Hasil untuk "Highway engineering. Roads and pavements"

Steffen Knoblauch, Ram Kumar Muthusamy, Pedram Ghamisi et al.

Highway networks are crucial for economic prosperity. Climate change-induced temperature fluctuations are exacerbating stress on road pavements, resulting in elevated maintenance costs. This underscores the need for targeted and efficient maintenance strategies. This study investigates the potential of open-source data to guide highway infrastructure maintenance. The proposed framework integrates airborne imagery and OpenStreetMap (OSM) to fine-tune YOLOv11 for highway crack localization. To demonstrate the framework's real-world applicability, a Swiss Relative Highway Crack Density (RHCD) index was calculated to inform nationwide highway maintenance. The crack classification model achieved an F1-score of $0.84$ for the positive class (crack) and $0.97$ for the negative class (no crack). The Swiss RHCD index exhibited weak correlations with Long-term Land Surface Temperature Amplitudes (LT-LST-A) (Pearson's $r\ = -0.05$) and Traffic Volume (TV) (Pearson's $r\ = 0.17$), underlining the added value of this novel index for guiding maintenance over other data. Significantly high RHCD values were observed near urban centers and intersections, providing contextual validation for the predictions. These findings highlight the value of open-source data sharing to drive innovation, ultimately enabling more efficient solutions in the public sector.

Liudmyla Parasiuk, Serhii Illiash, Tetiana Stasiuk et al.

Introduction. The article considers the features of calculating fuel consumption rates for machines and mechanisms. The main methods for determining fuel rates are analyzed, the influence of technical, operational and external factors is taken into account, and the environmental component is considered, in particular, the impact of fuel consumption on the environment and the use of SCR systems to reduce harmful emissions. The rational use of fuel and energy resources is one of the key tasks in the field of operation of vehicles and road construction equipment. An important role in this process is played by modern methods of assessing fuel consumption and the use of selective catalytic neutralization (SCR) systems, which allow reducing harmful emissions. To achieve maximum fuel efficiency, it is necessary to take into account a wide range of factors that affect the operation of equipment. Issues. Calculating fuel consumption rates is a complex process due to the variety of factors that influence it. The main problems facing researchers and engineers are the great variability of equipment operating conditions (terrain relief, climatic conditions, soil type, etc.), the dependence of fuel consumption on the technical condition of machines, the control method and the level of process automation, the lack of a universal methodology that takes into account all aspects of the operation of different types of equipment, the need to develop effective algorithms for optimizing fuel consumption. Purpose. The purpose of the study is to analyze modern approaches to calculating fuel consumption rates, identify key factors affecting fuel consumption, and develop recommendations for optimizing the use of fuel resources in mechanical engineering and the transport sector. Materials and methods. The article is of a review nature. The article uses a systematic approach, which is a set of general scientific methodological principles (requirements), based on the consideration of objects as systems.

Sebastian Cisowski

Abstract: The development of modern driver assistance systems, such as Adaptive Cruise Control (ACC) and its enhanced version ACC+, represents a significant step towards improving road safety. This article analyzes the operation of these systems in the context of the causes and effects of traffic accidents. Based on the reconstruction of a specific traffic incident, a detailed assessment of the impact of ACC and ACC+ on traffic safety was conducted. The study is based on the analysis of measurable technical parameters that change under varying road conditions, both with the systems activated and deactivated. The paper also discusses examples of preventive strategies for similar incidents, utilizing new technologies applied in modern vehicles. Particular attention is given to differences in vehicle behavior under various operational scenarios of ACC and ACC+ systems, with a focus on their impact on the safety of road users. This article contributes to the development of knowledge on minimizing accident risks through advanced driver assistance systems. Keywords: ACC; ACC+; Safety; Traffic

