to Building 1-2 of Hyeumwonji, the site of a temporary Goryeo Dynasty palace in Paju, South Korea. To reconstruct this lost structure, we combined historical and archaeological analyses to estimate the original design and generated blueprints that guided the HBIM-based 3D model of the building. We collected LiDAR point cloud data from the site, aligned them with the HBIM model, and visualized the integrated result using Unreal Engine 5. The outcome was a comprehensive virtual restoration comprising 13,814 individual building elements. This case study demonstrates that, even with minimal physical remains, wooden heritage sites can be digitally restored by leveraging HBIM and historical reasoning. It also highlights the strengths of HBIM in version tracking, incorporation of historical updates, and systematic documentation throughout the restoration process. Compared to traditional 2D CAD-based restoration methods, the HBIM approach offers significant advantages in terms of updatability, data integration, and long-term preservation of restoration data. Overall, the project illustrates how combining rigorous historical analysis with advanced digital modeling can revive lost heritage architecture in virtual form, providing a rich resource for research and conservation.

Mauro Marcelino, Marcos Alves, Bianca Trinkenreich et al.

[Context] An evidence briefing is a concise and objective transfer medium that can present the main findings of a study to software engineers in the industry. Although practitioners and researchers have deemed Evidence Briefings useful, their production requires manual labor, which may be a significant challenge to their broad adoption. [Goal] The goal of this registered report is to describe an experimental protocol for evaluating LLM-generated evidence briefings for secondary studies in terms of content fidelity, ease of understanding, and usefulness, as perceived by researchers and practitioners, compared to human-made briefings. [Method] We developed an RAG-based LLM tool to generate evidence briefings. We used the tool to automatically generate two evidence briefings that had been manually generated in previous research efforts. We designed a controlled experiment to evaluate how the LLM-generated briefings compare to the human-made ones regarding perceived content fidelity, ease of understanding, and usefulness. [Results] To be reported after the experimental trials. [Conclusion] Depending on the experiment results.

Lekshmi Murali Rani

The study of behavioral and social dimensions of software engineering (SE) tasks characterizes behavioral software engineering (BSE);however, the increasing significance of human-AI collaboration (HAIC) brings new directions in BSE by presenting new challenges and opportunities. This PhD research focuses on decision-making (DM) for SE tasks and collaboration within human-AI teams, aiming to promote responsible software engineering through a cognitive partnership between humans and AI. The goal of the research is to identify the challenges and nuances in HAIC from a cognitive perspective, design and optimize collaboration/partnership (human-AI team) that enhance collective intelligence and promote better, responsible DM in SE through human-centered approaches. The research addresses HAIC and its impact on individual, team, and organizational level aspects of BSE.

Marc Bruni, Fabio Gabrielli, Mohammad Ghafari et al.

Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code security. Our benchmark leverages two peer-reviewed prompt datasets and employs static scanners to evaluate code security at scale. We tested multiple prompt engineering techniques on GPT-3.5-turbo, GPT-4o, and GPT-4o-mini. Our results show that for GPT-4o and GPT-4o-mini, a security-focused prompt prefix can reduce the occurrence of security vulnerabilities by up to 56%. Additionally, all tested models demonstrated the ability to detect and repair between 41.9% and 68.7% of vulnerabilities in previously generated code when using iterative prompting techniques. Finally, we introduce a "prompt agent" that demonstrates how the most effective techniques can be applied in real-world development workflows.

Marvin Wyrich, Lloyd Montgomery

A well-rounded software engineer is often defined by technical prowess and the ability to deliver on complex projects. However, the narrative around the ideal Software Engineering (SE) candidate is evolving, suggesting that there is more to the story. This article explores the non-technical aspects emphasized in SE job postings, revealing the sociotechnical and organizational expectations of employers. Our Thematic Analysis of 100 job postings shows that employers seek candidates who align with their sense of purpose, fit within company culture, pursue personal and career growth, and excel in interpersonal interactions. This study contributes to ongoing discussions in the SE community about the evolving role and workplace context of software engineers beyond technical skills. By highlighting these expectations, we provide relevant insights for researchers, educators, practitioners, and recruiters. Additionally, our analysis offers a valuable snapshot of SE job postings in 2023, providing a scientific record of prevailing trends and expectations.

