Hasil untuk "Highway engineering. Roads and pavements"

Piotr A. Wrzecioniarz

The practical space age began in the 1950s. Its origins are well known. It is generally accepted that the broader concept of modern space commercialization emerged at the beginning of the 21st century [3]. Development will continue in both the civilian and military spheres, as evidenced by the works included in this issue of "Przegląd Komunikacyjny" [1, 2]. A new industry is developing before our eyes. Countries wishing to build this type of business must not only provide adequate financial resources but also contribute to the training of human resources in the field of advanced technologies. The development of space education in Poland to date has been spontaneous. Pioneers recognized emerging opportunities and undertook initiatives based on existing knowledge from other areas of human education and activity. For example, during a classic graduate seminar at the Faculty of Mechanical Engineering at Wrocław University of Science and Technology, "Future-oriented" fields of study were defined. In 2000, one of the graduate students at the time became interested in space issues and, along with colleagues from other institutions, became a founding member of the "Mars Society Polska," originally established by Robert Zubrin from the USA in 1998, focusing on Mars colonization and exploration projects. The then-student is currently among the founding members of the Mars Society.

Xing Gong, Quantao Liu, Haiqin Xu et al.

Isocyanate and its products are playing an increasingly important role in the high-performance development of asphalt pavement, but researchers have always focused on polyurethane (PU), one of the isocyanate products, and neglected the other roles of isocyanate-based materials in asphalt pavement. The application of isocyanate-based materials in asphalt pavement is still in the exploratory stage, and the research direction is not clear. It is necessary to summarize and propose research directions for the application of isocyanate-based materials in asphalt pavement. Therefore, this paper reviews the application of isocyanate-based materials in asphalt pavement, classifies the products synthesized from isocyanate for asphalt binder, introduces the application effects of different isocyanate-based materials in asphalt binder, and analyzes the limitations of each material. Meanwhile, the other roles of isocyanate-based materials in asphalt pavement, such as coating materials and adhesive materials, are summarized. Finally, the development direction of isocyanate-based materials in asphalt pavement is prospected. Isocyanate-based materials are expected to significantly increase the service life of asphalt pavement because of their excellent properties. With the advancement of technology, the application of isocyanate-based materials will become more and more common, promoting the sustainable development of road construction. This paper can provide a reference for the development and application of isocyanate-based materials in asphalt pavement.

Aldona Kowalczyk

Abstract: The article discusses the judgment of the Court of Justice of the European Union (CJEU) issued on 22 October 2024 in case C-653/22 (the so-called Kolin case), which presents important findings for the practice of awarding public contracts in the EU. This ruling sheds new light on the participation of entities from non-EU countries in public procurement procedures, particularly those countries that have not concluded international agreements with the EU guaranteeing reciprocal and equal access to public procurement markets. Most importantly, the Kolin case judgment highlights the significant role of contracting authorities, who may restrict access for entities from such countries in specific public procurement procedures in Poland or other EU Member States. Keywords: Court of Justice of the European Union; Public procurement; Third-country access; Public procurement directives

Soumya Ranjan Baral, Anwesha Rath, Hemanta Kumar Behera et al.

In this study, reclaimed asphalt pavement (RAP) used in different percentages in hot mix asphalt (HMA) and warm mix asphalt (WMA) were tested for moisture, fracture and rutting resistance adding hydrogenated castor oil flakes (HCOF) as rejuvenating agent. Volumetric and Marshall parameters were evaluated for both types of mixtures. Addition of 5% of HCOF by weight of binder content in RAP found to restore properties of aged binder. WMA mix was made by adding 0.1% Zycotherm by weight of optimum binder content. Moisture, rutting and fracture damage performance were assessed utilizing indirect tensile strength, wheel tracking and semi-circular bending tests. The mix's tensile strength ratio increased by 2.3% in the HMA with RAP (HMA-R) mix compared to the WMA with RAP (WMA-R) mix at 10% RAP content. HMA mixes provide better resistance to rutting compared to WMA. However, 40% of RAP content HMA-R and WMA-R using HCOF rejuvenator shows greater rutting performance compared to other RAP mix. HMA-R mix fracture resistance increased by 18.14% compared to WMA-R mix when RAP content increases to 40%. Regression analyses were carried out to validate the strain energy found from fracture damage analysis of both HMA-R and WMA-R with R2 value more than 0.9. HMA-R protected moisture and fracture damage better than WMA-R. The rejuvenating efficiency of HCOF was further validated using Fourier transform infrared and microscopic analysis.

