Hasil untuk "Highway engineering. Roads and pavements"

Esteban Parra, Sonia Haiduc, Preetha Chatterjee et al.

Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software engineering venues has outpaced the availability of qualified reviewers, creating a growing imbalance that risks constraining and negatively impacting the long-term growth of the Software Engineering (SE) research community. Our vision of the Future of the SE research landscape involves a more scalable, inclusive, and resilient peer review process that incorporates additional mechanisms for: 1) attracting and training newcomers to serve as high-quality reviewers, 2) incentivizing more community members to serve as peer reviewers, and 3) cautiously integrating AI tools to support a high-quality review process.

Himon Thakur, Armin Moin

The discussion around AI-Engineering, that is, Software Engineering (SE) for AI-enabled Systems, cannot ignore a crucial class of software systems that are increasingly becoming AI-enhanced: Those used to enable or support the SE process, such as Computer-Aided SE (CASE) tools and Integrated Development Environments (IDEs). In this paper, we study the energy efficiency of these systems. As AI becomes seamlessly available in these tools and, in many cases, is active by default, we are entering a new era with significant implications for energy consumption patterns throughout the Software Development Lifecycle (SDLC). We focus on advanced Machine Learning (ML) capabilities provided by Large Language Models (LLMs). Our proposed approach combines Retrieval-Augmented Generation (RAG) with Prompt Engineering Techniques (PETs) to enhance both the quality and energy efficiency of LLM-based code generation. We present a comprehensive framework that measures real-time energy consumption and inference time across diverse model architectures ranging from 125M to 7B parameters, including GPT-2, CodeLlama, Qwen 2.5, and DeepSeek Coder. These LLMs, chosen for practical reasons, are sufficient to validate the core ideas and provide a proof of concept for more in-depth future analysis.

Yang Yue, Zheng Jiang, Yi Wang

The heterogeneity in the organization of software engineering (SE) research historically exists, i.e., funded research model and hands-on model, which makes software engineering become a thriving interdisciplinary field in the last 50 years. However, the funded research model is becoming dominant in SE research recently, indicating such heterogeneity has been seriously and systematically threatened. In this essay, we first explain why the heterogeneity is needed in the organization of SE research, then present the current trend of SE research nowadays, as well as the consequences and potential futures. The choice is at our hands, and we urge our community to seriously consider maintaining the heterogeneity in the organization of software engineering research.

Klara Borowa, Andrzej Zalewski, Lech Madeyski

The software engineering researchers from countries with smaller economies, particularly non-English speaking ones, represent valuable minorities within the software engineering community. As researchers from Poland, we represent such a country. We analyzed the ICSE FOSE (Future of Software Engineering) community survey through reflexive thematic analysis to show our viewpoint on key software community issues. We believe that the main problem is the growing research-industry gap, which particularly impacts smaller communities and small local companies. Based on this analysis and our experiences, we present a set of recommendations for improvements that would enhance software engineering research and industrial collaborations in smaller economies.

Karina Kohl, Luigi Carro

Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware-energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment-intent articulation, architectural control, and verification-rather than code construction. This shift introduces accountability collapse as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices. We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education.

Sibo Ding, Dehuan Sun, Yong Hu et al.

With the continuous expansion of highway networks and rapid advancements in the transportation industry, the need for highway maintenance and reconstruction has become increasingly urgent. Resonant rubblization technology generates an interlocking structure within the pavement layer by producing diagonal cracks at angles of 35–40°, thereby significantly enhancing load-bearing capacity and structural stability. As a result, this technique offers substantial benefits, including a marked reduction in reflective cracking, efficient reuse of existing concrete slabs (with a utilization rate exceeding 85%), reduced construction costs (by 15–30% compared to conventional methods), and faster construction speeds—up to 7000 square yards per day. Consequently, resonant rubblization has emerged as a key method for rehabilitating aging cement concrete pavements. Building on this foundation, this paper reviews the fundamental principles of resonant rubblization technology by synthesizing global research findings and engineering case studies. It provides a comprehensive analysis of the historical development, equipment design, construction principles, and practical application outcomes of resonant rubblization, with particular attention to its effects on pavement structure, load-bearing capacity, and long-term stability. Future research should focus on developing more realistic subgrade models, improving evaluation methods for post-rubblization pavement performance, and advancing the intelligentization of resonant equipment. The ultimate goal is to enhance the quality of road maintenance and repair, ensure road safety, and promote the development of long-life, sustainable road infrastructure through the continued advancement and application of resonant rubblization technology.

