Hasil untuk "Highway engineering. Roads and pavements"

Diego Firmenich, Leandro Antonelli, Bruno Pazos et al.

User stories are one of the most widely used artifacts in the software industry to define functional requirements. In parallel, the use of high-fidelity mockups facilitates end-user participation in defining their needs. In this work, we explore how combining these techniques with large language models (LLMs) enables agile and automated generation of user stories from mockups. To this end, we present a case study that analyzes the ability of LLMs to extract user stories from high-fidelity mockups, both with and without the inclusion of a glossary of the Language Extended Lexicon (LEL) in the prompts. Our results demonstrate that incorporating the LEL significantly enhances the accuracy and suitability of the generated user stories. This approach represents a step forward in the integration of AI into requirements engineering, with the potential to improve communication between users and developers.

Volodymyr Abramov

Introduction. Structures made of metal corrugated structures (MCS) – culverts, small and medium-sized bridges, overpasses – are quite common modern objects of transport and hydraulic construction in many countries of the world. At the proper level of design, such road constructions are much more efficient than constructions in terms of their technical and economic indicators of the same class from massive reinforced concrete and metal structures. Therefore, it is important to have more ideas about the operation of these rational and specific objects under the road embankment in the “building – soil” system and the corresponding more correct methodology for their calculation. Problems. In the part of calculations, the existing regulatory and methodological documentation for the design of road structures with MCS is not sufficiently complete and does not reflect to the required degree the peculiarities of the operation of such structures of various cross-section forms. To a certain extent, this complicates the design and slows down the spread of such structures from MCS in the practice of road construction. Purpose and task. To show the possibilities of compiling the method of calculation of road structures from MCS according to the deformation criterion, which is most relevant for flexible vaulting under the embankment, using simple and understandable calculation schemes of various cross-section forms and generally accepted laws of construction mechanics and mechanics of soils and underground structures - the main goal and task of this work.

Min Wang, Xin Yu, Chen Chen

The occurrence of top-down (TD) cracking has gradually become a prevalent issue in semi-rigid base asphalt pavements after prolonged service. A coupled simulation model integrating the finite difference method (FDM) and discrete element method (DEM) was employed to investigate the mechanical behavior of asphalt pavement containing a pre-existing TD crack. The mesoscopic parameters of the model were calibrated based on the mixture modulus and the static mechanical response on the MLS66 test road. Finally, an analysis was performed to assess how variations in TD crack depth and longitudinal length affect the distribution patterns of transverse tensile stress, vertical shear stress, and vertical compressive stress. The results indicate that the vertical propagation of TD crack significantly increases both the tensile stress value and range on the middle surface, while the longitudinal development of TD crack has minimal impact. This phenomenon may result in more severe fatigue failure on the middle surface. With the vertical and longitudinal development of TD crack, the vertical shear stress and compressive stress show obvious ''two-stage'' characteristics. When the crack's vertical length reaches 40 ​mm, there is a sharp increase in stress on the upper surface. As the crack continues to propagate vertically, the growth of stress on the upper surface becomes negligible, while the stress in the middle and lower layers increased significantly. Conversely, for longitudinal development of TD crack, any changes in stress are insignificant when their length is less than 180 ​mm; however, as they continue to develop longitudinally beyond this threshold, there is a sharp increase in stress levels. These findings hold great significance for understanding pavement structure deterioration and maintenance behavior associated with TD crack.

Jiayv Jing, Xu Yang, Hang Cheng et al.

Pavement distress detection plays a pivotal role in ensuring roadway safety, serviceability, and cost-effective infrastructure management. With rapid advancements in intelligent transportation systems, computer vision, and sensing technologies, non-contact detection approaches based on images and point clouds have become increasingly prominent due to their efficiency, objectivity, and scalability. This review systematically examines both image-based and point cloud-based methodologies, structured along the complete detection pipeline encompassing data acquisition, preprocessing, distress extraction, and geometric quantification. Image-based techniques rely on visual cues, such as texture, color, and edge continuity, to identify surface-level anomalies efficiently, benefiting from mature deep learning frameworks for classification, object detection, and pixel-level segmentation. In contrast, point cloud-based methods capture rich three-dimensional geometric and structural information, enabling detailed modeling of crack depth, rutting deformation, and surface irregularities. Although each modality can independently achieve satisfactory performance, their complementary strengths have driven a growing trend toward hybrid frameworks, combining image-based rapid screening with point cloud-based precision modeling, to enhance detection accuracy, robustness, and adaptability across varying conditions. Furthermore, this paper highlights persistent challenges, including multimodal data fusion, high equipment and labeling costs, computational complexity, and the need for standardized benchmarks. By synthesizing current progress and identifying key technical bottlenecks, this review provides a comprehensive foundation and forward-looking perspective for developing intelligent, efficient, and scalable pavement distress detection systems.

