Hasil untuk "Transportation engineering"

Clayton José Gomes Silva, José Afonso Pereira Vitório, Arnaldo Manoel Pereira Carneiro et al.

Abstract Highway bridges are key elements for the economic and social development of any country, especially Brazil, which has the most used highway mode for transporting people and freight compared to the rail and waterway systems. This importance requires constant attention to the deterioration process to which such works are prone over time. It also requires the development of research on the assessment of performance, service life, and deterioration models, such as those being developed in several countries, to identify the conditions of stability and functionality, also contributing to decision-making focusing on planning necessary regular repairs and corrections. In this regard, this study intends to contribute to the evolution of this area of knowledge by presenting the results of the estimated service life of the superstructures of 98 bridges and viaducts of federal highways in the State of Pernambuco by applying the Factor Method and Survival Analysis techniques. The data of the set of works analyzed were obtained from the database of the National Department of Transportation Infrastructure (DNIT), in groups of the type of material of the superstructure (reinforced concrete and prestressed concrete), considering the information from the periodical inspections of such works in 2019. Among the results from the application of both methodologies, it is worth mentioning that the superstructures built using reinforced concrete have a higher level of deterioration than those using prestressed concrete. Lastly, the information obtained showed that both methodologies adopted in this study are reliable and consistent, since they produced appropriate results based on concepts validated in the literature for the intended purpose, and therefore can be used in estimating service life of the different typologies of Special Engineering Structures (SES) of Brazilian highways, even when there are limitations of information recorded in a database and the lack of regular inspection planning.

Changyu Du, Zihan Deng, Stavros Nousias et al.

The adoption of Building Information Modeling (BIM) and model-based design within the Architecture, Engineering, and Construction (AEC) industry has been hindered by the perception that using BIM authoring tools demands more effort than conventional 2D drafting. To enhance design efficiency, this paper proposes a BIM command recommendation framework that predicts the optimal next actions in real-time based on users' historical interactions. We propose a comprehensive filtering and enhancement method for large-scale raw BIM log data and introduce a novel command recommendation model. Our model builds upon the state-of-the-art Transformer backbones originally developed for large language models (LLMs), incorporating a custom feature fusion module, dedicated loss function, and targeted learning strategy. In a case study, the proposed method is applied to over 32 billion rows of real-world log data collected globally from the BIM authoring software Vectorworks. Experimental results demonstrate that our method can learn universal and generalizable modeling patterns from anonymous user interaction sequences across different countries, disciplines, and projects. When generating recommendations for the next command, our approach achieves a Recall@10 of approximately 84%. The code is available at: https://github.com/dcy0577/BIM-Command-Recommendation.git

Stuart M. Allen, Neil Chue Hong, Stephan Druskat et al.

Research Software Engineering (RSEng) is a key success factor in producing high-quality research software, which in turn enables and improves research outcomes. However, as a principal investigator or leader of a research group you may not know what RSEng is, where to get started with it, or how to use it to maximize its benefit for your research. RSEng also often comes with technical complexity, and therefore reduced accessibility to some researchers. The ten simple rules presented in this paper aim to improve the accessibility of RSEng, and provide practical and actionable advice to PIs and leaders for integrating RSEng into their research group. By following these rules, readers can improve the quality, reproducibility, and trustworthiness of their research software, ultimately leading to better, more reproducible and more trustworthy research outcomes.

Tor Sporsem, Rasmus Ulfsnes

This short paper explores how a maritime company develops and integrates large-language models (LLM). Specifically by looking at the requirements engineering for Retrieval Augmented Generation (RAG) systems in expert settings. Through a case study at a maritime service provider, we demonstrate how data scientists face a fundamental tension between user expectations of AI perfection and the correctness of the generated outputs. Our findings reveal that data scientists must identify context-specific "retrieval requirements" through iterative experimentation together with users because they are the ones who can determine correctness. We present an empirical process model describing how data scientists practically elicited these "retrieval requirements" and managed system limitations. This work advances software engineering knowledge by providing insights into the specialized requirements engineering processes for implementing RAG systems in complex domain-specific applications.

Ethem Utku Aktas, Burak Tuzlutas, Burak Yesiltas

Chaos Engineering is a discipline which enhances software resilience by introducing faults to observe and improve system behavior intentionally. This paper presents a design proposal for a customized Chaos Engineering framework tailored for Softtech, a leading software development company serving the financial sector. It outlines foundational concepts and activities for introducing Chaos Engineering within Softtech, while considering financial sector regulations. Building on these principles, the framework aims to be iterative and scalable, enabling development teams to progressively improve their practices. The study addresses two primary questions: how Softtech's unique infrastructure, business priorities, and organizational context shape the customization of its Chaos Engineering framework and what key activities and components are necessary for creating an effective framework tailored to Softtech's needs.