Jian Liu, Fangyu Liu, Linbing Wang

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, which involves selecting aggregate gradation and binder content, is time-consuming and labor-intensive. To accelerate the traditional mix design procedure, this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning (ML) and a meta-heuristic algorithm. Specifically, ML approaches were employed to model the relationship between volumetric properties (mixture bulk specific gravity (Gmb) and air void (VV)) and both mixture component properties and mixture proportion, based on a dataset collected from literature with 660 mixture designs. Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization (MOGWO) algorithm, an automatic asphalt mix design was proposed to pursue three goals, including VV, cost, and CO2 emission. The results indicated that least squares support vector regression (LSSVR) and eXtreme gradient boosting (XGBoost) achieved the highest prediction accuracies (correlation coefficient: 0.92 for VV and 0.96 for Gmb). The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs. cost vs. CO2 emission. Compared to the traditional laboratory design, the optimal mixture with VV of 4% achieves a cost saving of 2.46% and a reduction of 4.03% in carbon emission. The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.

D. S. Spivak, S. V. Kliuchnyk

Purpose. The paper aims to highlight and substantiate the need to find rational design schemes for lattice tube concrete bridges with a ride on top based on the analysis of recent research and regulatory documents. Methodology. The current scientific research is analyzed to determine the current state of development of pipe concrete lattice structures. Methods for improving structures are presented. Combinations of filling the grating elements with concrete, variants of cross-sections of the grating elements, their advantages and disadvantages are analyzed. The state of building codes of Ukraine and other countries is considered in order to determine possible options for the design of pipe concrete bridge structures. Due to the lack of detailed research on this issue, the feasibility of implementing optimization studies for these structures and the steps necessary for this are determined. Findings. The optimization of pipe-concrete bridge structures is a relevant area of research, but it requires a multicomponent approach and the use of modern computer facilities. The method of linear optimization is proposed and its general steps for finding economic models are determined. It was found that the base of Ukrainian SCSs in the field of pipe and concrete structures is limited, but can be expanded by using European standards and other international regulations. Originality. The necessity of global development and improvement of pipe concrete gratings of bridge spans is highlighted. Attention is focused on the advantages of this area, which contributes to decision-making at the stage of selecting the type of bridge and detailed design of pipe-concrete lattice bridges. A methodology for finding the optimal grids is proposed, which can integrate existing methods of structural improvement and the requirements of regulatory documents. Practical value. The results of the study can be used to improve the design of pipe concrete bridges at the design stage. Optimization of gratings can help to increase the efficiency of construction and reliability of this type of bridge structure.

Roberto Castro-Muñoz

Today, chemistry and nanotechnology cover molecular separations in liquid and gas states by aiding in the design of new nano-sized materials. In this regard, the synthesis and application of two-dimensional (2D) nanomaterials are current fields of research in which structurally defined 2D materials are being used in membrane separation either in self-standing membranes or composites with polymer phases. For instance, pervaporation (PV), as a highly selective technology for liquid separation, benefits from using 2D materials to selectively transport water or other solvent molecules. Therefore, this review paper offers an interesting update in revising the ongoing progress of PV membranes using 2D materials in several applications, including solvent purification (the removal of water from organic systems), organics removal (the removal of organic molecules diluted in water systems), and desalination (selective water transport from seawater). In general, recent reports from the past 3 years have been discussed and analyzed. Attention has been devoted to the proposed strategies and fabrication of membranes for the inclusion of 2D materials into polymer phases. Finally, the future trends and current research gaps are declared for the scientists in the field.

