Hasil untuk "Transportation engineering"

Xiuchen Xu, Zhen Wang, Zhaoyan Sheng et al.

Portable concrete barriers (PCBs) are modular structures widely used to separate traffic lanes from construction zones. However, these barriers often exhibit insufficient structural safety to effectively block intruding vehicles, leading to severe casualties. Appropriate structural design and experimental verification are required to prevent damage to the PCB structure. Existing domestic specifications lack explicit provisions regarding the structural design and load-bearing capacity of PCBs, presenting a critical bottleneck for the engineering application of temporary traffic barriers. To mitigate this limitation, a novel PCB was developed featuring a pre-embedded rectangular steel tube and I-beam joint, coupled with a single-slope cross-section (1200 mm in height), to satisfy the SB-level safety performance criteria specified in relevant standards. Distinct from conventional finite element simulation approaches, two full-scale vehicle impact tests were subsequently performed to verify the structural safety of the barrier. Experimental results demonstrate that the newly designed barrier can withstand vehicle impacts, meeting the SB protection level, with its containment, redirection, and energy absorption capabilities complying with relevant specification criteria. This study is anticipated to establish a foundational dataset for subsequent research in the field.

Hadiseh Rezaei, Rahim Taheri, Ehsan Nowroozi et al.

The rapid growth of Internet of Things (IoT) devices and the introduction of 5G networks have created new opportunities for enhancing network services, while also introducing significant security concerns. Intrusion Detection Systems (IDS) are crucial for identifying malicious activities and unauthorized access in these environments. However, current IDS solutions face challenges such as sharing sensitive data and managing large-scale networks. Federated Learning (FL) presents a promising solution by enabling models to be trained on decentralized devices without sharing private data. This paper examines how FL can enhance IDS for IoT and 5G networks, with an emphasis on privacy and security concerns. We analyze various privacy, homomorphic encryption, and security mechanisms in FL, including Differential Privacy (DP) and secure aggregation, and their potential applications in strengthening IDS solutions. Additionally, we explore how FL contributes to the development of more secure and efficient IDS systems while addressing challenges such as data heterogeneity and security risks. Finally, we identify gaps in the existing research and propose directions for future work to enhance the robustness and practicality of FL-based IDS for IoT and 5G environments.

Ainna Yue Moreno-Locubiche, Josep Vidal, Antonio Pascual-Iserte et al.

This paper proposes the joint design of reconfigurable intelligent surfaces (RIS) and zero-forcing (ZF) precoding for the downlink (DL) multiuser multiple-input single-output (MU-MISO) setup in millimeter-wave (mmWave) bands, where ZF is particularly attractive due to its ability to suppress inter-user interference by exploiting the large antenna arrays and sparse directional channels characteristic of mmWave systems. This ensures efficient spatial multiplexing with manageable complexity, making ZF a practical and in modern 5G/6G deployments. However, a careful design is necessary to overcome potential rank deficiency in the channel matrix. For the MU-MISO case, rank deficiency may arise if users exhibit significantly different channel gains or if, being in far-field, they are aligned with the position of the transmitter. On the other hand, the deployment of a RIS introduces artificial scattering which can shape the radio environment to address those situations. We explore the joint design under perfect channel knowledge, assess the impact of imperfect channel estimation on the bit error rate (BER) and propose a robust design of pilot transmissions that equalizes multiuser interference across users in the presence of channel errors in the precoder design. This evaluation shows the advantages of optimized RIS-aided ZF MU-MISO communication for the DL of wireless systems.

H. Sinan Bank, Daniel R. Herber

The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a "Map of Datasets in EDSE." The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.

Lin Gao, Minzhi Chen, Jianghong Zhu

Alaa Alqaryuti, Karim Moawad, Ahmad Mayyas

Mohammed Nouali, Anne Dony, Stéphanie Vignaud

Roland Nagy, István Szalai

William Powrie, David Milne, Geoff Watson et al.

