Hasil untuk "Highway engineering. Roads and pavements"

Sorna Shanmuga Raja, Abdelhafid Zenati

This paper presents a lightweight, end-to-end highway lane detection architecture that jointly captures spatial and temporal information for robust performance in real-world driving scenarios. Building on the strengths of 3D convolutional neural networks and instance segmentation, we propose two models that integrate a 3D-ResNet encoder with a Point Instance Network (PINet) decoder. The first model enhances multi-scale feature representation using a Feature Pyramid Network (FPN) and Self-Attention mechanism to refine spatial dependencies. The second model introduces a Region of Interest (ROI) detection head to selectively focus on lane-relevant regions, thereby improving precision and reducing computational complexity. Experiments conducted on the TuSimple dataset (highway driving scenarios) demonstrate that the proposed second model achieves 93.40% accuracy while significantly reducing false negatives. Compared to existing 2D and 3D baselines, our approach achieves improved performance with fewer parameters and reduced latency. The architecture has been validated through offline training and real-time inference in the Autonomous Systems Laboratory at City, St George's University of London. These results suggest that the proposed models are well-suited for integration into Advanced Driver Assistance Systems (ADAS), with potential scalability toward full Lane Assist Systems (LAS).

H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley

Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.

Rui Yang, Michael Fu, Chakkrit Tantithamthavorn et al.

Retrieval-augmented generation (RAG)-based applications are gaining prominence due to their ability to leverage large language models (LLMs). These systems excel at combining retrieval mechanisms with generative capabilities, resulting in more accurate, contextually relevant responses that enhance user experience. In particular, Transurban, a road operation company, is replacing its rule-based virtual assistant (VA) with a RAG-based VA (RAGVA) to offer more flexible customer interactions and support a wider range of scenarios. In this paper, drawing from the experience at Transurban, we present a comprehensive step-by-step guide for building a conversational application and how to engineer a RAGVA. These guides aim to serve as references for future researchers and practitioners. While the engineering processes for traditional software applications are well-established, the development and evaluation of RAG-based applications are still in their early stages, with numerous emerging challenges remaining uncharted. To address this gap, we conduct a focus group study with Transurban practitioners regarding developing and evaluating their RAGVA. We identified eight challenges encountered by the engineering team and proposed eight future directions that should be explored to advance the development of RAG-based applications. This study contributes to the foundational understanding of a RAG-based conversational application and the emerging AI software engineering challenges it presents.

Embun Sari Ayu Embun, Indra Khaidir, Eva Rita

Pengelolaan sumber daya merupakan salah satu dari beberapa faktor yang berpengaruh terhadap keberhasilan suatu proyek, namun pada kenyataannya beberapa kontraktor dinilai belum mampu memenuhi target untuk mencapai hasil yang maksimal. Pada studi ini dilakukan untuk menganalisis faktor penyebab kurang baiknya kinerja atau penurunan produktivitas kontraktor dalam pengelolaan sumber daya pada pelaksanaan proyek konstruksi di Sumetera Barat. Penelitian melakukan pengukuran tingkat variabel pada menggunakan skala likert. Analisis data menggunakan analisis deskriptif untuk mengetahui karakteristik tanggapan responden. Hasil dari analisis data menunjukan bahwa variabel dari tiap faktor menghasilkan hasil yang valid dan kredibel, data yang digunakan berdistribusi normal dan hasil uji KMO dan Bartlett menunjukan bahwa setiap variabel memenuhi kriteria pengujian dengan nilai hasil KMO dan Bartlett lebih besar dari 0,05. Faktor manajemen sumber daya yang berpengaruh terhadap produktivitas pelaksanaan proyek konstruksi di Sumatera Barat selama masa pandemi covid-19 yaitu manajemen sumber daya manusia dan manajemen sumber daya peralatan, dan faktor yang paling dominan yaitu manajemen sumber daya peralatan. Kesimpulan penelitian bahwa manajemen sumber daya memiliki pengaruh terhadap produktivitas pelaksanaan proyek sebesar 51,1%, hal ini dikarenakan bahwa untuk menyelesaikan proyek bukan hanya dipengaruhi oleh manajemen sumber daya, namun juga terdapat faktor cuaca, lingkungan, sosial, peraturan dan tingkat kesulitan pelaksanaan proyek yang dapat mempengaruhi produktivitas, oleh karena itu secara tingkat persentase sebesar 48,9% lainnya merupakan faktor lain yang dapat mempengaruhi produktivitas pelaksanaan proyek konstruksi di Sumatera Barat selama masa pandemi covid-19.