Nie Tian, Piergiorgio Tataranni, Cesare Sangiorgi

Enhancing rubber-bitumen compatibility is crucial to improve pavement performance and durability. To investigate the compatibility improvement between H2O2-activated waste crumb rubber (AWCR) and bitumen, coarse and fine waste crumb rubber (WCR) were treated and analyzed through multi-scale characterization and molecular simulation. Microstructure and chemical changes of WCR and AWCR were analyzed with scanning electron microscope (SEM), contact angle tests and Fourier transform infrared spectroscopy (FTIR). Compatibility was also indirectly evaluated through modified boiling tests and storage stability tests. Besides, molecular dynamics was used to explore the interaction between WCR/AWCR and bitumen. SEM, contact angle, and FTIR results showed bond breakage of CC and C–C and increased polar groups like –OH and –COOH in AWCR, resulting in a rougher texture and higher surface energy. Compared with WCR, AWCR showed a lower bitumen stripping rate after boiling, and the binder with AWCR also had a lower softening point difference and segregation rate after storage. Molecular dynamics simulations further confirmed that AWCR has a closer solubility parameter and higher binding energy to bitumen than WCR, reflected in a relatively slower diffusion rate. This study provides comprehensive evidence for an eco-friendly method of WCR surface treatment for more efficient recycling of tire rubber in asphalt pavements.

Haydar Gundogdu, Omer Faruk Cansiz, Mehmet Fatih Can

Road infrastructure is critical to the economic, social, and environmental sustainability of modern societies. This study compares classical methods (Multiple Linear Regression, Ridge, and LASSO) with soft computing techniques (Artificial Neural Networks, Fuzzy Logic, Random Forests, Gradient Boosting, Support Vector Machines, and Genetic Algorithms) for predicting road maintenance and repair costs. A comprehensive search has been conducted in Web of Science, and Scopus for studies published between January 2010 and March 2024. Boolean operators and specific key terms such as “road maintenance costs,” “soft computing,” and “classical prediction methods” have been used. The approach has been PRISMA-inspired but adapted for narrative review purposes; hence, no formal quality assessment or meta-analysis has been performed. Peer-reviewed journal articles have been included, while grey literature has been excluded to ensure methodological consistency. While classical methods offer simplicity and computational efficiency, they often fall short in addressing complex data structures such as non-linear relationships and multicollinearity. Conversely, soft computing techniques excel in modelling non-linear systems and managing uncertainties. Hybrid models combining classical and soft computing approaches enhance prediction accuracy by 20–30%, providing improved capabilities in modelling environmental factors. However, further research is required to evaluate their long-term performance and adaptability to diverse geographical conditions. This study highlights the theoretical advantages of hybrid models while offering practical solutions for sustainable infrastructure management. The findings provide policymakers and engineers with actionable insights, promoting efficient public resource use and sustainable development goals. Future research should focus on integrating IoT and big data analytics to address dynamic environmental variables, fostering innovation in infrastructure management.

Ivana Ban, Jacopo Bonari, Marco Paggi

Pavement surface textures obtained by a photogrammetry-based method for data acquisition and analysis are employed to investigate if related roughness descriptors are comparable to the frictional performance evaluated by finite element analysis. Pavement surface profiles are obtained from 3D digital surface models created with Close-Range Orthogonal Photogrammetry. To characterize the roughness features of analyzed profiles, selected texture parameters were calculated from the profile's geometry. The parameters values were compared to the frictional performance obtained by numerical simulations. Contact simulations are performed according to a dedicated finite element scheme where surface roughness is directly embedded into a special class of interface finite elements. Simulations were performed for different case scenarios and the obtained results showed a notable trend between roughness descriptors and friction performance, indicating a promising potential for this numerical method to be consistently employed to predict the frictional properties of actual pavement surface profiles.

Rashina Hoda

Agentic AI is poised to usher in a seismic paradigm shift in Software Engineering (SE). As technologists rush head-along to make agentic AI a reality, SE researchers are driven to establish agentic SE as a research area. While early visions of agentic SE are primarily focused on code-related activities, early empirical evidence calls for a consideration of a wider range of socio-technical activities and concerns to make it work in practice. This paper contributes to the emerging visions by: (a) recommending an expansion of its scope beyond code, toward a 'whole of process' vision, grounding it in SE foundations and evolution and emerging agentic SE frameworks, (b) proposing a preliminary set of values and principles to guide community efforts, and (c) sharing guidance on designing and using well-defined vocabulary for agentic SE. It is hoped that these ideas will encourage collaborations and steer the SE community toward laying strong foundations of agentic SE so it is not limited to enabling coding acceleration but becomes the next process-level paradigm shift.