Benjamin Hope, Jayden Bracey, Sahar Choukir et al.

Large language models (LLMs) such as OpenAI's ChatGPT hold potential for automating engineering analysis, yet their reliability in solving multi-step statics problems remains uncertain. This study evaluates the performance of ChatGPT-4o and ChatGPT-o1-preview on foundational statics tasks, from simple calculations of Newton's second law of motion to beam and truss analyses and compares their results to first-year engineering students on a typical statics exam. To enhance accuracy, we developed a Custom GPT, embedding refined prompts directly into its instructions. This optimized model achieved an 82% score, surpassing the 75% student average, demonstrating the impact of tailored guidance. Despite these improvements, LLMs continued to exhibit errors in nuanced or open-ended problems, such as misidentifying tension and compression in truss members. These findings highlight both the promise and current limitations of AI in structural analysis, emphasizing the need for improved reasoning, multimodal capabilities, and targeted training data for future AI-driven automation in civil and mechanical engineering.

Jian Zhang, Yanbo Dai, Xiqing Zhang et al.

The paper primarily focuses on the control strategy of an electric dump truck equipped with an M100 methanol range extender. In response to the significant adverse impact of the constant power control strategy on the lifespan of power batteries and the large rotational speed fluctuations of range extenders under the power-following control strategy, a constant-speed and variable-torque range extender control strategy based on the rule-based control strategy is proposed. This strategy enables power following within the range of 70 kW to 130 kW and fixed-point operation at 50 kW and 150 kW. Through co-simulation using AVL Cruise and MATLAB R2022b/Simulink, the results indicate that under the China Heavy-duty Commercial Vehicle Test Cycle-Dynamic (CHTC-D), with an average vehicle speed of 23.19 km/h, the constant-speed and variable-torque range extender control strategy achieves a higher methanol saving rate compared to both the constant power control strategy and the power-following control strategy, thereby demonstrating better fuel economy. The methanol consumption per 100 km for the dump truck using the constant power control strategy, the power-following control strategy, and the constant-speed and variable-torque control strategy are 62.89 L, 64.49 L, and 62.53 L, respectively. Compared with the same type of diesel range-extended electric dump truck, its fuel usage cost has a significant advantage.

LI Hang, ZHOU Xun, LUO Jiangguo et al.

[Objective] Subway carriage fire accidents often cause heavy loss of life and property, while the early detection of subway carriage fire can effectively ensure passenger′s lives. It is necessary to conduct research on this issue in different seasons under the environment of supply-return air. [Method] A full-scale subway carriage numerical simulation model and an HVAC (heating, ventilation and air conditioning) system including an air supply system, an exhaust system, and a return air system is established. Referring to three scenarios of natural ventilation, normal operation ventilation in different seasons, and carriage fires, the influence of HVAC system supply-return air in different seasons on the subway carriage airflow field and the fire early detection is analyzed. [Result & Conclusion] The characteristics of the subway carriage internal airflow field vary significantly under different seasonal operational ventilation. In the winter wind mode, heating is supplied to the carriages, resulting in stratified circulation of air with different temperatures between the upper and lower layers of the carriage during the stable period. Conversely, in the summer wind mode, cooling is provided to the interior of the carriages, resulting in a relatively uniform airflow pattern during the stable period compared to the winter wind mode. During a fire incident, smoke generated in the summer wind mode exhibits a faster smoke layer descent rate, while in the winter wind mode, the smoke spreads in a faster speed towards the sides of the carriages. As seen in the established model, the summer wind mode requires a shorter fire response time compared to the winter wind mode.

Asif Ali, Mireya Fernandez Chimeno, Marco A. Azpurua

Electromagnetic interference has the potential to affect the functionality and performance of radio communication links. This work investigates the impact electromagnetic disturbances have on such wireless links, taking quadrature phase-shift keying (QPSK) as a representative example. The evaluation of the effects of interference on a QPSK radio link becomes relevant as it is used by the IEEE 802.11b protocol. Through simulations, we study a QPSK link subjected to narrowband and broadband disturbances. The bit error rate (BER) and the error vector magnitude (EVM) are our quality assessment metrics for evaluating the system. Two specific scenarios are analyzed: continuous wave and chirp interferences as in-band and out-band conditions. The results indicate that the applied electromagnetic interference can degrade the signal quality, resulting in high BER. Furthermore, it is observed that chirp interference largely affects the radio links more than continuous wave disturbances. The aforementioned findings support and reinforce the applicability of the analysis methodology in real-life scenarios like those characterizing healthcare settings.