T.F. Fwa

Airport pavement engineers are required to maintain pavement skid resistance at a satisfactory level to minimize the likelihood of runway excursions. Runway overruns and skidding along rapid exit taxiways are the two most frequently encountered forms of runway excursion accidents. Currently only empirical statistical models based on historical accident data are available to predict the risks of runway excursions. All such models fail to account for the impacts of pavement skid resistance properties. Mechanistic solutions of the tire-fluid-pavement interaction problem are now available, and the impacts of pavement skid resistance properties on runway excursion accidents can now be quantitatively evaluated. This paper presents a state-of-the-art review of recent research developments on the topic. It highlights the Concept of Pavement Skid Resistance State which provides (i) a logical theoretical framework for mechanistic representation of tire-pavement skid resistance, and (ii) an approach for modeling of the physical process of aircraft skidding and hydroplaning. Next, runway excursion risk prediction models are presented for risk evaluation of aircraft hydroplaning, runway overruns, and rapid exit taxiway excursions. Also examined in detail mechanistically is the effectiveness of pavement grooving in reducing the risks of runway excursions. The review clearly confirms the capability of mechanistic approach in analyzing runway excursions for the purpose of enhancing safe aircraft operations on airport runways.

Jonathan Álvarez Ariza

Active Learning (AL) is a well-known teaching method in engineering because it allows to foster learning and critical thinking of the students by employing debate, hands-on activities, and experimentation. However, most educational results of this instructional method have been achieved in face-to-face educational settings and less has been said about how to promote AL and experimentation for online engineering education. Then, the main aim of this study was to create an AL methodology to learn electronics, physical computing (PhyC), programming, and basic robotics in engineering through hands-on activities and active experimentation in online environments. N=56 students of two engineering programs (Technology in Electronics and Industrial Engineering) participated in the methodology that was conceived using the guidelines of the Integrated Course Design Model (ICDM) and in some courses combining mobile and online learning with an Android app. The methodology gathered three main components: (1) In-home laboratories performed through low-cost hardware devices, (2) Student-created videos and blogs to evidence the development of skills, and (3) Teacher support and feedback. Data in the courses were collected through surveys, evaluation rubrics, semi-structured interviews, and students grades and were analyzed through a mixed approach. The outcomes indicate a good perception of the PhyC and programming activities by the students and suggest that these influence motivation, self-efficacy, reduction of anxiety, and improvement of academic performance in the courses. The methodology and previous results can be useful for researchers and practitioners interested in developing AL methodologies or strategies in engineering with online, mobile, or blended learning modalities.

Magdalena Skiba

Abstract: City policy focuses on prioritizing public transport. Actions related to the improvement of infrastructure, as well as modernization and replacement of rolling stock, are being implemented. However, improving the quality of public transport means not only investing in facilities and modern solutions for passengers but also ensuring the comfort of work for people directly carrying out transport tasks. The regulation in force from 2022 imposes on carriers the obligation to provide toilets and social points for drivers and tram drivers. The legislator has defined general guidelines. The article presents a possible interpretation of the introduced regulations on the example of tram and bus infrastructure in Wrocław. The need to install toilets and social points in selected locations was analyzed and the costs of the above project were estimated. Keywords: Public transport; Social points

Tao Yue, Shaukat Ali, Paolo Arcaini

Quantum software engineering (QSE) is receiving increasing attention, as evidenced by increasing publications on topics, e.g., quantum software modeling, testing, and debugging. However, in the literature, quantum software requirements engineering (QSRE) is still a software engineering area that is relatively less investigated. To this end, in this paper, we provide an initial set of thoughts about how requirements engineering for quantum software might differ from that for classical software after making an effort to map classical requirements classifications (e.g., functional and extra-functional requirements) into the context of quantum software. Moreover, we provide discussions on various aspects of QSRE that deserve attention from the quantum software engineering community.