Pavlo Holoborodko, Darius Bazaras, Aldona Jarašūnienė

The analysed research question raises the problem of modernisation of technical specifications of the locomotive on electric traction. The authors of the scientific research consider the modernisation of the DS3 locomotive with the transition from a single-phase (25 kV 50 Hz AC) to a two-phase (25 kV 50 Hz AC/3 kV DC) power supply system, which aims to increase interoperability with neighbouring railway networks. A multi-level literature review has been carried out, transient and dynamic characteristics have been modelled in MATLAB, and the principles of control of traction converters and motors have been formulated. Ensuring stable operating modes when switching power systems is confirmed by optimal attenuation ζ ≈ 0.7, and the electromagnetic compatibility analysis has revealed characteristic interference of 500–3000 Hz, thus allowing us to propose filters in accordance with EN 50121. The radar graph of the comparative analysis provides an improvement in the main metrics (power, traction efforts, efficiency, and reaction time) in the context of an ideal 100% scale. A step-by-step roadmap for the functional compatibility of ERTMS / ETCS and GSM-R in the Siemens SIBAS 32 platform has been designed and technical conditions for certification according to TSI and EN standards have been formed. The modernisation of DS3 is recognised as technically feasible, cost-effective and compliant with international technical aspects, which ensures prospects for joint operation in Ukraine and the EU.

Felipe H. Cava, Dimas B. Ribeiro, Claudia A. Pereira et al.

Airport pavement design methods, such as those of the FAA, assume general climatic conditions, although regional temperature variations significantly affect pavement behavior, stress strain distribution, performance, and reliability. With global warming, evaluating how temperature influences pavement materials and resilience has become increasingly important. This study conducts a reliability analysis of pavement designed by traditional methods while incorporating seasonal temperature variation for Sao Paulo Congonhas International Airport CGH. After designing the pavement based on the airports traffic mix, a Monte Carlo Simulation was used to assess reliability under temperature changes. Results show that the cumulative damage factor from MCS is 79% lower than that obtained with the FAA method, indicating that aircraft cause less damage than predicted. The pavement could support 2.5 times more traffic at 95% reliability and 5.0 times more at 50% reliability. These findings suggest that, in Brazilian airports where fatigue governs design, FAARFIELD overestimates damage and increases construction costs, highlighting the need to calibrate design methods to local climatic conditions. Limitations include the deterministic temperature model and the exclusion of factors such as precipitation, thickness variability, and temperature dependent fatigue testing. Future work will aim to address these gaps and refine performance equations.

Muneera Bano, Hashini Gunatilake, Rashina Hoda

Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.

Prathyush Kumar Reddy Lebaku, Lu Gao, Jingran Sun et al.

Road safety is impacted by a range of factors that can be categorized into human, vehicle, and roadway/environmental elements. This research explores the connection between pavement performance and road safety, particularly in relation to crash frequency and severity, using data from the Iowa Department of Transportation (DOT) for 2022. By merging crash data with pavement inventory data, we conduct a spatial analysis that incorporates the geographical coordinates of crash sites with the conditions of road segments. Statistical methods are applied to compare crash rates and severity across various pavement condition categories. To identify the most influential factors affecting crash rates and severity, we use machine learning models along with negative binomial and ordered probit regression models. The study's key findings reveal that higher speed limits, well-maintained roads, and improved friction scores correlate with lower crash rates, whereas rougher roads and adverse weather conditions are linked to higher crash severity. This analysis emphasizes the critical need for prioritizing pavement maintenance and integrating safety-focused design principles to boost road safety. Moreover, the study underscores the ongoing need for research to better understand and address the intricate relationship between pavement performance and road safety.

Eunice Ogochukwu Okeke, Okechukwu Pius Aghamelu, Olufemi Victor Omonona et al.

Construction of any sustainable road requires the determination of engineering properties of the materials to be used in the project. In this work, the impact of the construction materials used in some parts on Anambra State (southeastern Nigeria) with significant road pavement failure rate is investigated. Soil and aggregate samples were collected in situ on some failed spots on major roads in the state and were subjected to standard laboratory analyses. The results indicated that the roads were constructed on sandy subgrades with gravel-sized particles that ranged from 0 – 43 %, sand 25 – 77 %, and fines 14 – 63 %. The liquid limit of the subgrade materials ranged from 27 – 44 %, plasticity index 10 – 24 %, while the linear shrinkage ranges from 8 – 11 %. These Atterberg limit results denote low to moderate expandable soils. Maximum dry density ranged between 1.93 - 2.20 Mg/m3, while optimum moisture content ranged from 6.78 to 15.9%. The California bearing ratio (CBR), on the other hand, ranged between 60 – 161 %, for unsoaked condition, and 17.8 %, for soaked condition, indicating fairly stable materials. Based on the American Association of State Highway and Transportation Officials (AASHTO) classification system, the subgrade samples classify mainly as A-2-6 soil or as CL according to the Unified Soil Classification System (USCS). The particle density value ranges from 2.35 - 2.71 Mg/m3, while the field relative compaction density ranges between 89 – 108 %. Analysis on the sub base and base course aggregates indicated bulk density of fine aggregate (sand) that ranged from 0.16 - 1.68 Mg/m3. Comparison of the results with an engineering standard revealed that the materials did not meet standard limits(FMW&H), hence, the failure recorded on the paved roads. Water inflow would result in subgrade material expansion, hence, stability and bearing capacity reduction. ANOVA test result indicates significant variable from the data source while principal component and correlation analyses suggests that moisture content, liquid limit and plasticity index have direct statistical relationships. This research therefore advocates proper drainage design and stabilization of the subgrade materials in the area during road construction.