Muhammad Zohaib

The Continuous software engineering is a collaborative software development environment which offers the continues development and deployment of quality software project within short time. The Continuous software engineering practices are not yet mature enough, and the software organizations hesitate to adopt it. This study aims: (1) to explore the Continuous software engineering challenges by conducting systematic literature review (SLR) and to get the insight of industry experts via questionnaire survey study; (2) to prioritize the investigated challenges using fuzzy analytical hierarchy process (FAHP). The study findings provides the set of critical challenges faced by the software organizations while adopting Continuous software engineering and a prioritization based taxonomy of the Continuous software engineering challenges. The application of FAHP is novel in this research area as it assists in addressing the vagueness of practitioners concerning the influencing factors of Continuous software engineering. We believe that the finding of this study will serve as a body of knowledge for real world practitioners and researchers to revise and develop the new strategies for the successful implementation of Continuous software engineering practices in the software industry.

Saara Tenhunen, Tomi Männistö, Matti Luukkainen et al.

Tertiary education institutions aim to prepare their computer science and software engineering students for working life. While much of the technical principles are covered in lower-level courses, team-based capstone projects are a common way to provide students with hands-on experience and teach soft skills. This paper explores the characteristics of software engineering capstone courses presented in the literature. The goal of this work is to understand the pros and cons of different approaches by synthesising the various aspects of software engineering capstone courses and related experiences. In a systematic literature review for 2007-2022, we identified 127 primary studies. These studies were analysed based on their presented course characteristics and the reported course outcomes. The characteristics were synthesised into a taxonomy consisting of duration, team sizes, client and project sources, project implementation, and student assessment. We found out that capstone courses generally last one semester and divide students into groups of 4-5 where they work on a project for a client. For a slight majority of courses, the clients are external to the course staff and students are often expected to produce a proof-of-concept level software product as the main end deliverable. The courses also offer versatile assessments for students throughout the project. This paper provides researchers and educators with a classification of characteristics of software engineering capstone courses based on previous research. We further synthesise insights on the reported outcomes of capstone courses. Our review study aims to help educators to identify various ways of organising capstones and effectively plan and deliver their own capstone courses. The characterisation also helps researchers to conduct further studies on software engineering capstones.

Eriks Klotins, Michael Unterkalmsteiner, Tony Gorschek

Context: Start-up companies have become an important supplier of innovation and software-intensive products. The flexibility and reactiveness of start-ups enables fast development and launch of innovative products. However, a majority of software start-up companies fail before achieving any success. Among other factors, poor software engineering could be a significant contributor to the challenges experienced by start-ups. However, the state-of-practice of software engineering in start-ups, as well as the utilization of state-of-the-art is largely an unexplored area. Objective: In this study we investigate how software engineering is applied in start-up context with a focus to identify key knowledge areas and opportunities for further research. Method: We perform a multi-vocal exploratory study of 88 start-up experience reports. We develop a custom taxonomy to categorize the reported software engineering practices and their interrelation with business aspects, and apply qualitative data analysis to explore influences and dependencies between the knowledge areas. Results: We identify the most frequently reported software engineering (requirements engineering, software design and quality) and business aspect (vision and strategy development) knowledge areas, and illustrate their relationships. We also present a summary of how relevant software engineering knowledge areas are implemented in start-ups and identify potentially useful practices for adoption in start-ups. Conclusions: The results enable a more focused research on engineering practices in start-ups. We conclude that most engineering challenges in start-ups stem from inadequacies in requirements engineering. Many promising practices to address specific engineering challenges exists, however more research on adaptation of established practices, and validation of new start-up specific practices is needed.

Zhaoping Tang, Wenda Li, Shengyu Zhou et al.

This paper discusses the optimization of emergency resource scheduling for major railway emergencies under multiple uncertainties while considering the uncertainties in demand, reserve, and transportation costs of resources. We introduce a novel approach that integrates stochastic mathematical programming, interval parameter programming, and fuzzy mathematical programming to study uncertain parameter interactions and coupling. A two-stage interval fuzzy credibility-constrained model is established and solved using an interval interactive algorithm. Finally, through a case study on China Railway Nanchang Group Co., Ltd., the novelty and effectiveness of the proposed method for optimizing emergency resource scheduling strategies under multiple uncertainties are demonstrated.

Siyuan Zhang, Liran Liu, Qingxiang Meng et al.

COVID-19 has undergone multiple mutations, with the Omicron variant proving to be highly contagious and rapidly spreading across many countries. The United States was severely hit by the Omicron variant. However, it was still unclear how Omicron transferred across the United States. Here, we collected daily COVID-19 cases and deaths in each county from 1 December 2021 to 28 February 2022 as the Omicron wave. We adopted space-time scan statistics, the Hoover index, and trajectories of the epicenter to quantify spatiotemporal patterns of the Omicron wave of COVID-19. The results showed that the highest and earliest cluster was located in the Northeast. The Hoover index for both cases and deaths exhibited phases of rapid decline, slow decline, and relative stability, indicating a rapid spread of the Omicron wave across the country. The Hoover index for deaths was consistently higher than that for cases. The epicenter of cases and deaths shifted from the west to the east, then southwest. Nevertheless, cases were more widespread than deaths, with a lag in mortality data. This study uncovers the spatiotemporal patterns of Omicron transmission in the United States, and its underlying mechanisms deserve further exploration.