Baoyang Yu, Zhizhang Zhou, Chunshuai Zhang et al.

Research on the microscopic mechanisms of open-graded friction courses (OGFCs) is still in its early stages, and the specific effects of various factors on the fatigue performance of OGFCs have not been fully explored. This study investigates the effects of oil-stone ratios, void fractions, and maximum nominal particle sizes on the fatigue life of OGFCs at the macroscopic and microscopic scales. At the macroscopic level, indirect tensile fatigue tests were conducted on OGFC specimens. At the microscopic level, a three-dimensional (3D) reconstruction model of OGFC was developed using computed tomography (CT) and image processing techniques. Additionally, a 3D randomized aggregate model was developed using the Monte Carlo method and an aggregate random placement algorithm. Virtual splitting and fatigue tests were conducted to analyse the correlation between virtual and experimental macroscopic tests. The results showed that the splitting strength and fatigue life of the OGFC increased at higher oil-stone ratios but decreased at higher void fractions and larger nominal maximum particle sizes. The variation in the results of the virtual splitting fatigue tests derived from the CT reconstruction model and the experimental results was only 9–11%, indicating a strong correlation between the two approaches.

Morgan M. Fong, Hilary Egan, Marc Day et al.

Background: Harnessing advanced computing for scientific discovery and technological innovation demands scientists and engineers well-versed in both domain science and computational science and engineering (CSE). However, few universities provide access to both integrated domain science/CSE cross-training and Top-500 High-Performance Computing (HPC) facilities. National laboratories offer internship opportunities capable of developing these skills. Purpose: This student presents an evaluation of federally-funded postgraduate internship outcomes at a national laboratory. This study seeks to answer three questions: 1) What computational skills, research skills, and professional skills do students improve through internships at the selected national laboratory. 2) Do students gain knowledge in domain science topics through their internships. 3) Do students' career interests change after these internships? Design/Method: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate that participants improve CSE skills and domain science knowledge, and are more interested in working at national labs. Participants go on to degree programs and positions in relevant domain science topics after their internships. Conclusions: We show that national laboratory internships are an opportunity for students to build CSE skills that may not be available at all institutions. We also show a growth in domain science skills during their internships through direct exposure to research topics. The survey instrument and approach used may be adapted to other studies to measure the impact of postgraduate internships in multiple disciplines and internship settings.

Shahin Shabani, Jalal Ayoubinejad, Nassir Baradaran Rahmanian

The sliding window method is a road network screening approach commonly used for identifying black spots. Previous studies have indicated that the selection of window length significantly impacts the black spot identification process. This research proposes a new method that optimizes the sliding window framework by examining its characteristics. The optimization methodology employed in this study is as follows: Firstly, the road is segmented, and for each segment, different scenarios of window lengths are chosen using the Density-Based Spatial Clustering of Applications with Noise algorithm. Next, a Safety Performance Function is developed to calculate the predicted and expected number of crashes, as well as the Potential Safety Improvement, for each window movement across all selected scenarios within the segment. Subsequently, the average differences are calculated using the analysis of variance, and the window length with the lowest dispersion of difference values from the mean is identified as the optimal length for each segment. The case study yielded noteworthy results, indicating that the utilization of the sliding window with optimal lengths led to the identification of 122 high-risk black spot-candidates. These points exhibit a higher crash density, effective length, and greater value in quantitative evaluation tests compared to the results obtained using windows with common fixed lengths.

Jiangmiao Yu, Zengyao Lin, Guilian Zou et al.

The application of reclaimed asphalt pavement (RAP) and reclaimed asphalt shingles (RAS) on asphalt pavement can reduce the asphalt paving cost, conserve energy and protect the environment. However, the use of high contents of RAP and RAS in asphalt pavement may lead to durability issues, especially the fatigue cracking and thermal cracking. It is necessary to conduct a series of analyses on asphalt mixtures containing high RAP and RAS, and seek methods to enhance their long-term performance. This paper provides a comprehensive over-view of the long-term performance of recycled asphalt mixtures containing high contents of RAP and RAS. The findings in this research show that rutting resistance of high recycled asphalt mixtures is not a concern, whereas their resistance to fatigue and thermal cracking is not conclusive. Recycling agents can be used to improve the thermal cracking resistance of high recycled asphalt mixtures. An optimum decision on recycling agents will improve the durability properties of high recycled asphalt mixtures. It is recommended that to use a balanced mixture design approach with testing of the blended asphalt binders will provide better understanding of long-term performance of recycled asphalt mixtures containing high RAP and RAS.

