Hasil untuk "Highway engineering. Roads and pavements"

Grzegorz Wrochna

At the Polish Space Agency (POLSA), we have always enthusiastically entered the new decade with ambitious development plans to strengthen Poland's position on the international stage and accelerate technological and economic progress in the country. In an era of increasing competition in the space sector, as a key institution, we have worked with commitment to build space infrastructure, develop innovative technologies and promote international cooperation. Our priorities for the coming years, in my opinion, should include, among other things, the development of advanced satellite systems, participation in space exploration, enhancing security in orbit, and building a human resource base for the space sector. The Polish Space Agency (POLSA) was established in 2014 to support the development of the domestic space sector and integrate it with international programs. Over the decade, Poland has become an active participant in global initiatives, signing agreements with ESA, NASA and agencies from France, Italy, the US and Israel, among others, and joining the Artemis Accords in 2021 opened the way for Poland to participate in lunar exploration missions.

Xiuru Jia, Qiaoling Ji, Yu Cheng et al.

To address challenges in subgrade construction using Yellow River floodplain soils, this study developed a reduced density filler by mixing aged fly ash with locally sourced clayey sand. The research aimed to establish a robust framework for bridging the gap between pavement design specifications (resilient modulus, E 0 ) and construction quality control (compaction degree, K ), enabling performance-based quality assurance. Laboratory experiments evaluated compaction characteristics, California Bearing Ratio (CBR), and Laboratory Static Resilient Modulus ( E 0 lab ) of the mixtures. Results showed optimal performance at 30%–36% fly ash content, achieving maximum CBR of 23.6% and E 0 lab of 54.56 MPa. A novel global regression model was established, directly linking CBR to both K and fly ash content ( FA% ), offering a powerful tool for construction quality assurance. Furthermore, a practical pathway was developed to convert E 0 lab to field design E 0 (via CBR and existing correlations), facilitating the translation of laboratory findings into engineering design. This research culminates in a unified framework for construction quality control, providing recommended K and FA% acceptance windows to guarantee target E 0 and CBR values. The developed mixtures are highly suitable for Class II (lower layers) and Class III/IV (top layers) highway subgrades. This study offers robust technical support for sustainable fly ash utilization and performance-driven subgrade construction.

Anna Miniukova, Pavlo Stashuk

Introduction. Many bridge structures worldwide were designed in accordance with outdated standards and no longer meet the demands of modern transport loads. This creates a global safety issue, making the development of effective solutions for their strengthening a critically important task. Problem Statement. he vast majority of the global bridge infrastructure, particularly in developing countries, was constructed in the mid-20th century. The design of these structures was based on the normative loads of that time, which were significantly lower than current ones. Today, the increase in traffic intensity and the emergence of oversized and heavy-duty vehicles lead to bridge overloading. This causes premature physical wear of the structures, the appearance of cracks, a reduction in their load-bearing capacity, and, consequently, poses a safety threat to all road users. Materials and methods. The methodology of this study is based on a comprehensive approach that combines theoretical analysis, mathematical modeling, and numerical calculations. The purpose of using these selected methods is to substantiate the need for strengthening existing reinforced concrete bridge structures and to prove the effectiveness of external prestressed reinforcement compared to traditional solutions. The research is based on the Finite Element Method (FEM). This numerical method allows for approximating the behavior of a complex structure by dividing it into a set of simple elements (finite elements). This makes it possible to accurately determine the stress-strain state of the entire object. The calculations were performed using the licensed software package LIRA-SAPR, a modern tool for modeling, analyzing, and designing building structures.

Yu Zhang, Chen Zhang, Zhiduo Zhu et al.