Shiruo Hu, Wenbo Shan, Yingjia Li et al.

Hydro-Science and Engineering (Hydro-SE) is a critical and irreplaceable domain that secures human water supply, generates clean hydropower energy, and mitigates flood and drought disasters. Featuring multiple engineering objectives, Hydro-SE is an inherently interdisciplinary domain that integrates scientific knowledge with engineering expertise. This integration necessitates extensive expert collaboration in decision-making, which poses difficulties for intelligence. With the rapid advancement of large language models (LLMs), their potential application in the Hydro-SE domain is being increasingly explored. However, the knowledge and application abilities of LLMs in Hydro-SE have not been sufficiently evaluated. To address this issue, we propose the Hydro-SE LLM evaluation benchmark (Hydro-SE Bench), which contains 4,000 multiple-choice questions. Hydro-SE Bench covers nine subfields and enables evaluation of LLMs in aspects of basic conceptual knowledge, engineering application ability, and reasoning and calculation ability. The evaluation results on Hydro-SE Bench show that the accuracy values vary among 0.74 to 0.80 for commercial LLMs, and among 0.41 to 0.68 for small-parameter LLMs. While LLMs perform well in subfields closely related to natural and physical sciences, they struggle with domain-specific knowledge such as industry standards and hydraulic structures. Model scaling mainly improves reasoning and calculation abilities, but there is still great potential for LLMs to better handle problems in practical engineering application. This study highlights the strengths and weaknesses of LLMs for Hydro-SE tasks, providing model developers with clear training targets and Hydro-SE researchers with practical guidance for applying LLMs.

Blessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah et al.

This research introduces the first multimodal approach for pavement condition assessment, providing both quantitative Pavement Condition Index (PCI) predictions and qualitative descriptions. We introduce PaveCap, a novel framework for automated pavement condition assessment. The framework consists of two main parts: a Single-Shot PCI Estimation Network and a Dense Captioning Network. The PCI Estimation Network uses YOLOv8 for object detection, the Segment Anything Model (SAM) for zero-shot segmentation, and a four-layer convolutional neural network to predict PCI. The Dense Captioning Network uses a YOLOv8 backbone, a Transformer encoder-decoder architecture, and a convolutional feed-forward module to generate detailed descriptions of pavement conditions. To train and evaluate these networks, we developed a pavement dataset with bounding box annotations, textual annotations, and PCI values. The results of our PCI Estimation Network showed a strong positive correlation (0.70) between predicted and actual PCIs, demonstrating its effectiveness in automating condition assessment. Also, the Dense Captioning Network produced accurate pavement condition descriptions, evidenced by high BLEU (0.7445), GLEU (0.5893), and METEOR (0.7252) scores. Additionally, the dense captioning model handled complex scenarios well, even correcting some errors in the ground truth data. The framework developed here can greatly improve infrastructure management and decision18 making in pavement maintenance.

Linh Trinh, Ali Anwar, Siegfried Mercelis

Maintaining roads is crucial to economic growth and citizen well-being because roads are a vital means of transportation. In various countries, the inspection of road surfaces is still done manually, however, to automate it, research interest is now focused on detecting the road surface defects via the visual data. While, previous research has been focused on deep learning methods which tend to process the entire image and leads to heavy computational cost. In this study, we focus our attention on improving the classification performance while keeping the computational cost of our solution low. Instead of processing the whole image, we introduce a segmentation model to only focus the downstream classification model to the road surface in the image. Furthermore, we employ contrastive learning during model training to improve the road surface condition classification. Our experiments on the public RTK dataset demonstrate a significant improvement in our proposed method when compared to previous works.

Essizewa Essowedeou Agate, Nyomboi Timothy, Ambassah O. Nathaniel et al.

Expansive soils such as Black Cotton Soils (BCS) present significant challenges for road subgrade construction due to their high plasticity, swelling potential, and low strength. This study explores a triphasic stabilization method using Bamboo Charcoal (BC), Quarry Dust (QD), and Lime (L) to enhance the engineering properties of BCS for rural road applications. Initial soil characterization involved standard tests, including Atterberg limits, compaction, and Californian Bearing Ratio (CBR) assessments. The soil was treated with varying BC proportions (5% to 35% at 5% intervals) in the initial phase, leading to a progressive reduction in the Plasticity Index (PI) and swell index and an enhancement in the CBR up to 20% BC content. This further resulted in a soaked CBR value of 2.7%. In the second phase, additional treatment combined with BC and QD, incorporating diverse QD proportions (4% to 24%) relative to the optimal BC content. This further improved the CBR to 7.7% at 12% QD, but the PI exhibited a non-linear trend. Finally, 5% lime was introduced in the final phase. This minimized the PI to 11.2% and significantly increased the CBR to 19%. The optimal combination of 20% BC, 12% QD, and 5% Lime achieved optimal plasticity, compaction, and strength characteristics, demonstrating the viability of this approach for transforming BCS into a sustainable and cost-effective alternative for rural road subgrade construction.