Michał Rybacki

Abstract: The first concepts for the construction of High-Speed Rail (HSR) in Poland date back to 1995. However, it is only in recent years that these projects have begun to take concrete shape. A key investment within the HSR program is the "Y" line, connecting Warsaw, Łodź, Poznań, and Wrocław, where trains will reach speeds of up to 320 km/h. An essential infrastructure element is the construction of the long-distance tunnel in Łodź, which will become part of a multimodal railway hub. Celebrating its 20th anniversary in 2024, TINES has played a signifi cant role in the development of modern railway track structures in Poland, particularly in slab track construction. Its innovative solutions help reduce vibrations and noise while enhancing infrastructure durability. TINES actively participates in infrastructure projects, adapting its products to meet EU and national technical standards. However, regulatory and legal challenges continue to pose a risk to the full utilization of Polish companies' potential in HSR construction. Nevertheless, the industry's commitment and growing expertise inspire optimism regarding the implementation and future development of Poland's high-speed rail system. Keywords: High-Speed Rail; Ballastless Track; Infrastructure Development

Jiao Jin, Ruyi Rao, Shuai Liu et al.

Severe segregation and poor rheological properties in crumb rubber (CR) modified asphalt (CRMT) were addressed by investigating the performance improvement effect of organic expanded vermiculite (OEV) as a co-modifier, while the modification mechanism of the resulting asphalt was also elucidated. Vermiculite was thermally treated and chemically modified to enhance its interaction with the asphalt matrix and CR, improving dispersion and interfacial properties. CR/OEV/furfural extract oil (OIL) composite modified asphalt (COMT) was prepared in this study. The compatibility and microscopic mechanism of modified asphalt were characterized by dynamic shear rheological test, multiple stress creep recovery (MSCR) test, BBR test, thermal segregation test, fluorescent scanning test, infrared spectroscopy, and gel permeation chromatography. Rheological tests showed that the modified asphalt exhibited improved high-temperature stability, with increased G∗/sin(δ) values, and better low-temperature flexibility. Storage stability tests showed a reduced softening point difference, indicating enhanced homogeneity and reduced segregation. Microscopic analysis revealed that OEV effectively optimized the microstructure of the composite system by promoting the uniform dispersion of CR within the asphalt matrix. Furthermore, the macromolecular weight of COMT was increased by 31.9​%, molecular weight analysis confirmed a higher proportion of large molecular weight fractions, contributing to enhanced rheological properties and compatibility. These findings demonstrated that OEV significantly improved the performance and durability of CRMT, providing a promising approach for sustainable road construction.

Michał Krzysztof

Abstract: This article presents the world's first miniature MEMS scanning electron microscope. The device, thanks to its small size, low power consumption, and durable construction, can be used in previously inaccessible places, including space missions for imaging samples of cosmic dust, lunar, or Martian soil. Keywords: Miniature scanning electron microscope; Electron source; MEMS; Imaging

Mugeng Liu, Siqi Zhong, Weichen Bi et al.