The reasonable selection of soil layer parameters relates to the accurate prediction of the horizontal deformation of the foundation pit, which is the main problem of highway tunnel pit design. The aim of this paper is to obtain suitable soil layer parameters for finite element simulation of highway tunnel based on the particle swarm optimisation (PSO) and support vector machine (SVM). First, considering the overfitting problem of SVM in the inversion of soil parameters, the PSO was used to improve the SVM model. Second, the PSO- SVM model was trained with 25 groups of elastic modulus as input values and deformation as output values. Then, according to the monitored deformation data, the soil parameters were inverted by PSO-SVM model. Finally, the inversion parameters were substituted into the finite element model to predict the horizontal deformation of the foundation pit. The results showed that based on the inversion parameters of PSO-SVM model, the finite element method had a good accuracy in predicting the horizontal deformation of the foundation pit. The average relative error between the predicted value and monitored value was 2.95%. Therefore, the application of the parameter inversion method based on PSO-SVM had a reference value for tunnel pit design.

Piotr Szyszka, Tomasz Grzebyk

Abstract: The article describes a miniature mass spectrometer made at the Wroclaw University of Science and Technology in cooperation with Creotech Instruments for of the European Space Agency. It was manufactured by the use of MEMS technology (micro-electro-mechanical systems) combined with precise 3D printing. It is one of the smallest gas composition analyzers (the measuring head is 2×2×10 cm 3 and weighs only 120 grams) that can be used in space applications. Its operating parameters are not much worse than for the classic, much larger counterparts (resolution reaches 100 and mass range up to 400 AMU). Keywords: Mass spectrometry; MEMS technology; Gas composition analysis

Edmond V. Muho, Niki D. Beskou, Jiang Qian

This work reviews models and methods for determining the dynamic response of pavements to moving vehicle loads in the framework of continuum-based three dimensional models and linear theories. This review emphasizes the most representative models and methods of analysis in the existing literature and illustrates all of them by numerical examples. Thus, 13 such examples are presented here in some detail. Both flexible and rigid (concrete) pavement models involving simple and elaborate cases with respect to geometry and material behavior are considered. Thus, homogeneous or layered half-spaces with isotropic or cross-anisotropic and elastic, viscoelastic or poroelastic properties are considered. The vehicles are modeled as simple point or distributed loads or discrete spring-mass-dashpot system moving with constant or variable velocity. The dynamic response of the above pavement-vehicle systems is obtained by analytical/numerical or purely numerical methods of solution. Analytical/numerical methods have mainly to do with Fourier transforms or complex Fourier series with respect to both space and time. Purely numerical methods involve the finite element method (FEM) and the boundary element method (BEM) working in time or frequency domain. Critical discussions on the advantages and disadvantages of the various pavement-vehicle models and their methods of analysis are provided and the effects of the main parameters on the pavement response are determined through parametric studies and presented in the examples. Finally, conclusions are provided and suggestions for future research are made.

Christoph Treude, Marco A. Gerosa

Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development.

Taqwa I. Alhadidi, Asmaa Alazmi, Shadi Jaradat et al.

Pavement distress, such as cracks and potholes, is a significant issue affecting road safety and maintenance. In this study, we present the implementation and evaluation of Bidirectional Cascaded Neural Networks (BCNNs) for the classification of pavement crack images following image augmentation. We classified pavement cracks into three main categories: linear cracks, potholes, and fatigue cracks on an enhanced dataset utilizing U-Net 50 for image augmentation. The augmented dataset comprised 599 images. Our proposed BCNN model was designed to leverage both forward and backward information flows, with detection accuracy enhanced by its cascaded structure wherein each layer progressively refines the output of the preceding one. Our model achieved an overall accuracy of 87%, with precision, recall, and F1-score measures indicating high effectiveness across the categories. For fatigue cracks, the model recorded a precision of 0.87, recall of 0.83, and F1-score of 0.85 on 205 images. Linear cracks were detected with a precision of 0.81, recall of 0.89, and F1-score of 0.85 on 205 images, and potholes with a precision of 0.96, recall of 0.90, and F1-score of 0.93 on 189 images. The macro and weighted average of precision, recall, and F1-score were identical at 0.88, confirming the BCNN's excellent performance in classifying complex pavement crack patterns. This research demonstrates the potential of BCNNs to significantly enhance the accuracy and reliability of pavement distress classification, resulting in more effective and efficient pavement maintenance and management systems.