Xuling LIU, Xiaoning ZHANG

In freeway merging areas, vehicles exhibit flexibility in lane-changing manoeuvres to facilitate merging. However, the lack of effective communication among vehicles leads to inadequate coordination between mainline and ramp vehicles at the merge point, increasing the likelihood of traffic congestion. The technology of connected and autonomous vehicles allows information interaction and cooperation between vehicles, which can effectively solve this problem and improve the efficiency of vehicle merging. This study proposes a merging optimisation framework for connected and autonomous vehicles, dividing the merging area into cooperative lane-change and trajectory optimisation areas. To simulate and manage connected and autonomous vehicles’ behaviour, the research employs VISSIM for scenario creation and leverages both VISSIM COM and Python for control purposes. In the cooperative lane-changing area, the optimal number of lane-changing vehicles is determined by considering traffic distribution in the inner and outer lanes downstream of the confluence area. Subsequently, the sequence and combination of these vehicles are established based on connected and autonomous vehicles’ cooperative lane-changing mode analysis. Within the trajectory optimisation area, the model refines each vehicle’s speed and acceleration, guiding connected and autonomous vehicles to merge smoothly and safely at the confluence point. The simulation results show that the optimisation framework for the freeway merging area proposed in this study performs well. As the level of demand increases, the scenario with control demonstrates superior performance in terms of enhanced trip efficiency, diminished total delay time and a reduction in the number of stops.

MAO Quan, ZHENG Junqiu, MA Hui et al.

Asphalt pavement is prone to fatigue cracking during long-term service, which can lead to secondary diseases and weaken the structural durability. For in-depth revelation of the fatigue damage behavior of asphalt mixtures, in this paper, a semi-circular bending (SCB) mesoscopic model was established based on the discrete element method. The fatigue fracture behavior of three typical asphalt mixtures in Jiangsu Province’s expressways, including SMA-13, SUP-20, and SUP-25, was simulated under different stress ratio conditions. The fatigue damage mechanism and evolution law of asphalt mixtures were analyzed from the aspects of fatigue life, crack propagation, and residual strength. The results show that all three mixtures exhibit typical three-stage fatigue damage characteristics, and the increase of stress ratio will significantly shorten the fatigue life and accelerate the crack development. SMA-13 shows excellent fatigue resistance due to its dense skeleton and modified asphalt mortar, followed by SUP-20, while SUP-25 performs the worst due to insufficient skeleton constraint and low mortar toughness. Meanwhile, the crack propagation paths and residual strength attenuation laws of different mixtures are significantly different, further revealing the key role of skeleton structure and asphalt mortar performance in the fatigue damage mechanism.

Yikui Wang, Yuhong Xiong, Jianxiong Lian et al.

During the transportation, paving, and compaction of hot mix asphalt aggregate segregation occurs, leading to changes in the theoretical maximum specific gravity (Gmm) of the asphalt mixture. To accurately determine the Gmm of the asphalt mixture after segregation, field samples of the asphalt mixture were collected and analyzed using the ignition method to evaluate the extent of aggregate segregation at different locations (auger and paver overlap zones). The asphalt immersion method was employed to precisely measure the Gmm. Grey relational analysis was applied to identify influencing factors and their patterns of impact on Gmm, and the relational degree of each factor was determined. By introducing weight factors and variation ratios for each factor, an optimized prediction model for Gmm was established. The results indicate that the specific gravity ratio of coarse to fine aggregate is the most significant factor affecting Gmm. The error between the predicted air void content of the asphalt pavement based on the corrected Gmm and the core-measured values obtained via the asphalt immersion method does not exceed 0.5%. This model is suitable for estimating the Gmm of segregated asphalt mixtures, thereby improving the accuracy of compaction quality assessment in segregated areas of asphalt pavements.

Xiaomi Wu, Yiyang Liu, Zhixing Li et al.