Guruprasad Parasnis, Anmol Chokshi, Vansh Jain et al.

This research paper presents a novel approach to pothole detection using Deep Learning and Image Processing techniques. The proposed system leverages the VGG16 model for feature extraction and utilizes a custom Siamese network with triplet loss, referred to as RoadScan. The system aims to address the critical issue of potholes on roads, which pose significant risks to road users. Accidents due to potholes on the roads have led to numerous accidents. Although it is necessary to completely remove potholes, it is a time-consuming process. Hence, a general road user should be able to detect potholes from a safe distance in order to avoid damage. Existing methods for pothole detection heavily rely on object detection algorithms which tend to have a high chance of failure owing to the similarity in structures and textures of a road and a pothole. Additionally, these systems utilize millions of parameters thereby making the model difficult to use in small-scale applications for the general citizen. By analyzing diverse image processing methods and various high-performing networks, the proposed model achieves remarkable performance in accurately detecting potholes. Evaluation metrics such as accuracy, EER, precision, recall, and AUROC validate the effectiveness of the system. Additionally, the proposed model demonstrates computational efficiency and cost-effectiveness by utilizing fewer parameters and data for training. The research highlights the importance of technology in the transportation sector and its potential to enhance road safety and convenience. The network proposed in this model performs with a 96.12 % accuracy, 3.89 % EER, and a 0.988 AUROC value, which is highly competitive with other state-of-the-art works.

Haoyu Sun, Yan Yan

Due to limited budgets allocated for road maintenance projects in various countries, road management departments face difficulties in making scientific maintenance decisions. This paper aims to provide road management departments with more scientific decision tools and evidence. The framework proposed in this paper mainly has the following four innovative points: 1) Predicting pavement performance deterioration levels of road sections as decision basis rather than accurately predicting specific indicator values; 2) Determining maintenance route priorities based on multiple factors; 3) Making maintenance plan decisions by establishing deep reinforcement learning models to formulate predictive strategies based on past maintenance performance evaluations, while considering both technical and management indicators; 4) Determining repair section priorities according to actual and suggested repair effects. By resolving these four issues, the framework can make intelligent decisions regarding optimal maintenance plans and sections, taking into account limited funds and historical maintenance management experiences.

Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real et al.

Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined task. For instance, we need a corpus of text broken into sentences to train a natural language machine-learning model. In this work, we will use the token \textit{dataset} to designate a structured set of data built to perform a well-defined task. Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table. Specifically, in science, each area has unique forms to organize, gather and handle its datasets. We believe that datasets must be a first-class entity in any knowledge-intensive process, and all workflows should have exceptional attention to datasets' lifecycle, from their gathering to uses and evolution. We advocate that science and engineering discovery processes are extreme instances of the need for such organization on datasets, claiming for new approaches and tooling. Furthermore, these requirements are more evident when the discovery workflow uses artificial intelligence methods to empower the subject-matter expert. In this work, we discuss an approach to bringing datasets as a critical entity in the discovery process in science. We illustrate some concepts using material discovery as a use case. We chose this domain because it leverages many significant problems that can be generalized to other science fields.

Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza et al.

Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.

Vahid Eghbal Akhlaghi, Ann Melissa Campbell, Ibrahim Demir

Natural disasters are highly complex and unpredictable. However, long-term planning and preparedness activities can help to mitigate the consequences and reduce the damage. For example, in cities with a high risk of flooding, appropriate roadway mitigation can help reduce the impact of floods or high waters on transportation systems. Such communities could benefit from a comprehensive assessment of mitigation on road networks and identification of the best subset of roads to mitigate. In this study, we address a pre-disaster planning problem that seeks to strengthen a road network against flooding. We develop a network design problem that maximizes the improvement in accessibility and travel times between population centers and healthcare facilities subject to a given budget. We provide techniques for reducing the problem size to help make the problem tractable. We use cities in the state of Iowa in our computational experiments.

Nura Shehu Aliyu Yaro, Muslich Hartadi Sutanto, Noor Zainab Habib et al.