Jonathan Ullrich, Matthias Koch, Andreas Vogelsang

With the advent of generative LLMs and their advanced code generation capabilities, some people already envision the end of traditional software engineering, as LLMs may be able to produce high-quality code based solely on the requirements a domain expert feeds into the system. The feasibility of this vision can be assessed by understanding how developers currently incorporate requirements when using LLMs for code generation-a topic that remains largely unexplored. We interviewed 18 practitioners from 14 companies to understand how they (re)use information from requirements and other design artifacts to feed LLMs when generating code. Based on our findings, we propose a theory that explains the processes developers employ and the artifacts they rely on. Our theory suggests that requirements, as typically documented, are too abstract for direct input into LLMs. Instead, they must first be manually decomposed into programming tasks, which are then enriched with design decisions and architectural constraints before being used in prompts. Our study highlights that fundamental RE work is still necessary when LLMs are used to generate code. Our theory is important for contextualizing scientific approaches to automating requirements-centric SE tasks.

Maarten Vlaswinkel, Duarte Antunes, Frank Willems

Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential to keep development time and costs at acceptable levels. In this work, an innovative self-learning calibration method is presented based on in-cylinder pressure curve shaping. This method combines Principal Component Decomposition with constrained Bayesian Optimization. To realize maximal thermal engine efficiency, the optimization problem aims at minimizing the difference between the actual in-cylinder pressure curve and an Idealized Thermodynamic Cycle. By continuously updating a Gaussian Process Regression model of the pressure's Principal Components weights using measurements of the actual operating conditions, the mean in-cylinder pressure curve as well as its uncertainty bounds are learned. This information drives the optimization of calibration parameters, which are automatically adapted while dealing with the risks and uncertainties associated with operational safety and combustion stability. This data-driven method does not require prior knowledge of the system. The proposed method is successfully demonstrated in simulation using a Reactivity Controlled Compression Ignition engine model. The difference between the Gross Indicated Efficiency of the optimal solution found and the true optimum is 0.017%. For this complex engine, the optimal solution was found after 64.4s, which is relatively fast compared to conventional calibration methods.

Tzu-Yun Tseng, Hongyu Lyu, Josephine Li et al.

Road damage can create safety and comfort challenges for both human drivers and autonomous vehicles (AVs). This damage is particularly prevalent in rural areas due to less frequent surveying and maintenance of roads. Automated detection of pavement deterioration can be used as an input to AVs and driver assistance systems to improve road safety. Current research in this field has predominantly focused on urban environments driven largely by public datasets, while rural areas have received significantly less attention. This paper introduces M2S-RoAD, a dataset for the semantic segmentation of different classes of road damage. M2S-RoAD was collected in various towns across New South Wales, Australia, and labelled for semantic segmentation to identify nine distinct types of road damage. This dataset will be released upon the acceptance of the paper.

Shraddha Surana, Ashwin Srinivasan, Michael Bain

Engineering information systems for scientific data analysis presents significant challenges: complex workflows requiring exploration of large solution spaces, close collaboration with domain specialists, and the need for maintainable, interpretable implementations. Traditional manual development is time-consuming, while "No Code" approaches using large language models (LLMs) often produce unreliable systems. We present iProg, a tool implementing Interactive Structured Inductive Programming. iProg employs a variant of a '2-way Intelligibility' communication protocol to constrain collaborative system construction by a human and an LLM. Specifically, given a natural-language description of the overall data analysis task, iProg uses an LLM to first identify an appropriate decomposition of the problem into a declarative representation, expressed as a Data Flow Diagram (DFD). In a second phase, iProg then uses an LLM to generate code for each DFD process. In both stages, human feedback, mediated through the constructs provided by the communication protocol, is used to verify LLMs' outputs. We evaluate iProg extensively on two published scientific collaborations (astrophysics and biochemistry), demonstrating that it is possible to identify appropriate system decompositions and construct end-to-end information systems with better performance, higher code quality, and order-of-magnitude faster development compared to Low Code/No Code alternatives. The tool is available at: https://shraddhasurana.github.io/dhaani/

Zhen Liu, Wenxiu Wu, Xingyu Gu et al.

