Abstract: This article provides an overview of concrete pavement surface finishing methods.
Various methods used since the first introduction of rigid cement concrete pavement
technologies are presented. It is concluded that different surface finishing methods
significantly impact operational characteristics, particularly noise emissions. Currently,
proven and optimal methods are recommended: grinding, grooving, and "exposed
aggregates."
Keywords: Concrete pavement; Texture; Grinding; Grooving; "Exposed Aggregate"
Highway engineering. Roads and pavements, Bridge engineering
Igor Muchowski, Bartosz Nowak, Daria Kassin
et al.
Abstract: The study concerns the application of Building Information Modelling (BIM) in the
process of railway infrastructure upgrading. The research site was railway line no. 218
Prabuty-Kwidzyn (Poland). The main aim was to verify integration of BIM tools, particularly
OpenRail Designer, to enhance the geometric complexity and operational efficiency of
railway projects. The methodology included: terrain data acquisition, reconstruction of the
existing state, preparation of new project documentation. Upgrading was based on
redesigning track alignments, introducing dual tracks, and achieving a target maximum speed
of 120 kph, thereby improving both technical and operational parameters. The findings show
BIM's potential for interdisciplinary collaboration. Future work suggests integrating train
movement simulations to improve the evaluation of modernization benefits.
Keywords: BIM, upgrading, railway line, OpenRail Designer, designing 3D
Highway engineering. Roads and pavements, Bridge engineering
To reveal the effects of environmental and loading conditions, as well as asphalt properties on the nonlinear rheological behavior of asphalt, the large amplitude oscillation shear (LAOS) test was introduced, and the Fourier transform rheology, Lissajous curve method, and the LAOS fatigue test have been applied to investigate the nonlinear rheological behavior of asphalt binders. The research results indicate that a decrease in temperature, an increase in shear frequency and strain level, the introduction of polymer modifiers, and the aging effect of asphalt can significantly increase the nonlinearity of asphalt, manifested by the higher relative magnitude of the third harmonic and zero-strain nonlinear coefficient. For the two polymer modifiers selected in this study, the 4% polyurethane modifier exhibits a higher nonlinear lifting effect than the 4% styrene-butadiene-styrene (SBS). The impact of long-term aging on nonlinear viscoelasticity is observably greater than that of short-term aging. The zero-strain nonlinear coefficient estimated based on the average value method can accurately characterize the nonlinear viscoelasticity of asphalt, which can serve as an effective supplement to the relative magnitude of the third harmonic. All asphalts exhibit shear thinning behavior under the test temperature of 24 °C, and the decrease in test temperature, the increase in shear rate and strain level, the introduction of modifiers, and the aging effect of asphalt all exacerbate the shear thinning behavior of asphalt. In addition, the fatigue failure process of asphalt materials is accompanied by an increasing degree of nonlinearity.
Highway engineering. Roads and pavements, Engineering (General). Civil engineering (General)
Autonomous highway driving demands a critical balance between proactive, efficiency-seeking behavior and robust safety guarantees. This paper proposes Language Action-guided Reinforcement Learning (LA-RL) with Safety Guarantees, a novel framework that integrates the semantic reasoning of large language models (LLMs) into the actor-critic architecture with an improved safety layer. Within this framework, task-specific reward shaping harmonizes the dual objectives of maximizing driving efficiency and ensuring safety, guiding decision-making based on both environmental insights and clearly defined goals. To enhance safety, LA-RL incorporates a safety-critical planner that combines model predictive control (MPC) with discrete control barrier functions (DCBFs). This layer formally constrains the LLM-informed policy to a safe action set, employs a slack mechanism that enhances solution feasibility, prevents overly conservative behavior and allows for greater policy exploration without compromising safety. Extensive experiments demonstrate that it significantly outperforms several current state-of-the-art methods, offering a more adaptive, reliable, and robust solution for autonomous highway driving. Compared to existing SOTA, it achieves approximately 20$\%$ higher success rate than the knowledge graph (KG) based baseline and about 30$\%$ higher than the retrieval augmented generation (RAG) based baseline. In low-density environments, LA-RL achieves a 100$\%$ success rate. These results confirm its enhanced exploration of the state-action space and its ability to autonomously adopt more efficient, proactive strategies in complex, mixed-traffic highway environments.
Ankit Bhardwaj, Rohail Asim, Sachin Chauhan
et al.