I. Cárdenas-León, R. Morales-Ortega, M. Koeva et al.

According to the KNMI Klimaatsignaal’21, the average surface temperature in The Netherlands has increased by 2.3°C between 1901 and 2020. Moreover, The Netherlands is also experiencing more frequent and intense heatwaves. Urban development significantly impacts the environmental conditions of a city, influencing thermal comfort and human well-being. To deal with these problems, municipalities across the country have been tasked to find ways to measure, understand, and find solutions to the increasing temperatures, specifically in urban areas. Because of this, several contrasting urban heat maps have been produced using different metrics and methods by different agencies. Koopman et al. presented a methodology for a standardized urban heat map at a 1-m spatial resolution to unify the stress tests by selecting the Physical Equivalent Temperature (PET) as a metric for heat stress. The PET is a key indicator in bio-meteorology, quantifying the combined effects of various environmental factors on human thermal perception. Despite its utility, widespread adoption of PET-based assessments by municipalities remains limited. To address this gap, this paper presents the development of a Digital Twin framework using PET analysis, enabling a collaborative, nondestructive, and cost-effective assessment of urban interventions’ impact on thermal conditions. Leveraging geoprocessing workflows and geospatial data, our framework allows for real-time PET calculations and scenario testing, facilitating informed decision-making by urban planners. The framework was tested and applied for Enschede, Netherlands, demonstrating its efficacy in visualizing current conditions, projecting future scenarios, and evaluating intervention strategies. Feedback from urban planners highlighted the tool’s usability and potential for enhancing community engagement in urban planning processes.

Noorhayati Idros, Lariyah Mohd Sidek, Nur Anis Aishah M. Rahim et al.

The aim of this study is to evaluate the current water quality status of one of the urban rivers in Malaysia, called Sungai Air Hitam. The river's water supply is not only unsuitable for the inhabitants but also hazardous to the aquatic species that depend on it. In order to simulate the water quality formulation of the river, the Model for Urban Stormwater Improvement Conceptualization (MUSIC) was used. The effects of various best management practices (BMPs) components have been examined to improve the river's water quality. This study also investigated different scenarios of the expected future changes in the land cover and the quality of the river. As the proportion of impervious surfaces increases, the urban hydrology cycle can be significantly altered, resulting in an increase in volumes and peak flows, and a decrease in storage, infiltration, and interception. The MUSIC results have shown significant reductions in biochemical oxygen demand (BOD), total suspended solids (TSS), total phosphorus (TP), and total nitrogen (TN) after introducing BMPs. It was also noticed that the prediction of pollutants falls within the acceptable range set by the Urban Stormwater Management Manual for Malaysia (MSMA) 2nd edition. For the land cover, it was found that the total reduction of BOD, TSS, TP, and TN for existing land use is 92.5 %, 94.5 %, 90.7 % and 91.9 %. Meanwhile, the total reduction in future land use is 81.6 % for BOD, 86.2 % for TSS, 80.9 % for TP and 80.8 % for TN. From the simulation results, it was observed that the application of BMPs has successfully reduced the observed mean BOD concentration from 92.38 mg/L (Class V) to 6.93 mg/L (Class IV) of the national water quality standards, NWQS, water quality index. As a result, the water quality index of the overall catchment has improved from Class IV to Class III (WQ1, WQ3, and WQ4) and from Class V to IV (WQ2) with the application of the BMPs. This assessment aims to raise awareness within the Sungai Air Hitam community regarding the importance of preserving river cleanliness and understanding the long-term environmental impact of water quality. These findings underscore the importance of an integrated system in managing urban water systems, which can offer valuable insight to the decision-makers.

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research software is often developed ad hoc without following stringent processes. With this paper, we want to characterize research software as a new application domain that needs attention from the Requirements Engineering community. We conducted an exploratory study based on 8 interviews with 12 researchers who develop software. We describe how researchers elicit, document, and analyze requirements for research software and what processes they follow. From this, we derive specific challenges and describe a vision of Requirements Engineering for research software.

Johan Cederbladh, Antonio Cicchetti

In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.