The PaveDistress dataset contains high-resolution images of road surface distresses, including cracks, repairs, potholes, and background images without defects. The data were collected using a specialized pavement inspection vehicle along the S315 highway in China. The vehicle was equipped with a Basler raL2048-80km line scan camera and infrared laser-assisted lighting, capturing images at 1mm intervals with a resolution of 3854 × 2065 pixels. The images were taken every 2 meters across various lighting conditions, including daylight, dusk, and in challenging environments such as tunnels and cloudy weather. The dataset is organized into distinct categories, covering transverse cracks, longitudinal cracks, map cracks, and more, enabling detailed categorization of pavement distresses. Each image represents a real-world road coverage area of 3.9m × 2.1m, allowing for accurate measurements of defect dimensions. This dataset supports the development of deep learning models for non-destructive detection of road defects, providing valuable resources for civil engineering research and practical applications in road maintenance systems. The dataset can be reused for tasks such as image classification, object detection, and segmentation, enabling researchers to create advanced machine learning models for road distress detection and assessment. By providing high-quality, diverse images, the PaveDistress dataset offers significant potential for research in automated pavement condition monitoring and management systems.

Yvonne Dittrich, Johan Bolmsten, Catherine Seidelin

Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings, and makes it possible to develop methods, tools and techniques with software engineering practitioners. However, as the research moves beyond the observational approach, it requires a different kind of interaction with the software development organisation. This makes action research a challenging endeavour, and it makes it difficult to teach action research through a course that goes beyond explaining the principles. This chapter is intended to support learning and teaching action research, by providing a rich set of examples, and identifying tools that we found helpful in our action research projects. The core of this chapter focusses on our interaction with the participating developers and domain experts, and the organisational setting. This chapter is structured around a set of challenges that reoccurred in the action research projects in which the authors participated. Each section is accompanied by a toolkit that presents related techniques and tools. The exercises are designed to explore the topics, and practise using the tools and techniques presented. We hope the material in this chapter encourages researchers who are new to action research to further explore this promising opportunity.

Nikhil Patnaik, Joseph Hallett, Awais Rashid

Writing secure code is challenging and so it is expected that, following the release of code-generative AI tools, such as ChatGPT and GitHub Copilot, developers will use these tools to perform security tasks and use security APIs. However, is the code generated by ChatGPT secure? How would the everyday software or security engineer be able to tell? As we approach the next decade we expect a greater adoption of code-generative AI tools and to see developers use them to write secure code. In preparation for this, we need to ensure security-by-design. In this paper, we look back in time to Saltzer & Schroeder's security design principles as they will need to evolve and adapt to the challenges that come with a world of AI-generated code.

Zhengji Li, Xi Xiao, Jiacheng Xie et al.

With the development of modern society, traffic volume continues to increase in most countries worldwide, leading to an increase in the rate of pavement damage Therefore, the real-time and highly accurate pavement damage detection and maintenance have become the current need. In this paper, an enhanced pavement damage detection method with CycleGAN and improved YOLOv5 algorithm is presented. We selected 7644 self-collected images of pavement damage samples as the initial dataset and augmented it by CycleGAN. Due to a substantial difference between the images generated by CycleGAN and real road images, we proposed a data enhancement method based on an improved Scharr filter, CycleGAN, and Laplacian pyramid. To improve the target recognition effect on a complex background and solve the problem that the spatial pyramid pooling-fast module in the YOLOv5 network cannot handle multiscale targets, we introduced the convolutional block attention module attention mechanism and proposed the atrous spatial pyramid pooling with squeeze-and-excitation structure. In addition, we optimized the loss function of YOLOv5 by replacing the CIoU with EIoU. The experimental results showed that our algorithm achieved a precision of 0.872, recall of 0.854, and mean average precision@0.5 of 0.882 in detecting three main types of pavement damage: cracks, potholes, and patching. On the GPU, its frames per second reached 68, meeting the requirements for real-time detection. Its overall performance even exceeded the current more advanced YOLOv7 and achieved good results in practical applications, providing a basis for decision-making in pavement damage detection and prevention.