Large language model-specific inference engines (in short as \emph{LLM inference engines}) have become a fundamental component of modern AI infrastructure, enabling the deployment of LLM-powered applications (LLM apps) across cloud and local devices. Despite their critical role, LLM inference engines are prone to bugs due to the immense resource demands of LLMs and the complexities of cross-platform compatibility. However, a systematic understanding of these bugs remains lacking. To bridge this gap, we present the first empirical study on bugs in LLM inference engines. We mine official repositories of 5 widely adopted LLM inference engines, constructing a comprehensive dataset of 929 real-world bugs. Through a rigorous open coding process, we analyze these bugs to uncover their symptoms, root causes, commonality, fix effort, fix strategies, and temporal evolution. Our findings reveal six bug symptom types and a taxonomy of 28 root causes, shedding light on the key challenges in bug detection and location within LLM inference engines. Based on these insights, we propose a series of actionable implications for researchers, inference engine vendors, and LLM app developers, along with general guidelines for developing LLM inference engines.

Ashis Kumar Mandal, Md Nadim, Chanchal K. Roy et al.

Research in software engineering is essential for improving development practices, leading to reliable and secure software. Leveraging the principles of quantum physics, quantum computing has emerged as a new computational paradigm that offers significant advantages over classical computing. As quantum computing progresses rapidly, its potential applications across various fields are becoming apparent. In software engineering, many tasks involve complex computations where quantum computers can greatly speed up the development process, leading to faster and more efficient solutions. With the growing use of quantum-based applications in different fields, quantum software engineering (QSE) has emerged as a discipline focused on designing, developing, and optimizing quantum software for diverse applications. This paper aims to review the role of quantum computing in software engineering research and the latest developments in QSE. To our knowledge, this is the first comprehensive review on this topic. We begin by introducing quantum computing, exploring its fundamental concepts, and discussing its potential applications in software engineering. We also examine various QSE techniques that expedite software development. Finally, we discuss the opportunities and challenges in quantum-driven software engineering and QSE. Our study reveals that quantum machine learning (QML) and quantum optimization have substantial potential to address classical software engineering tasks, though this area is still limited. Current QSE tools and techniques lack robustness and maturity, indicating a need for more focus. One of the main challenges is that quantum computing has yet to reach its full potential.

Shuoshuo Xu, Kai Zhao, James Loney et al.

Effective and rapid evaluation of pavement surface condition is critical for prioritizing maintenance, ensuring transportation safety, and minimizing vehicle wear and tear. While conventional manual inspections suffer from subjectivity, existing machine learning-based methods are constrained by their reliance on large and high-quality labeled datasets, which require significant resources and limit adaptability across varied road conditions. The revolutionary advancements in Large Language Models (LLMs) present significant potential for overcoming these challenges. In this study, we propose an innovative automated zero-shot learning approach that leverages the image recognition and natural language understanding capabilities of LLMs to assess road conditions effectively. Multiple LLM-based assessment models were developed, employing prompt engineering strategies aligned with the Pavement Surface Condition Index (PSCI) standards. These models' accuracy and reliability were evaluated against official PSCI results, with an optimized model ultimately selected. Extensive tests benchmarked the optimized model against evaluations from various levels experts using Google Street View road images. The results reveal that the LLM-based approach can effectively assess road conditions, with the optimized model -employing comprehensive and structured prompt engineering strategies -outperforming simpler configurations by achieving high accuracy and consistency, even surpassing expert evaluations. Moreover, successfully applying the optimized model to Google Street View images demonstrates its potential for future city-scale deployments. These findings highlight the transformative potential of LLMs in automating road damage evaluations and underscore the pivotal role of detailed prompt engineering in achieving reliable assessments.

Tim Wittenborg, Ildar Baimuratov, Ludvig Knöös Franzén et al.