Summary: During the production of palm oil, a significant amount of waste is generated. However, because of inefficient handling and utilization, these wastes are becoming a larger issue. As a result, one initiative is to use these wastes in the pavement industry as sustainable materials. However, there is still a lack of understanding about the wider incorporation of palm oil waste in asphalt pavement and its performance. This study examines existing literature on the use of various wastes in the pavement industry, including palm oil clinker (POC), palm oil fibre (POF), palm kernel shell (PKS), and palm oil fuel ash (POFA). As a result, this paper presents a systematic review and scientometric investigation of related study publications on many uses of palm oil waste in the asphalt pavement industry and its performance from 2009 to 2022. The VOSviewer application was used to conduct the scientometric study analysis. The relationship between interactions detected in co-authored country studies cited sources of co-citation, and the keyword of the co-occurrence and publication source enabled the identification of the research gap. According to the systematic literature review, 40%–60% POC can be used to fine aggregate for optimal performance, while 0–100% PKS can be used to replace coarse aggregate. In addition, 50%–80% POFA or POC fine (POCF) can be used as a filler replacement, 5%–8% POCF or POFA as a bitumen modifier, and 0.3% POF as a stabilizing additive. Furthermore, the study demonstrates that the safety of utilizing wastes with more than 50% CO2 emissions can be curtailed with minimal heavy metal leaching and radioactivity levels. The scientometric analysis may encourage researchers to seek out gaps in the literature that will aid in the long-term, multifaceted use of palm oil wastes in the asphalt pavement industry. Furthermore, the study recommends employing and researching the enormous potential of using palm oil waste in the pavement sectors because they are more sustainable and have better performance. However, there are some barriers to using palm oil waste in the asphalt pavement industry, such as a lack of design standards and guidelines, inefficient raw material processing conversion facilities, and large-scale production equipment.

Vilius Filotenkovas, Audrius Vaitkus

The performance of unbound base materials, exclusively of the upper base layers, besides compaction level and layer thickness, depends on unbound material type, aggregates shape, fine content and mechanical properties of aggregates. The response of the pavement structure to loading is expressed through stress and strain magnitudes, accumulation of which leads to layer permanent deformations. One of the key factors for designing unbound base layers is resilient modulus, which can be found from triaxial tests. The aim of the research is to analyse the effect of the aggregate particle shape, structure and the resistance to crushing properties on resilient modulus of the upper layers of the unbound base layers. The following properties have been determined during the tests: aggregate particle size distribution, particle shape and flakiness, percentage of crushed and broken particle surfaces, density, water absorption, resilient modulus under low stress level loading. According to the performed research with tested aggregate mixtures, it is assumed that most influence on resilient modulus is exerted by aggregate whole granular size distribution, water absorption and the largest aggregate particle surface angularity. Resilient modulus in the tested dolomite fraction mixtures differing from 32 mm to 56 mm showed any reasonable difference with mean nominal pressures being higher than 300 kPa.

Jones Yeboah, Feifei Pang, Hari Priya Ponnakanti

This paper represents preliminary work in identifying the foundation for the discipline of Software Engineering and discovering the links between the domains of Software Engineering and Information Technology (IT). Our research utilized IEEE Transactions on Software Engineering (IEEE-TSE), ACM Transactions on Software Engineering and Methodology (ACM-TOSEM), Automated Software Engineering (ASE), the International Conference on Software Engineering(ICSE), and other related journal publication in the software engineering domain to address our research questions. We explored existing frameworks and described the need for software engineering as an academic discipline. We went further to clarify the distinction difference between Software Engineering and Computer Science. Through this efforts we contribute to an understanding of how evidence from IT research can be used to improve Software Engineering as a discipline.

Eric L. Manibardo, Ibai Laña, Esther Villar et al.