Mykola Harkusha

Introduction. The reliability and durability of culverts are affected by static and dynamic loads from vehicles and temperature and climatic influences, the destructive force of water flow, abrasive destruction, corrosion of the material, shortcomings in the choice of structures and unsatisfactory design, all of which cause the premature collapse of the structure. Today, culverts made of metal corrugated structures (hereinafter referred to as metal corrugated structures) are widely used. The well-known advantages that justify the choice of such a solution are mainly a short term and a relatively low cost of construction, construction provided that the appropriate technological regime is observed does not cause any particular problems. Therefore, there is a need to develop a recommendation for the restoration of culverts using metal corrugated structures. Problems. It was established that there is a need to restore culverts without stopping the movement of vehicles. Goal. It consists in offering approaches for the restoration of culverts using metal corrugated structures. Results. Recommendations for the restoration of culverts using metal corrugated structures are offered, with recommendations for the selection of materials and the organization and technology of culvert restoration works.

Zishun Zheng, Yihan Wang, Yuan Lin

To enable autonomous vehicles to perform discretionary lane change amidst the random traffic flow on highways, this paper introduces a decision-making and control method for vehicle lane change based on Model Predictive Control (MPC). This approach divides the driving control of vehicles on highways into two parts: lane-change decision and lane-change control, both of which are solved using the MPC method. In the lanechange decision module, the minimum driving costs for each lane are computed and compared by solving the MPC problem to make lane-change decisions. In the lane-change control module, a dynamic bicycle model is incorporated, and a multi-objective cost function is designed to obtain the optimal control inputs for the lane-change process. Additionally, A long-short term memory (LSTM) model is used to predict the trajectories of surrounding vehicles for both the MPC decision and control modules. The proposed lane-change decision and control method is simulated and validated in a driving simulator under random highway traffic conditions.

Yuhui Wang, Miroslav Strupl, Francesco Faccio et al.

Learning from multi-step off-policy data collected by a set of policies is a core problem of reinforcement learning (RL). Approaches based on importance sampling (IS) often suffer from large variances due to products of IS ratios. Typical IS-free methods, such as $n$-step Q-learning, look ahead for $n$ time steps along the trajectory of actions (where $n$ is called the lookahead depth) and utilize off-policy data directly without any additional adjustment. They work well for proper choices of $n$. We show, however, that such IS-free methods underestimate the optimal value function (VF), especially for large $n$, restricting their capacity to efficiently utilize information from distant future time steps. To overcome this problem, we introduce a novel, IS-free, multi-step off-policy method that avoids the underestimation issue and converges to the optimal VF. At its core lies a simple but non-trivial \emph{highway gate}, which controls the information flow from the distant future by comparing it to a threshold. The highway gate guarantees convergence to the optimal VF for arbitrary $n$ and arbitrary behavioral policies. It gives rise to a novel family of off-policy RL algorithms that safely learn even when $n$ is very large, facilitating rapid credit assignment from the far future to the past. On tasks with greatly delayed rewards, including video games where the reward is given only at the end of the game, our new methods outperform many existing multi-step off-policy algorithms.

Francisco Iturralde, Miguel Andrés Andrés Guerra

The construction industry faces the challenge to balance the growing demand for new infrastructure and the sustainable use of resources that are generated throughout the different stages of civil infrastructure work. Other industries use materials that leave behind great amount of waste, that often their biodegradation and recycling processes are complex. To find avenues to adapt or incorporate such materials into the productive cycle, characteristics and mechanical properties studies are necessary. For example, due to the large consumption of coconut water, the elimination of these resources has generated major problems such as environmental pollution, methane emissions, insect outbreaks, reduction of the useful life of landfills and increased costs of maintenance of cities. However, several research have demonstrated that coconut fibers and shells may be utilized in concrete applications. This study provides an in-depth review of the literature to learn about the current state of the art in the use of coconut fibers and shells as aggregates in pavement mixtures. Thanks to new technologies, it has been possible to carry out a more in-depth analysis regarding the use of environmentally friendly materials to be used in civil infrastructure works. The incorporation of the shell and coconut fibers are aimed to determine its influence on asphalt mix, considering the amount to be used and its long-term performance and to provide a new technology engineering in the construction of sustainable roads and highways using available ecological material. Researchers propose and agenda for future work for both researchers and practitioners.