Thomas Fork, Francesco Camozzi, Xiao-Yu Fu et al.

We present an approach for predictive braking of a four-wheeled vehicle on a nonplanar road. Our main contribution is a methodology to consider friction and road contact safety on general smooth road geometry. We use this to develop an active safety system to preemptively reduce vehicle speed for upcoming road geometry, such as off-camber turns. Our system may be used for human-driven or autonomous vehicles and we demonstrate it with a simulated ADAS scenario. We show that loss of control due to driver error on nonplanar roads can be mitigated by our approach.

Vytenis Puzonas, Alfredas Laurinavičius, Lina Juknevičiūtė-Žilinskienė

The study focused on the lighting in road tunnels within the city of Vilnius. The condition of the lighting was assessed both visually and through measurements of road surface illumination (brightness). High-quality lighting in road tunnels is crucial for ensuring safe and optimal conditions for car travel. Well-designed lighting reduces stress, enhances information visibility for drivers, ensures uniform visibility throughout the tunnel, and promotes efficient energy use. After analysing the data, the required road surface luminance was calculated following the technical regulations applicable to road tunnel lighting design in other countries. The results suggest a need to update the lighting in existing road tunnels by adopting new types of lamps, adjusting their arrangement, and enhancing the physical characteristics of the tunnels.

Larysa Bodnar, Liuda Panibratets, Oleksandr Kanin et al.

Introduction. Questions of priority of road bridges (hereinafter referred to as bridges) arise when drafting various types of long -term, medium -term or short -term strategies, programs and plans for routine maintenance, current and major repairs, reconstruction, replacement of emergency or destroyed bridges in a limited amount (financial, production capacity, terms, etc.). As a result of the priority, the list (or lists) of the bridges is determined by some criteria, which require the application of certain measures, depending on the place of bridges in this list. Problem Statement. One of the prerequisites for effective management of the operation of bridges on the network of roads of Ukraine during their use is to improve the evaluation of bridges rating in the Software complex «Analytical expert system of bridge management» (SC AESUM), taking into account world experience, requirements of regulatory and technical documents and specific results for technical assessment of bridges.

Anne de Bortoli, Maxime Agez

Industries struggle to build robust environmental transition plans as they lack the tools to quantify their ecological responsibility over their value chain. Companies mostly turn to sole greenhouse gas (GHG) emissions reporting or time-intensive Life Cycle Assessment (LCA), while Environmentally-Extended Input-Output (EEIO) analysis is more efficient on a wider scale. We illustrate EEIO analysis usefulness to sketch transition plans on the example of Canada s road industry - estimation of national environmental contributions, most important environmental issues, main potential transition levers of the sector, and metrics prioritization for green purchase plans). To do so, openIO-Canada, a new Canadian EEIO database, coupled with IMPACT World plus v1.30-1.48 characterization method, provides a multicriteria environmental diagnosis of Canada s economy. The road industry generates a limited impact (0.5-1.8 percent) but must reduce the environmental burden from material purchases - mainly concrete and asphalt products - through green purchase plans and eco-design and invest in new machinery powered with cleaner energies such as low-carbon electricity or bioenergies. EEIO analysis also captures impacts often neglected in process-based pavement LCAs - amortization of capital goods, staff consumptions, and services - and shows some substantial impacts advocating for enlarging system boundaries in standard LCA. Yet, pavement construction and maintenance only explain 5 percent of the life cycle carbon footprint of Canada s road network, against 95 percent for the roads usage. Thereby, a carbon-neutral pathway for the road industry must first focus on reducing vehicle consumption and wear through better design and maintenance of roads (...)

Arian Prabowo, Hao Xue, Wei Shao et al.