Free-flow road networks, such as suburban highways, are increasingly experiencing traffic congestion due to growing commuter inflow and limited infrastructure. Traditional control mechanisms, such as traffic signals or local heuristics, are ineffective or infeasible in these high-speed, signal-free environments. We introduce self-regulating cars, a reinforcement learning-based traffic control protocol that dynamically modulates vehicle speeds to optimize throughput and prevent congestion, without requiring new physical infrastructure. Our approach integrates classical traffic flow theory, gap acceptance models, and microscopic simulation into a physics-informed RL framework. By abstracting roads into super-segments, the agent captures emergent flow dynamics and learns robust speed modulation policies from instantaneous traffic observations. Evaluated in the high-fidelity PTV Vissim simulator on a real-world highway network, our method improves total throughput by 5%, reduces average delay by 13%, and decreases total stops by 3% compared to the no-control setting. It also achieves smoother, congestion-resistant flow while generalizing across varied traffic patterns, demonstrating its potential for scalable, ML-driven traffic management.
In order to solve the dusting and rutting problems of hot mix asphalt mixture during construction and operation, rutting test, bending test, Marshall stability test, freeze-thaw splitting test, uniaxial compression dynamic modulus test, overlay test, and four-point bending fatigue life test are applied on WHMM-13 to study the high-temperature stability, low-temperature cracking resistance, water stability, anti-reflection crack performance and fatigue durability in comparison with HMM-13. The results show that, compared to HMM-13, the dynamic stability and dynamic modulus (45 ℃, 10 Hz) of WHMM- 13 are improved by 10.0% and 47% respectively, and the rutting depth is reduced by 27.3%, indicating that the high temperature stability of WHMM-13 has been greatly improved. As for low temperature cracking resistance, the bending failure strain, stiffness modulus, flexural tensile strength and rupture energy of WHMM-13 are slightly lower than those of HMM-13. As for water stability, the residual stability of WHMM-13 in immersion Marshall test is 87.5%, and the splitting strength is 82.3%, both higher than that of HMM-13. As for anti-reflection crack performance, the total tensile rupture energy of WHMM-13 is 2.32 times that of HMM-13, with cracking resistance index (CRI) increased by 25%, which shows that WHMM-13 has better strength, can effectively prevent the crack from spreading and effectively improve the cracking resistance capacity of asphalt pavement. As for fatigue durability, the fatigue life of WHMM-13 is 5.4% lower than that of HMM-13, with bending stiffness modulus and cumulative dissipation energy reduced by 11.3% and 2.8% respectively, indicating that the fatigue durability of WHMM-13 is slightly reduced.
Highway engineering. Roads and pavements, Bridge engineering
Tiia-Riikka Loponen, Rizwanullah Shaik, Riku Varis
et al.
The running performance of different freight wagons affects the damage they cause to the track, which in turn influences the maintenance costs of the track. The track access charges may be imposed based on the track friendliness of a wagon if the features of the wagons are known. In Finland, besides the structure of the bogies, the width of the bogies also varies since some of the bogies are designed for a track gauge of 1524 mm and some for a track gauge of 1520 mm. In this paper, the performance of different freight wagon bogies on a small radius curve is investigated by means of field measurements and simulations. Wheel-rail contact forces and angle of attack values of six different wagon types are measured. In addition, simulations are created to gain further knowledge on wheel-rail contact forces, angle of attack values, and wear values. Based on this research, it would be beneficial to use a wider track gauge in small radius curves to make the curve negotiation easier for some bogie types. It was also noticed that a small radius curve with only a short transition zone could be very risky regarding derailment potential, especially for empty wagons.
Highway engineering. Roads and pavements, Bridge engineering
Anatolyi Pasichnyk, Bohdan Stasiuk, Iryna Lebid
et al.
Summary
Introduction. Critical infrastructure, including transport and civilian infrastructure, is one of the determining factors for the stable and efficient functioning of the economy and development of the state. In this regard, in order to organize the restoration and modernization of Ukrainian infrastructure damaged as a result of Russian aggression, it is extremely important to determine the necessary amount of funding for these works, which will provide more favorable conditions for determining the sources of funding, timing and appropriate resources for their implementation.
Problem Statement. A wide range of damage and destruction has been sustained by a fairly large number of transport and civilian infrastructure facilities, making it impossible to determine their exact extent. At present, generalized statistics on the scale of such losses are mostly known. Therefore, the development of methodological approaches to building approximation estimates of the required amount of funding for the restoration and modernization of Ukrainian transport and civil infrastructure is quite relevant from both a scientific and practical point of view.