Cavitation is a critical phenomenon in hydraulic systems, particularly in gear pumps, where it can significantly affect performance and reliability. This study uses numerical simulations with the Full Cavitation Model and <i>k-ε</i> turbulence model to investigate the thermodynamic effects of cavitation in gear pump lubricating oil at varying temperatures. It focuses on the formation and evolution of cavitation vortex structures in the outlet bridge area. The simulations reveal significant heat exchange between liquid and vapor phases, causing a local temperature drop and a reduction in saturated vapor pressure, which suppresses cavitation development. As temperature increases, this effect diminishes due to the lower density of the hydraulic oil.

Pengcheng Liu, Guoxing Bai, Zeshuo Liu et al.

Mining articulated vehicles (MAVs) are widely used as primary transportation equipment in both underground and open-pit mines. These include various machines such as Load–Haul–Dump machines and mining trucks. Path tracking control for MAVs has been an important research topic. Most current research focuses on path tracking control during forward driving. However, there are relatively limited studies on reverse path tracking control. Reversing plays a crucial role in the operation of MAVs. Nevertheless, existing methods typically use the center of the front axle as the control point; therefore, the positioning system is usually installed at the front axle. In practice, however, this means the positioning system is actually located at the rear axle during reverse operations. While it is theoretically possible to infer the position and orientation of the front axle from the rear axle, a strong nonlinear relationship exists between the motion states of the front and rear axles, which introduces significant errors in the system. As a result, these existing methods are not suitable for reverse driving conditions. To address this issue, this paper proposes a nonlinear model predictive control (NMPC) method for path tracking during mining-articulated vehicle (MAV) reverse operations. This method innovatively reconstructs the reverse-motion model by selecting the center of the rear axle as the control point, effectively addressing the instability issues encountered in traditional control methods during reverse maneuvers without requiring additional positioning devices. A comparative analysis with other control strategies, such as NMPC for forward driving, reverse NMPC using the front axle model, and reverse linear model predictive control (LMPC), reveals that the proposed NMPC method achieves excellent control accuracy. Displacement and heading error amplitudes do not exceed 0.101 m and 0.0372 rad, respectively. The maximum solution time per control period is 0.007 s. In addition, as the complexity of the reverse path increases, it continues to perform excellently. Simulation results show that as the curvature of the U-shaped curve increases, the proposed NMPC method consistently maintains high accuracy under various operational conditions.

Guofa WANG, Jinhu ZHANG, Huaiwei REN et al.

Summarizing the current development status of intelligent and digital technologies in the coal industry, analyzing the new situations and requirements for research and application of intelligent technologies and complete sets of equipment for efficient coal mining. Addressing challenges such as comprehensive perception of the state of high-strength mining surrounding rock and equipment under different coal seam conditions and integrated coordinated advancement and linked control of equipment groups, the paper explores the deep integration of advanced information technologies such as the Internet of Things, cloud computing, big data analytics, and high-precision inertial navigation with coal mining technologies. This integration has enabled condition monitoring and data integrated management of coal mining equipment, improving monitoring, management, and decision-making efficiency in the coal mining process through precise perception, real-time data analysis, and intelligent control. It has also enhanced the adaptability of complete mining equipment sets to different coal seam conditions. To address the challenges of integrated advancement and coordinated control of equipment groups in ultra-long workfaces for medium-thick coal seams, the paper introduces synchronous mapping of geological information ahead of the mining face through dynamic perception of geographical information and real-time model updating technology. High-power rapid advancement equipment suitable for ultra-long workfaces has been developed, and a multi-area synchronous advancement process system covering all aspects of support, mining, and transportation has been established. This forms a linked mechanism for support equipment groups in ultra-long workfaces and achieves coordinated control among equipment groups, significantly improving mining efficiency and resource recovery rates for medium-thick coal seams. For complex geological conditions in deep thick coal seams, refined control of supports and intelligent coordinated control technologies for equipment groups have been developed, enabling perceptual coordination and adaptive precise control between equipment, thus improving system reliability and efficiency. Addressing challenges such as coal wall protection in ultra-large mining spaces, intense dynamic loads on surrounding rock in the workface, and significant variations in coal flow loads in 8−10 m ultra-high mining height workfaces, the paper proposes adaptive control technology for coupling hydraulic supports with surrounding rock, enhancing the adaptability of equipment to ultra-high mining height workface environments. Key technologies such as a guard plate monitoring system, adaptive cutting technology for shearer stability, coal flow load balancing, and dynamic chain tensioning control have been developed, enabling efficient mining under conditions of significant coal seam thickness variations and strong mining pressure. In the overseas promotion of intelligent and digital complete sets of equipment for efficient coal mining, the paper addresses challenges such as differences in coal mine conditions, safety requirements, and technical standards through customized adjustments to technical equipment. An integrated monitoring and big data analytics system has been developed, improving the response to abnormal situations and enabling autonomous perception, high-reliability data transmission, intelligent analysis and decision-making, precise control and execution among equipment groups within the workface. A technical system for intelligent and digital complete sets of equipment for efficient coal mining adaptable to different working conditions has been established and has achieved good results in engineering practice. This provides support for the high-quality development of coal mine intelligence and solutions to key technical challenges in coal mine intelligent construction.