Nishesh P. Chhetri, Rishav Jaiswal, Rabina Poudel

Nepal is a country known for its diverse and challenging topography, and it relies heavily on a robust road infrastructure network to connect its remote regions and urban centers. This study addresses the critical need for enhanced road safety and infrastructure resilience on the Siddhababa road section of the Siddhartha Highway, Nepal, notorious for its high accident rates and susceptibility to landslides. Given the road's strategic importance in connecting remote regions and its challenging topographical conditions, our research aimed to identify the most suitable pavement type to mitigate these issues. Through a detailed examination incorporating eight different soil tests, alongside evaluations of traffic loads, weather conditions, and existing pavement performance, we adopted a comparative analysis methodology to assess the viability of flexible versus rigid pavements within this unique context. Results revealed that the soil composition and environmental conditions of the Siddhababa section significantly influence pavement performance, with specific gravity, moisture content, and California Bearing Ratio (CBR) tests indicating a nuanced suitability for both pavement types under varying circumstances. Our analysis concluded that, despite the economic and staged reinforcement benefits of flexible pavements, the durability, safety, and maintenance considerations favor the adoption of rigid pavement for the Siddhababa road section. However, acknowledging the economic constraints, a hybrid approach is recommended, emphasizing rigid pavements for the most vulnerable sections and flexible pavements elsewhere. This study contributes to the pavement engineering field by providing a model for pavement type selection in mountainous regions, aiming to enhance road safety and durability amidst challenging environmental conditions.

Artem Bezuglyi, Bohdan Stasiuk, Nataliia Mudrychenko et al.

Introduction. Effective functioning of road transport is only possible when the quantitative growth, quality level, and technical condition of roads meet the demands of road transport. This combination of road network characteristics can only be achieved with adequate funding to implement all planned works. Investments in road infrastructure can become a catalyst for economic recovery and growth, as they will improve logistics capabilities, reduce freight and passenger transportation costs, and enhance regional competitiveness. Problem Statement. In the first decades of independence, the road industry faced chronic underfunding, which negatively affected its development. The situation worsened with the full-scale military aggression of the Russian Federation in 2024. According to the World Bank's “Rapid Damage and Needs Assessment (RDNA3)” report, post-war recovery needs for public roads and bridges alone are estimated at around USD 28.8 billion. For the rapid and quality restoration and development of Ukraine, this issue is critically important, as the state of the roads affects transportation costs, prices, employment levels, and the overall pace of economic development. Furthermore, the full-scale invasion by the Russian Federation raises questions about ensuring reliable logistics routes, prompt transportation of humanitarian aid, and the country's defense capabilities. Objective. The aim of this research is to determine the key principles for socio-economic evaluation of state-level programmatic documents to improve road management efficiency. Materials and Methods. The article uses a systematic (comprehensive) approach, which represents a set of general scientific methodological principles (requirements) based on considering objects as systems. A priority in this process is ensuring an adequate level of data objectivity, timeliness, and the relevance of processed information. In addition, the article applies methods of comprehensive and systemic analysis, abstract-logical, graphical, statistical, computational-design, comparative analysis, and expert assessments. Results. A mathematical calculation was obtained for assessing the effectiveness of the State Targeted Economic Program for the Development of Public Roads of National Importance for 2018-2023.

Muhammed Yasin Çodur, Halis Bahadir Kasil, Emre Kuşkapan

Hot mix asphalt, which is frequently used in road pavements, contains bitumen in certain proportions. This bitumen ratio varies according to the layers in the road pavements. The bitumen ratio in each pavement is usually estimated by the Marshall design method. However, this method is costly as well as time-consuming. In this study, the Naive Bayes method, which is a machine learning algorithm, was used to estimate the bitumen ratio practically. In the study, a total of 102 asphalt concrete designs were examined, which were taken from the wearing course, binder course, and asphalt concrete base course and stone mastic asphalt wearing course layers. Each road pavement layer was divided into three different classes according to the bitumen ratios and the algorithm was trained with machine learning. Then the bitumen ratio was estimated for each data set. As a result of this process, the bitumen ratios of the layers were estimated with an accuracy between 75% and 90%. In this study, it was revealed that the bitumen ratio in the road pavement layers could be estimated practically and economically.