Physics-Informed machine learning models have recently emerged with some interesting and unique features that can be applied to reservoir engineering. In particular, physics-informed neural networks (PINN) leverage the fact that neural networks are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations. The transient diffusivity equation is a fundamental equation in reservoir engineering and the general solution to this equation forms the basis for Pressure Transient Analysis (PTA). The diffusivity equation is derived by combining three physical principles, the continuity equation, Darcy's equation, and the equation of state for a slightly compressible liquid. Obtaining general solutions to this equation is imperative to understand flow regimes in porous media. Analytical solutions of the transient diffusivity equation are usually hard to obtain due to the stiff nature of the equation caused by the steep gradients of the pressure near the well. In this work we apply physics-informed neural networks to the one and two dimensional diffusivity equation and demonstrate that decomposing the space domain into very few subdomains can overcome the stiffness problem of the equation. Additionally, we demonstrate that the inverse capabilities of PINNs can estimate missing physics such as permeability and distance from sealing boundary similar to buildup tests without shutting in the well.
Fabio Calefato, Luigi Quaranta, Filippo Lanubile
et al.
Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members. Aims. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers. Method. To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception. Results. We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain. Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members. Conclusions. We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.
Configurable software systems can be tuned for better performance. Leveraging on some Pareto optimizers, recent work has shifted from tuning for a single, time-related performance objective to two intrinsically different objectives that assess distinct performance aspects of the system, each with varying aspirations. Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process. For this, the community takes two alternative optimization models: either quantifying and incorporating the aspirations into the search objectives that guide the tuning, or not considering the aspirations during the search but purely using them in the later decision-making process only. However, despite being a crucial decision that determines how an optimizer can be designed and tailored, there is a rather limited understanding of which optimization model should be chosen under what particular circumstance, and why. In this paper, we seek to close this gap. Firstly, we do that through a review of over 426 papers in the literature and 14 real-world requirements datasets. Drawing on these, we then conduct a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation. We found that (1) the realism of aspirations is the key factor that determines whether they should be used to guide the tuning; (2) the given patterns and the position of the realistic aspirations in the objective landscape are less important for the choice, but they do matter to the extents of improvement; (3) the available tuning budget can also influence the choice for unrealistic aspirations but it is insignificant under realistic ones.
Alejandro Gutierrez-Alcoba, Roberto Rossi, Belen Martin-Barragan
et al.
Electric road systems (ERS) are roads that allow compatible vehicles to be powered by grid electricity while in transit, reducing the need for stopping to recharge electric batteries. We investigate how this technology can affect routing and delivery decisions for hybrid heavy good vehicles (HGVs) travelling on a ERS network to support the demand of a single product faced by a set of retailers in the network. We introduce the Electric Roads Routing Problem, which accounts for the costs of electricity and fuel on a ERS network, consumption that are affected by the battery level of the vehicle in each step of the journey, the routing decisions and the variable weight of the vehicle, which depends on vehicle load and delivery decisions. In particular, we study a stochastic demand version of the problem, formulating a mathematical programming heuristic and proving its effectiveness. We use our model on a realistic instance of the problem, showcasing the different strategies that a vehicle may follow depending on fuel costs in relation to the costs of electricity.
The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. Neural Ordinary Differential Equations -- Neural ODEs are exploited as the deep learning operator. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed Neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via physics-informed Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigen-analysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, i.e., the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.
Sushma Reddy Yadavalli, Lokesh Chandra Das, Myounggyu Won
A platoon refers to a group of vehicles traveling together in very close proximity using automated driving technology. Owing to its immense capacity to improve fuel efficiency, driving safety, and driver comfort, platooning technology has garnered substantial attention from the autonomous vehicle research community. Although highly advantageous, recent research has uncovered that an excessively small intra-platoon gap can impede traffic flow during highway on-ramp merging. While existing control-based methods allow for adaptation of the intra-platoon gap to improve traffic flow, making an optimal control decision under the complex dynamics of traffic conditions remains a challenge due to the massive computational complexity. In this paper, we present the design, implementation, and evaluation of a novel reinforcement learning framework that adaptively adjusts the intra-platoon gap of an individual platoon member to maximize traffic flow in response to dynamically changing, complex traffic conditions for highway on-ramp merging. The framework's state space has been meticulously designed in consultation with the transportation literature to take into account critical traffic parameters that bear direct relevance to merging efficiency. An intra-platoon gap decision making method based on the deep deterministic policy gradient algorithm is created to incorporate the continuous action space to ensure precise and continuous adaptation of the intra-platoon gap. An extensive simulation study demonstrates the effectiveness of the reinforcement learning-based approach for significantly improving traffic flow in various highway on-ramp merging scenarios.