The aerospace industry operates at the frontier of technological innovation while maintaining high standards regarding safety and reliability. In this environment, with an enormous potential for re-use and adaptation of existing solutions and methods, Knowledge-Based Engineering (KBE) has been applied for decades. The objective of this study is to identify and examine state-of-the-art knowledge management practices in the field of aerospace engineering. Our contributions include: 1) A SWARM-SLR of over 1,000 articles with qualitative analysis of 164 selected articles, supported by two aerospace engineering domain expert surveys. 2) A knowledge graph of over 700 knowledge-based aerospace engineering processes, software, and data, formalized in the interoperable Web Ontology Language (OWL) and mapped to Wikidata entries where possible. The knowledge graph is represented on the Open Research Knowledge Graph (ORKG), and an aerospace Wikibase, for reuse and continuation of structuring aerospace engineering knowledge exchange. 3) Our resulting intermediate and final artifacts of the knowledge synthesis, available as a Zenodo dataset. This review sets a precedent for structured, semantic-based approaches to managing aerospace engineering knowledge. By advancing these principles, research, and industry can achieve more efficient design processes, enhanced collaboration, and a stronger commitment to sustainable aviation.

László Gáspár, Z. Bencze

Introduction. An important tendency of our time is sustainability, good value for money and long-term planning. These are also the goals of the recently developed discipline, lifetime engineering. Although the principles of the science were originally developed for buildings and engineering structures, they can also be adapted to public roads. The article — through a case study in Hungary — presents the applicability of life engineering science to public road asset management. Besides, examples of pavement structure design, durability, maintenance-operation and environmental protection are also presented. Problem Statement. The typical lifetime engineering science principles used in the presentation were: increasing (pavement) life cycle; complex, multi-disciplinary design methodology; modular design; effective, quality insurance methods; high level satisfaction of the customers’ needs; minimisation of lifetime costs; sustainable, end-of-life strategies. It is emphasized that lifetime engineering is based on specific basic principles that effectively promote an up-to-date approach to any discipline related to engineering infrastructure. The paper shows that the efficacy of a road asset management. can be considerably increased if some elements of lifetime engineering are utilized.

Jieke Shi, Zhou Yang, David Lo

Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as well as green LLM4SE solutions that reduce energy consumption, water usage, and carbon emissions. This paper aims to redirect the focus of the research community towards the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance of LLM4SE and highlights the need for efficient and green LLM4SE solutions. Subsequently, the paper presents a vision for a future where efficient and green LLM4SE revolutionizes the LLM-based software engineering tool landscape, benefiting various stakeholders, including industry, individual practitioners, and society. The paper then delineates a roadmap for future research, outlining specific research paths and potential solutions for the research community to pursue. While not intended to be a definitive guide, the paper aims to inspire further progress, with the ultimate goal of establishing efficient and green LLM4SE as a central element in the future of software engineering.

Karol Lange

Abstract: Rail transit is a qualified form of cross-border traffic. Due to the specific functional scope, they are often marginalized or completely omitted in strategic documents determining the shape of the transport policy of the country or its individual regions. However, their proper regulation in the Polish and international legal system and ensuring an attractive transport offer can significantly affect the socio-economic development of border areas. In the current legal status, two divergent approaches of the legislator to traffic regulation on transit lines can be observed. One of them contains mechanisms stimulating traffic on the routes in question, while the other quite radically limits its operation. In this publication, an attempt was made to characterize and evaluate the legal regulations concerning the functioning of transport in the subject matter. Keywords: Transportation law; Railway law; Railway transport; International law; Railway; Public transport

Zhuoxuan Li, Iakov Korovin, Xinli Shi et al.

Rutting of asphalt pavements is a crucial design criterion in various pavement design guides. A good road transportation base can provide security for the transportation of oil and gas in road transportation. This study attempts to develop a robust artificial intelligence model to estimate different asphalt pavements' rutting depth clips, temperature, and load axes as primary characteristics. The experiment data were obtained from 19 asphalt pavements with different crude oil sources on a 2.038 km long full-scale field accelerated pavement test track (RIOHTrack, Road Track Institute) in Tongzhou, Beijing. In addition, this paper also proposes to build complex networks with different pavement rutting depths through complex network methods and the Louvain algorithm for community detection. The most critical structural elements can be selected from different asphalt pavement rutting data, and similar structural elements can be found. An extreme learning machine algorithm with residual correction (RELM) is designed and optimized using an independent adaptive particle swarm algorithm. The experimental results of the proposed method are compared with several classical machine learning algorithms, with predictions of Average Root Mean Squared Error, Average Mean Absolute Error, and Average Mean Absolute Percentage Error for 19 asphalt pavements reaching 1.742, 1.363, and 1.94\% respectively. The experiments demonstrate that the RELM algorithm has an advantage over classical machine learning methods in dealing with non-linear problems in road engineering. Notably, the method ensures the adaptation of the simulated environment to different levels of abstraction through the cognitive analysis of the production environment parameters.