Bin Xu, Aodong Gao, Zhou Chen et al.

With the rapid growth of road transportation, the increase in road subgrade and pavement diseases has become a pressing issue, requiring the development of cost-effective filling materials that meet both strength and economic requirements. Foam lightweight soil, as a novel construction material, offers excellent characteristics such as adjustability in density and strength, high fluidity, and self-supporting capabilities. It has been widely utilized in various engineering applications, including road subgrade backfilling and retaining wall fillings. However, the conventional application of foam lightweight soil, predominantly cement-based, has raised concerns about pollution and high energy consumption due to large cement dosages. To address this issue, this study proposes the integration of phosphogypsum, a byproduct of wet-process phosphoric acid production, into foam lightweight soil. Phosphogypsum has a significant annual discharge and accumulation, but its comprehensive utilization rate remains relatively low. The research investigates the combination of phosphogypsum and foam lightweight soil by introducing mineral admixtures such as microsilica and slag powder to improve early strength development and reduce the influence of fluoride impurities on early strength. The optimal mix proportions for two types of foam lightweight soil, namely phosphogypsum cement microsilica foam (PGCF) and phosphogypsum cement slag powder foam (PGCS), were determined based on single-factor tests. The key parameters considered for optimization were water–binder ratio, foam content, and phosphogypsum dosage. The findings indicate that both PGCF and PGCS foam lightweight soil possess superior mechanical properties and thermal conductivity. By incorporating phosphogypsum into the mix, the early strength development of foam lightweight soil is effectively improved. Moreover, with suitable mix proportions, the maximum phosphogypsum dosage can be achieved, demonstrating potential economic and environmental benefits. In conclusion, this research provides valuable insights into the effective utilization of phosphogypsum in foam lightweight soil, offering a promising solution for the challenges associated with phosphogypsum disposal and the demand for sustainable construction materials in highway engineering.

Оleksii Sokolov

Introduction. The composition of the grading of the mineral part of the asphalt mixture significantly affects the properties of road asphalt, namely its strength, roughness, durability, stability, reliability, quality, etc., especially when using secondary industrial waste. The designing of asphalt mixture grading (hereinafter referred to as AM) involves the calculation of its parameters that, among other things, meet the requirements of Tables 6 and 7 of DSTU B V.2.7-119:2011 [1]. Designing the aggregate composition and binder content of an AM that meets the specification requirements is a lengthy trial-and-error procedure, and success in designing an AM largely depends on the designer’s experience. This difficulty can be overcome by the development and implementation of computer software for designing the optimal AM parameters to obtain the desired properties and control them. In general, three main approaches have been proposed to computerize the computations for processing laboratory test results and optimizing the parameters of the AM: 1) Excel spreadsheets for performing a volumetric analysis of AM (Asphalt Mix Design Tools), for example, according to the methodology of Part 5 of the manual [3]; 2) optimization of the process of designing the optimal AM using artificial neural networks (hereinafter referred to as ANN) and genetic algorithm (hereinafter referred to as GA), for example, [4]; 3) improving the design and management of AM production by means of computer simulation modeling and optimization of AM parameters in real-time [5]. Excel spreadsheets are the most common in the construction industry worldwide. They are offered on the software market or developed on their own by individual specialists who are familiar with Excel. Microsoft Excel includes a so-called Solver, which uses a GA to find the optimal number of components of the mineral part of the AM. In addition, the Visual Basic for Application (VBA) programming language available in Excel makes it possible to create programs that correspond to the complexity of the problem of optimizing the parameters of the AM and have a convenient user interface. A modern approach to optimizing the process of AM designing based on ANN models of various types, for example, is proposed in [4]. Artificial intelligence tools have gained popularity in recent years.

Noah Goodall

Transportation agencies routinely collect weather data to support maintenance activities. With the proliferation of smartphones, many agencies have begun using crowdsourced data in operations. This study evaluates a novel unplowed roads dataset from the largest crowdsourced transportation data provider Waze. User-reported unplowed roads in Virginia were compared to national and state weather data for accuracy, and found 81% of reports were near known snow events, with false positives occurring at a regular rate of approximately 10 per day statewide. Reports were largely located on primary roads, limiting the usefulness for transportation agencies who may be most concerned with poorly monitored secondary roads. An effort to encourage unplowed road reporting in Waze through targeted messages on social media did not increase participation. Low reporting may be due to the feature's novelty, recent mild winters, or COVID-19 school and business closures.

Neeloy Chakraborty, Aamir Hasan, Shuijing Liu et al.