Abstract: In train – track coupled systems, interaction between subsystems occurs in wheel- rail contact. The most common contact model is perfectly elastic, linearized Hert’z spring. It has wide range of application in numerical simulations. In more detailed interaction models, the energy dissipation in wheel-rail contact is taken into account. There are no known comparisons of these models in the literature, that would indicate the effects of a specific solution application. In this paper, the main purpose is to analyze and compare the effects of two contact models in terms of numerical simulations of train – track vibrations. The reference contact model taken into account is linearized, perfectly elastic Hert’z spring. The second spring, proposed by the authors is enriched with viscous element based on hysteresis damping. Application of both models, and its effects were examined in plane, train – track vibrations simulations with threshold inequality excitation in the middle of the track length. Concluding from the analyzes performed, it was found that viscoelastic contact model application is important, when track stresses and fatigue are being investigated. In addition, it was found that neglecting the damping element in contact model reduces the probability of the wheel – rail contact loss phenomenon, and thus leads to incorrect identification of its occurrence. Keywords: Wheel/rail contact; Hertz contact; Linearized contact spring; Train/track dynamics
Highway engineering. Roads and pavements, Bridge engineering
Peteris Skels, Viktors Haritonovs, Edvards Pavlovskis
Wood fly ash stabilised road base layers with high recycled asphalt pavements content was studied both at the laboratory and in-situ. The original recipe was chosen based on an actual stabilised pavement base layer design with cement CEM II/B-T 42.5R but optimised using wood fly ash. The existing road base layer from gravel was mixed with dolomite aggregate and recycled asphalt pavement, adding cement and wood fly ash at different proportions. The mixture was compacted at optimal water content according to the Standard Proctor test and further conditioned. Resistance to freezing and thawing of hydraulically bound mixtures was checked after 28 days of conditioning. Even 50 cycles of freezing and thawing were used. Test results indicated wood fly ash as an effective alternative to the typically used cement for road base stabilisation, including recycled asphalt pavement material. Three hydraulically bound mixtures were chosen for test sections in the pilot project. The project includes five different sections with three different hydraulic binder recipes. The performance of each section was evaluated.
Highway engineering. Roads and pavements, Bridge engineering
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El equipo editorial de la revista Infraestructura Vial ha detectado un error involuntario en la información brindada en su página web relacionada con los derechos de autor en los artículos publicados según la lista que se indica. En razón de lo cual hacemos constar que los autores son quienes retienen los derechos de todas sus publicaciones en nuestra revista y que ya ha sido rectificado el error para cada artículo en la página web de Infraestructura Vial.
Software project management makes extensive use of predictive modeling to estimate product size, defect proneness and development effort. Although uncertainty is acknowledged in these tasks, fuzzy inference systems, designed to cope well with uncertainty, have received only limited attention in the software engineering domain. In this study we empirically investigate the impact of two choices on the predictive accuracy of generated fuzzy inference systems when applied to a software engineering data set: sampling of observations for training and testing; and the size of the rule set generated using fuzzy c-means clustering. Over ten samples we found no consistent pattern of predictive performance given certain rule set size. We did find, however, that a rule set compiled from multiple samples generally resulted in more accurate predictions than single sample rule sets. More generally, the results provide further evidence of the sensitivity of empirical analysis outcomes to specific model-building decisions.
Humans make daily routine decisions based on their internal states in intricate interaction scenarios. This paper presents a probabilistically reconstructive learning approach to identify the internal states of multi-vehicle sequential interactions when merging at highway on-ramps. We treated the merging task's sequential decision as a dynamic, stochastic process and then integrated the internal states into an HMM-GMR model, a probabilistic combination of an extended Gaussian mixture regression (GMR) and hidden Markov models (HMM). We also developed a variant expectation-maximum (EM) algorithm to estimate the model parameters and verified it based on a real-world data set. Experiment results reveal that three interpretable internal states can semantically describe the interactive merge procedure at highway on-ramps. This finding provides a basis to develop an efficient model-based decision-making algorithm for autonomous vehicles (AVs) in a partially observable environment.