Jukka Ruohonen, Kalle Hjerppe

Participatory citizen platforms are innovative solutions to digitally better engage citizens in policy-making and deliberative democracy in general. Although these platforms have been used also in an engineering context, thus far, there is no existing work for connecting the platforms to requirements engineering. The present paper fills this notable gap. In addition to discussing the platforms in conjunction with requirements engineering, the paper elaborates potential advantages and disadvantages, thus paving the way for a future pilot study in a software engineering context. With these engineering tenets, the paper also contributes to the research of large socio-technical software systems in a public sector context, including their implementation and governance.

Enrique González-Plaza, David García, Jesús-Ignacio Prieto

Stirling engines are currently of interest due to their adaptability to a wide range of energy sources. Since simple tools are needed to guide the sizing of prototypes in preliminary studies, this paper proposes two groups of simple models to estimate the maximum power in Stirling engines with a kinematic drive mechanism. The models are based on regression or ANN techniques, using data from 34 engines over a wide range of operating conditions. To facilitate the generalisation and interpretation of results, all models are expressed by dimensionless variables. The first group models use three input variables and 23 data points for correlation construction or training purposes, while another 66 data points are used for testing. Models in the second group use eight inputs and 18 data points for correlation construction or training, while another 36 data points are used for testing. The three-input models provide estimations of the maximum brake power with an acceptable accuracy for feasibility studies. Using eight-input models, the predictions of the maximum indicated power are very accurate, while those of the maximum brake power are less accurate, but acceptable for the preliminary design stage. In general, the best results are achieved with ANN models, although they only employ one hidden layer.

Ozhen Taha Ali Ways, Hekmat M. Ibrahim

Rainwater harvesting could be a possible solution to decrease the consequences of water scarcity and energy deficiency in Iraq and the Kurdistan Region of Iraq (KRI). This study aims to calculate the water and energy (electricity) saved by rainwater harvesting for rooftops and green areas in Sulaimani city, KR, Iraq. Various data were acquired from different formal entities in Sulaimani city. Moreover, Google Earth and ArcMap 10.4 software were used for digitizing and calculating the total rooftop and green areas. The results showed that for the used runoff coefficients (0.8 and 0.95), the harvested rainwater volumes were 2901563 and 12197131 m³ during the study period (2005 – 2006) and (2019-2020). Moreover, by comparing the study area's rainwater harvesting volume and water production, the water-saving percentage was 8.21 to 22.68%. Furthermore, the energy-saving percentage recorded was from 7.70 to 22.5% by implementing rooftop rainwater harvesting. On the other hand, using average daily rainfall data for the year (2005-2020), the total water-saving percentage and the total energy-saving rate for both runoff coefficients were very close. Water and energy-saving results were calculated using year-by-year rainfall data, taking more time and effort for its computation. Moreover, the water-saving percentage for the selected green area was not encouraging, and the results were between 0.73 and 11.15%. Additionally, the storage size for three typical buildings was calculated, and the results show the average storage size required for rainwater harvesting using daily rainfall data was 11.2 to 14.68 m³ (house), 291.42 to 422.33 m³ (school), and 10.5 to 11.41 m³ (hotel) for runoff coefficients of 0.8 and 0.95, respectively.  

Mubarak Yakubova, Olga Manankova, Assel Mukasheva et al.

The research problem described in this article is related to the security of an IP network that is set up between two cities using hosting. The network is used for transmitting telephone traffic between servers located in Germany and the Netherlands. The concern is that with the increasing adoption of IP telephony worldwide, the network might be vulnerable to hacking and unauthorized access, posing a threat to the privacy and security of the transmitted information. This article proposes a solution to address the security concerns of the IP network. After conducting an experiment and establishing a connection between the two servers using the WireShark sniffer, a dump of real traffic between the servers was obtained. Upon analysis, a vulnerability in the network was identified, which could potentially be exploited by malicious actors. To enhance the security of the network, this article suggests the implementation of the Transport Layer Security (TLS) protocol. TLS is a cryptographic protocol that provides secure communication over a computer network, ensuring data confidentiality and integrity during transmission. Integrating TLS into the network infrastructure, will protect the telephone traffic and prevent unauthorized access and eavesdropping.