In autonomous driving, detection of abnormal driving behaviors is essential to ensure the safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that modeling interactions between agents improves detection accuracy, but certain abnormal behaviors where structured road information is paramount are poorly identified, such as wrong-way and off-road driving. We propose a novel unsupervised framework for highway anomaly detection named Structural Attention-Based Recurrent VAE (SABeR-VAE), which explicitly uses the structure of the environment to aid anomaly identification. Specifically, we use a vehicle self-attention module to learn the relations among vehicles on a road, and a separate lane-vehicle attention module to model the importance of permissible lanes to aid in trajectory prediction. Conditioned on the attention modules' outputs, a recurrent encoder-decoder architecture with a stochastic Koopman operator-propagated latent space predicts the next states of vehicles. Our model is trained end-to-end to minimize prediction loss on normal vehicle behaviors, and is deployed to detect anomalies in (ab)normal scenarios. By combining the heterogeneous vehicle and lane information, SABeR-VAE and its deterministic variant, SABeR-AE, improve abnormal AUPR by 18% and 25% respectively on the simulated MAAD highway dataset over STGAE-KDE. Furthermore, we show that the learned Koopman operator in SABeR-VAE enforces interpretable structure in the variational latent space. The results of our method indeed show that modeling environmental factors is essential to detecting a diverse set of anomalies in deployment. For code implementation, please visit https://sites.google.com/illinois.edu/saber-vae.

Shahrzad Pour, Amir Masoumi, Niels Skov Dujardin

Decision Support Systems for pavement and maintenance strategies have traditionally been designed as silos led to local optimum systems. Moreover, since big data usage didn't exist as result of Industry 4.0 as of today, DSSs were not initially designed adaptive to the sources of uncertainties led to rigid decisions. Motivated by the vulnerability of the road assets to the climate phenomena, this paper takes a visionary step towards introducing a Technology-Driven Adaptive Decision Support System for Integrated Pavement and Maintenance activities called TDADSS-IPM. As part of such DSS, a bottom-up risk assessment model is met via Bayesian Belief Networks (BBN) to realize the actual condition of the Danish roads due to weather condition. Such model fills the gaps in the knowledge domain and develops a platform that can be trained over time, and applied in real-time to the actual event.

Karol Wach

Abstract: The subject of the article is the issue of statutory concessions applied in public collective transport. The level of their differentiation in relation to bus and rail transport is a serious barrier to the tariff integration of these two means of transport. Hence, in the course of works on the amendment to the act on public collective transport, it seems advisable to attempt to standardize statutory concessions, as well as to include provisions relating to them to the indicated normative act. In the current legal state, the provisions regulating this issue have been included in at least a few acts. The adoption of the assumption assuming their unification should lead to simplification of the currently applicable system, and thus accelerate the process of ticket integration. Keywords: Tariff integration; Ticket; Statutory discount

V. H. Lad, D. A. Patel, K. A. Chauhan et al.

The resilience of a bridge is computed using different quantitative and qualitative assessment methodologies. However, the resilience score obtained by these assessment approaches is insufficient for the decision-makers for setting a priority level for bridges in need of resilience improvement. To address this issue, the present study develops a methodology using the data envelopment analysis (DEA) approach. A total of 12 bridges are selected as the decision-making units in the DEA model. This study considers the variables such as age, area, design high flood level, and finish road level of the bridge as inputs, and bridge resilience index as the output variable. Based on these variables, three frameworks are developed to compute the efficiency of bridge resilience. A variable return to scale with the output-oriented formulation of DEA is selected to compute the efficiency of bridge resilience in all three frameworks. Thus, the proposed methodology enables bridge owners to set a priority level for bridges in need of resilience improvement based on the scores of the assessment methodology.

Leonardo Castro González, M. E. Lárraga, J. Antonio del Río

Nowadays, methodologies coming from studying physical systems are being applied to the description of a wide variety of complex systems. In particular, one can study thermodynamical methods to describe the overall behavior of many systems, independent of the precise microscopic construction. In this paper, a real Mexican highway is studied as a cellular automata system using available official data released by the Mexican Government. The system studied is the Cuernavaca bypass which was modified in 2016. Official data allows to compare the highway before and after the modifications. As more complex thermodynamic variables such as entropy is difficult to define and measure in discrete traffic models, it is shown how other more simple variables such as the standard deviation can be enough to have a complete analysis of the system. More specifically, it is shown how standard deviation can be seen as a measure of order. Results from the study of the highway show how, taking a minimal measure such as ordering the transit of heavy trucks can reduce up to 32\% the travel time from one end to another. Otherwise, travel times stays practically constant with respect to the original system.