Marcus Kessel, Colin Atkinson

The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.

Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito et al.

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.

Valerio Terragni, Annie Vella, Partha Roop et al.

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.

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.

Er. Vikash Sharma

Highways plays a vital role in the development and progress of any nation, which provides them access to the resources and interconnection between countries, states and cities. India is known to have one of largest highway network in the world, which transports approximately 64.5% of goods all over the country and 90% traffic passengers of the country, who uses this network to commute. The total expenditure allocated by the Ministry of Road Transport and Highways for 2023-2024 is Rs. 2.7 lakh crores, which is 36% higher than 2022-2023(1.99 lakh crores). All we need now a day is the economical and engineering approach technique to enhance durability and life of the existing roads/ highways. This study is a review of application of stabilizers for Granular Sub Base of Flexible Pavements. The main objective of this study to determine Unconfined Compressive Strength, Elastic Modulus of stabilized material prepared with different dose of calcium chloride as a stabilizer for use in Cement Treated Sub Base (CTSB), determine optimum dose of CaCl2 Stabilizers with or without cement to achieve desired strength of Cement Treated Sub Base (CTSB) and to determine the durability characteristics of the samples prepared at optimum dose. This paper deduces that many researchers used different stabilizers such as pumice, fly-ash, lime stabilized fly-ash, combined cement and bitumen emulsion, which were found suitable and effective to achieve required strength for the flexible pavements

Michał Żochowski

Abstract: The author presented the legal status in relation to the use of balustrades on bridges. The analysis presented regulations on technical conditions on bridges and regulations on occupational health and safety. The article attempts to carry out the analysis in such a way that the conclusions are universal and can be applied to all types of objects. It has been shown that the balustrades are an element which protects against falling from a height. This type of collective protection elements should be used when there is a risk of falling from a height. Keywords: Balustrade, Bridge, Safety

Jie JW Wu

Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional complexity and interdisciplinary collaboration challenges. This poses difficulties in using traditional software lifecycle models such as waterfall, spiral, or agile models when building ML-enabled systems. In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems. By interviewing practitioners from software companies, we established a set of 8 propositions for using V-Model to manage interdisciplinary collaborations when building products with ML components. Based on the propositions, we found that despite requiring additional efforts, the characteristics of V-Model align effectively with several collaboration challenges encountered by practitioners when building ML-enabled systems. We recommend future research to investigate new process models, frameworks and tools that leverage the characteristics of V-Model such as the system decomposition, clear system boundary, and consistency of Validation & Verification (V&V) for building ML-enabled systems.

Agnieszka Fortońska

Abstract: The article presents the issue of responsibility for the use of unmanned aerial vehicles. As part of the study, selected domestic and foreign regulations will be analyzed. The author will present selected legal regulations of the European Union Member States: Poland and Croatia. Moreover, the laws of Canada and the United States will be discussed. Keywords: Drone; Civil liability; Criminal Liability

Lorenz Graf-Vlachy, Daniel Graziotin, Stefan Wagner

Context: Citations are a key measure of scientific performance in most fields, including software engineering. However, there is limited research that studies which characteristics of articles' metadata (title, abstract, keywords, and author list) are driving citations in this field. Objective: In this study, we propose a simple theoretical model for how citations come to be with respect to article metadata, we hypothesize theoretical linkages between metadata characteristics and citations of articles, and we empirically test these hypotheses. Method: We use multiple regression analyses to examine a data set comprising the titles, abstracts, keywords, and authors of 16,131 software engineering articles published between 1990 and 2020 in 20 highly influential software engineering venues. Results: We find that number of authors, number of keywords, number of question marks and dividers in the title, number of acronyms, abstract length, abstract propositional idea density, and corresponding authors in the core Anglosphere are significantly related to citations. Conclusion: Various characteristics of articles' metadata are linked to the frequency with which the corresponding articles are cited. These results partially confirm and partially go counter to prior findings in software engineering and other disciplines.