David Lo

For decades, much software engineering research has been dedicated to devising automated solutions aimed at enhancing developer productivity and elevating software quality. The past two decades have witnessed an unparalleled surge in the development of intelligent solutions tailored for software engineering tasks. This momentum established the Artificial Intelligence for Software Engineering (AI4SE) area, which has swiftly become one of the most active and popular areas within the software engineering field. This Future of Software Engineering (FoSE) paper navigates through several focal points. It commences with a succinct introduction and history of AI4SE. Thereafter, it underscores the core challenges inherent to AI4SE, particularly highlighting the need to realize trustworthy and synergistic AI4SE. Progressing, the paper paints a vision for the potential leaps achievable if AI4SE's key challenges are surmounted, suggesting a transition towards Software Engineering 2.0. Two strategic roadmaps are then laid out: one centered on realizing trustworthy AI4SE, and the other on fostering synergistic AI4SE. While this paper may not serve as a conclusive guide, its intent is to catalyze further progress. The ultimate aspiration is to position AI4SE as a linchpin in redefining the horizons of software engineering, propelling us toward Software Engineering 2.0.

Jan Heine, Herbert Palm

The increasing pressure within VUCA (volatility, uncertainty, complexity and ambiguity) driven environments causes traditional, plan-driven Systems Engineering approaches to no longer suffice. Agility is then changing from a "nice-to-have" to a "must-have" capability for successful system developing organisations. The current state of the art, however, does not provide clear answers on how to map this need in terms of processes, methods, tools and competencies (PMTC) and how to successfully manage the transition within established industries. In this paper, we propose an agile Systems Engineering (SE) Framework for the automotive industry to meet the new agility demand. In addition to the methodological background, we present results of a pilot project in the chassis development department of a German automotive manufacturer and demonstrate the effectiveness of the newly proposed framework. By adopting the described agile SE Framework, companies can foster innovation and collaboration based on a learning, continuous improvement and self-reinforcing base.

M. Genet, Z. Sendekie, Addis Lemessa Jembere

Abstract Overwhelming increase in plastic waste since recent years became series ecological issue for both developed and developing countries. Compromising the large plastic waste potential in Ethiopia and poor pavement performance in the roads due to severe weather and high traffic loads, it is viewed to use this in-hand resource as asphalt modifier to urge sustainability, enhance the stability of asphalt mix and reduce the amount of bitumen used. The study aimed at investigating the effect of using waste LDPE plastic as a modifier of virgin bitumen. This study also tries to amplify the advantage of using wet blending method over dry. Four modified bitumen mixes prepared with 4, 6, 8 and 10% waste LDPE plastic content by the weight of optimum bitumen content (OBC) at different mixing temperatures (160, 170, and 180 °C) and different mixing times (1, 1.5 and 2 hours) to evaluate penetration point, softening point and ductility. 170 °C mixing temperature and 1.5 hour mixing time results, homogeneous mix between bitumen and waste LDPE plastic materials compared to other mixing temperatures and mixing times. Marshall Test Method was used to determine the optimum bitumen content and to evaluate the marshal properties of the plastic modified asphalt mix. The OBC of non-modified marshal sample was found to be 5.16% by weight of the total aggregate while the optimum waste LDPE modified bitumen content was found to be 6.5% by weight of the OBC. Asphalt mix modified with 6.5% of waste LDPE plastic content has 33.67% higher stability value compared to the non-modified asphalt mix.