Traffic forecasting models rely on data that needs to be sensed, processed, and stored. This requires the deployment and maintenance of traffic sensing infrastructure, often leading to unaffordable monetary costs. The lack of sensed locations can be complemented with synthetic data simulations that further lower the economical investment needed for traffic monitoring. One of the most common data generative approaches consists of producing real-like traffic patterns, according to data distributions from analogous roads. The process of detecting roads with similar traffic is the key point of these systems. However, without collecting data at the target location no flow metrics can be employed for this similarity-based search. We present a method to discover locations among those with available traffic data by inspecting topological features. These features are extracted from domain-specific knowledge as numerical representations (embeddings) to compare different locations and eventually find roads with analogous daily traffic profiles based on the similarity between embeddings. The performance of this novel selection system is examined and compared to simpler traffic estimation approaches. After finding a similar source of data, a generative method is used to synthesize traffic profiles. Depending on the resemblance of the traffic behavior at the sensed road, the generation method can be fed with data from one road only. Several generation approaches are analyzed in terms of the precision of the synthesized samples. Above all, this work intends to stimulate further research efforts towards enhancing the quality of synthetic traffic samples and thereby, reducing the need for sensing infrastructure.

Jakub Majewski

Abstract: The aim of this article is to present the plans for the energy transformation of the Polish railway system in the context of the objectives of the European and national climate policy. After a short introduction and reference to the main sources, the author discusses modern methods of reducing pollutant emissions accompanying the use of traction energy on the railroad. The second part describes the steps of a specific plan with the goal of achieving zero emissions for this mode of transport. The whole is closed with a summary that indicates a general change in the priorities of the European transport policy and the resulting new opportunities and impulses for the development of railways. Keywords: Sustainable transport; Railway; Climate policy; Emissions reduction

Moein Latifi, Fateme Golivand Darvishvand, Omid Khandel et al.

Road maintenance planning is an integral part of road asset management. One of the main challenges in Maintenance and Rehabilitation (M&R) practices is to determine maintenance type and timing. This research proposes a framework using Reinforcement Learning (RL) based on the Long Term Pavement Performance (LTPP) database to determine the type and timing of M&R practices. A predictive DNN model is first developed in the proposed algorithm, which serves as the Environment for the RL algorithm. For the Policy estimation of the RL model, both DQN and PPO models are developed. However, PPO has been selected in the end due to better convergence and higher sample efficiency. Indicators used in this study are International Roughness Index (IRI) and Rutting Depth (RD). Initially, we considered Cracking Metric (CM) as the third indicator, but it was then excluded due to the much fewer data compared to other indicators, which resulted in lower accuracy of the results. Furthermore, in cost-effectiveness calculation (reward), we considered both the economic and environmental impacts of M&R treatments. Costs and environmental impacts have been evaluated with paLATE 2.0 software. Our method is tested on a hypothetical case study of a six-lane highway with 23 kilometers length located in Texas, which has a warm and wet climate. The results propose a 20-year M&R plan in which road condition remains in an excellent condition range. Because the early state of the road is at a good level of service, there is no need for heavy maintenance practices in the first years. Later, after heavy M&R actions, there are several 1-2 years of no need for treatments. All of these show that the proposed plan has a logical result. Decision-makers and transportation agencies can use this scheme to conduct better maintenance practices that can prevent budget waste and, at the same time, minimize the environmental impacts.

Mohammad Kasra Habib, Stefan Wagner, Daniel Graziotin

Requirements Engineering (RE) is the initial step towards building a software system. The success or failure of a software project is firmly tied to this phase, based on communication among stakeholders using natural language. The problem with natural language is that it can easily lead to different understandings if it is not expressed precisely by the stakeholders involved, which results in building a product different from the expected one. Previous work proposed to enhance the quality of the software requirements detecting language errors based on ISO 29148 requirements language criteria. The existing solutions apply classical Natural Language Processing (NLP) to detect them. NLP has some limitations, such as domain dependability which results in poor generalization capability. Therefore, this work aims to improve the previous work by creating a manually labeled dataset and using ensemble learning, Deep Learning (DL), and techniques such as word embeddings and transfer learning to overcome the generalization problem that is tied with classical NLP and improve precision and recall metrics using a manually labeled dataset. The current findings show that the dataset is unbalanced and which class examples should be added more. It is tempting to train algorithms even if the dataset is not considerably representative. Whence, the results show that models are overfitting; in Machine Learning this issue is solved by adding more instances to the dataset, improving label quality, removing noise, and reducing the learning algorithms complexity, which is planned for this research.