Sy Hung Mai, Huy-Khanh Dang, Van Thuan Nguyen et al.

This work comprehensively investigates the realistic ultimate resistance of steel frame structures based on the probability study model of input random variables due to uncertainty of material properties and geometric parameters. Four independent parameters and thirteen of their dependent components vary randomly in a variational range and seven scenarios of random combination are investigated by the probabilistic modeling, which incorporates the advanced inelastic analysis with direct Monte Carlo simulation. The analysis results for a typical example shows that the stochastic analysis result of steel frame's realistic ultimate resistance is lower than its deterministic analysis result, indicating that a stochastic analysis is needed to comprehensively qualify the safety of the structure. Moreover, a spectrum response, which is produced based on stochastic analysis, provides a better scientific solution.

Julian Frattini, Davide Fucci, Daniel Mendez et al.

Context: Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored. Objective: We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners' understanding and managing of requirements engineering debt (RED). Method: We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory. Results: We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results. Conclusions: The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

Hongyu Li, Hansi Liu, Hongsheng Lu et al.

Connected vehicles can benefit from sharing and merging their observations to develop a more complete understanding of the traffic scene and track traffic participants behind obstructions. Although vehicle-to-vehicle(V2V) communications provide a channel for point cloud data sharing, it is challenging to align point clouds from two vehicles with state-of-the-art techniques due to localization errors, visual obstructions, and differences in perspective. Therefore, we propose a two-phase point cloud registration mechanism to fuse point clouds which focuses on key objects in the scene where the point clouds are most similar and infer the transformation from those. Our system first identifies co-visible objects between vehicle views based on hyper-graph matching using multiple similarity metrics, and then refines the overlap region between co-visible objects across the views for point cloud registration. The system is evaluated based on both experimental and simulation data, which shows tremendous performance improvement when combing with state-of-art baselines.

Jozef Kubás, Michal Ballay, Jozef Ristvej et al.

The article presents an assessment of the use of alternative fuels in Europe with an emphasis on electromobility. In this regard, the impact of political intentions, ambitions and goals are analyzed, in relation to the current situation. Based on the results of empirical measurements, the efficiency of implementation of a European publicly accessible infrastructure for charging electric vehicles was determined. Using the method, the efficiency of electromobility in all the countries of the European Union was investigated. The resulting parameters are comparable in the context of the objectives of the Green Deal and the expected impact of electric cars. Using the results of the DEA model, one can point out the efficiency of countries and suggest ways to improve it.

Luka Vukić, Giambattista Guidi, Tanja Poletan Jugović et al.

Following the sustainable transport policy, environmental criteria are becoming a competitive factor within the maritime shipping industry. The use of greener fuels in internal combustion engines, including electric drive, is a measure that can reduce external costs of transport. Alternative fuels in maritime transport, benefits, and potential attainable savings have been examined on the Kamenari–Lepatane ro-ro ferry route in the Bay of Kotor located in Montenegro. The results indicate higher total fuel cost savings by switching to LNG compared with electric power. However, the external costs of the latter are considerably lower, especially using renewable energy sources rather than fossil ones in the production process. The results obtained, relative to the magnitude and assumed complete internalization of external costs, justify the incentive to use the renewable sources as energy providers on the examined ro-ro ferry route. Environmental criteria should play a decisive role in assessing the overall benefit value, under the current trends and regulations of emissions reduction in maritime transport.

Shichun Yang, Zhengjie Zhang, Rui Cao et al.

Summary: An intelligent battery management system is a crucial enabler for energy storage systems with high power output, increased safety and long lifetimes. With recent developments in cloud computing and the proliferation of big data, machine learning approaches have begun to deliver invaluable insights, which drives adaptive control of battery management systems (BMS) with improved performance. In this paper, a general framework utilizing an end-edge-cloud architecture for a cloud-based BMS is proposed, with the composition and function of each link described. Cloud-based BMS leverages from the Cyber Hierarchy and Interactional Network (CHAIN) framework to provide multi-scale insights, more advanced and efficient algorithms can be used to realize the state-of-X estimation, thermal management, cell balancing, fault diagnosis and other functions of traditional BMS system. The battery intelligent monitoring and management platform can visually present battery performance, store working-data to help in-depth understanding of the microscopic evolutionary law, and provide support for the development of control strategies. Currently, the cloud-based BMS requires more effects on the multi-scale integrated modeling methods and remote upgrading capability of the controller, these two aspects are very important for the precise management and online upgrade of the system. The utility of this approach is highlighted not only for automotive applications, but for any battery energy storage system, providing a holistic framework for future intelligent and connected battery management.