New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads) are rarely explored. In this paper, we introduce a novel setup called a spatio-temporal (ST) split to evaluate the models' capabilities to generalize to unseen roads. In this setup, the models are trained on data from a sample of roads, but tested on roads not seen in the training data. Moreover, we also present a novel framework called Spatial Contrastive Pre-Training (SCPT) where we introduce a spatial encoder module to extract latent features from unseen roads during inference time. This spatial encoder is pre-trained using contrastive learning. During inference, the spatial encoder only requires two days of traffic data on the new roads and does not require any re-training. We also show that the output from the spatial encoder can be used effectively to infer latent node embeddings on unseen roads during inference time. The SCPT framework also incorporates a new layer, named the spatially gated addition (SGA) layer, to effectively combine the latent features from the output of the spatial encoder to existing backbones. Additionally, since there is limited data on the unseen roads, we argue that it is better to decouple traffic signals to trivial-to-capture periodic signals and difficult-to-capture Markovian signals, and for the spatial encoder to only learn the Markovian signals. Finally, we empirically evaluated SCPT using the ST split setup on four real-world datasets. The results showed that adding SCPT to a backbone consistently improves forecasting performance on unseen roads. More importantly, the improvements are greater when forecasting further into the future. The codes are available on GitHub: https://github.com/cruiseresearchgroup/forecasting-on-new-roads .

Neema Jakisa Owor, Hang Du, Abdulateef Daud et al.

The Pavement Condition Index (PCI) is a widely used metric for evaluating pavement performance based on the type, extent and severity of distresses detected on a pavement surface. In recent times, significant progress has been made in utilizing deep-learning approaches to automate PCI estimation process. However, the current approaches rely on at least two separate models to estimate PCI values -- one model dedicated to determining the type and extent and another for estimating their severity. This approach presents several challenges, including complexities, high computational resource demands, and maintenance burdens that necessitate careful consideration and resolution. To overcome these challenges, the current study develops a unified multi-tasking model that predicts the PCI directly from a top-down pavement image. The proposed architecture is a multi-task model composed of one encoder for feature extraction and four decoders to handle specific tasks: two detection heads, one segmentation head and one PCI estimation head. By multitasking, we are able to extract features from the detection and segmentation heads for automatically estimating the PCI directly from the images. The model performs very well on our benchmarked and open pavement distress dataset that is annotated for multitask learning (the first of its kind). To our best knowledge, this is the first work that can estimate PCI directly from an image at real time speeds while maintaining excellent accuracy on all related tasks for crack detection and segmentation.

Xudong Wang, Lijun Sun

Highway traffic states data collected from a network of sensors can be considered a high-dimensional nonlinear dynamical system. In this paper, we develop a novel data-driven method -- anti-circulant dynamic mode decomposition with sparsity-promoting (circDMDsp) -- to study the dynamics of highway traffic speed data. Particularly, circDMDsp addresses several issues that hinder the application of existing DMD models: limited spatial dimension, presence of both recurrent and non-recurrent patterns, high level of noise, and known mode stability. The proposed circDMDsp framework allows us to numerically extract spatial-temporal coherent structures with physical meanings/interpretations: the dynamic modes reflect coherent spatial bases, and the corresponding temporal patterns capture the temporal oscillation/evolution of these dynamic modes. Our result based on Seattle highway loop detector data showcases that traffic speed data is governed by a set of periodic components, e.g., mean pattern, daily pattern, and weekly pattern, and each of them has a unique spatial structure. The spatiotemporal patterns can also be used to recover/denoise observed data and predict future values at any timestamp by extrapolating the temporal Vandermonde matrix. Our experiments also demonstrate that the proposed circDMDsp framework is more accurate and robust in data reconstruction and prediction than other DMD-based models.

Ertugrul Basar

Noise: an enemy to be dealt with and a major factor limiting communication system performance. However, what if there is gold in that garbage? In conventional engineering, our focus is primarily on eliminating, suppressing, combating, or even ignoring noise and its detrimental impacts. Conversely, could we exploit it similarly to biology, which utilizes noise-alike carrier signals to convey information? In this context, the utilization of noise, or noise-alike signals in general, has been put forward as a means to realize unconditionally secure communication systems in the future. In this tutorial article, we begin by tracing the origins of thermal noise-based communication and highlighting one of its significant applications for ensuring unconditionally secure networks: the Kirchhoff-law-Johnson-noise (KLJN) secure key exchange scheme. We then delve into the inherent challenges tied to secure communication and discuss the imperative need for physics-based key distribution schemes in pursuit of unconditional security. Concurrently, we provide a concise overview of quantum key distribution (QKD) schemes and draw comparisons with their KLJN-based counterparts. Finally, extending beyond wired communication loops, we explore the transmission of noise signals over-the-air and evaluate their potential for stealth and secure wireless communication systems.