K. Ishola, K. Adeyemo, Mutiu Abiodun Kareem et al.

The mining waste deposit such as iron ore tailings (IOT) in Nigeria is a menace to the environment by constituting a nuisance to the mining industry, and its effect on lateritic soil as a compaction and plasticity reduction material for road pavement is considered in this study. The used natural lateritic soil was classified as A-7-6(11) or CL by the American Association of State Highway and Transportation Officials (AASHTO) and Unified Soil Classification System (USCS) respectively. Up to 16% of the soil’s dry weight was modified with iron ore tailings (IOT). Studying the effects of IOT on the altered soil focused on its cation exchange capacity, plasticity, compaction properties and California bearing ratio (CBR). British Standard Light (BSL) energy was used for the compaction process. However, the results of regression analysis showed that the optimum moisture content had a substantial impact on the soil CBR values. The results of the tests show that as the IOT content increased, plasticity and compaction characteristic values increased. Although an optimal 10% IOT treatment of lateritic soil significantly improved its strength properties, the plasticity characteristics recorded exceeded the requirement specified by the Nigerian General Specifications for direct use as a subbase or base material. Thus, reusing 10% iron ore tailings as filling materials required additional percentages of other pozzolanic materials to lessen the environmental problems associated with their deposition.

D. Melese, Belete Aymelo, Tewodros Weldesenbet et al.

Black cotton soil is highly susceptible to volume change due to moisture fluctuations. This leads to the deformation of structures built on such soil. Therefore, the aim of this study is to improve the soil-bearing capacity and deformation analysis of black cotton soil. The laboratory tests were done according to the American Association State of highway and Transport Official (AASHTO) and the American Society for Testing and Materials (ASTM). These tests were natural moisture content, grain size distribution, X-ray diffraction test, Atterberg limit test, modified compaction, California bearing ratio, and triaxial test. Soil sample was stabilized with a ratio of 0%, 4%, 8%, 12%, and 16% of brick dust and 0%, 1%, 3%, 5%, and 7% of lime, respectively. The result of the laboratory test at the optimum percentage of 12% brick dust and 5% lime shows that the liquid limit improved from 93.2% to 67.5%, plastic limit improved from 48.71%, to 58.2%. The optimum moisture content improved from 26.76 to18.5% and Maximum dry density improved from 1.42 g/cm3 to 1.58 g/cm3. The California bearing ratio improved from 1.29%, to 13.6%. The deformation analysis result shows that at optimum percentage of stabilizing agent, the deformation reduced from 2.087 mm to 0.973 mm. Therefore, brick dust-lime soil stabilization shows the promising improvement of weak subgrade soil.

N. Consoli, Aziz Tebechrani Neto, Aghileh Khajeh et al.

Ze Shi Li, Nowshin Nawar Arony, Kezia Devathasan et al.

Capstone courses in undergraduate software engineering are a critical final milestone for students. These courses allow students to create a software solution and demonstrate the knowledge they accumulated in their degrees. However, a typical capstone project team is small containing no more than 5 students and function independently from other teams. To better reflect real-world software development and meet industry demands, we introduce in this paper our novel capstone course. Each student was assigned to a large-scale, multi-team (i.e., company) of up to 20 students to collaboratively build software. Students placed in a company gained first-hand experiences with respect to multi-team coordination, integration, communication, agile, and teamwork to build a microservices based project. Furthermore, each company was required to implement plug-and-play so that their services would be compatible with another company, thereby sharing common APIs. Through developing the product in autonomous sub-teams, the students enhanced not only their technical abilities but also their soft skills such as communication and coordination. More importantly, experiencing the challenges that arose from the multi-team project trained students to realize the pitfalls and advantages of organizational culture. Among many lessons learned from this course experience, students learned the critical importance of building team trust. We provide detailed information about our course structure, lessons learned, and propose recommendations for other universities and programs. Our work concerns educators interested in launching similar capstone projects so that students in other institutions can reap the benefits of large-scale, multi-team development