Sun Wenjie, Tang Xiaobin

The dynamic behavior of the straddle monorail pantograph system is significantly influenced by nonlinearities arising from its unique structural configuration and operational environment. This article presents a mathematical model that incorporates both nonlinear geometric and material properties of the pantograph. A finite element model is utilized to perform modal and static analyses, identifying stress concentrations and dynamic characteristics. To account for random vibration excitations, a nonlinear dynamic response framework is developed, considering vehicle-induced vibrations and network irregularities as stochastic excitation sources. The results demonstrate that these nonlinearities amplify specific resonances under random excitation, leading to increased stress and deformation at critical points. These findings provide a foundation for enhancing fault detection strategies and designing more robust pantographs, with nonlinear vibration monitoring proposed as an effective diagnostic tool. This study contributes to both theoretical understanding and practical improvements in ensuring the reliability and longevity of straddle monorail systems.

Alexandra Mazak-Huemer, Christian Huemer, Michael Vierhauser et al.

With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.

Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae et al.

Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.

Allysson Allex Araújo, Marcos Kalinowski, Matheus Paixao et al.

[Background] Emotional Intelligence (EI) can impact Software Engineering (SE) outcomes through improved team communication, conflict resolution, and stress management. SE workers face increasing pressure to develop both technical and interpersonal skills, as modern software development emphasizes collaborative work and complex team interactions. Despite EI's documented importance in professional practice, SE education continues to prioritize technical knowledge over emotional and social competencies. [Objective] This paper analyzes SE students' self-perceptions of their EI after a two-month cooperative learning project, using Mayer and Salovey's four-ability model to examine how students handle emotions in collaborative development. [Method] We conducted a case study with 29 SE students organized into four squads within a project-based learning course, collecting data through questionnaires and focus groups that included brainwriting and sharing circles, then analyzing the data using descriptive statistics and open coding. [Results] Students demonstrated stronger abilities in managing their own emotions compared to interpreting others' emotional states. Despite limited formal EI training, they developed informal strategies for emotional management, including structured planning and peer support networks, which they connected to improved productivity and conflict resolution. [Conclusion] This study shows how SE students perceive EI in a collaborative learning context and provides evidence-based insights into the important role of emotional competencies in SE education.

Yongqi Dong, Bart van Arem, Haneen Farah

Automated Vehicles (AVs) hold promise for revolutionizing transportation by improving road safety, traffic efficiency, and overall mobility. Despite the steady advancement in high-level AVs in recent years, the transition to full automation entails a period of mixed traffic, where AVs of varying automation levels coexist with human-driven vehicles (HDVs). Making AVs socially compliant and understood by human drivers is expected to improve the safety and efficiency of mixed traffic. Thus, ensuring AVs' compatibility with HDVs and social acceptance is crucial for their successful and seamless integration into mixed traffic. However, research in this critical area of developing Socially Compliant AVs (SCAVs) remains sparse. This study carries out the first comprehensive scoping review to assess the current state of the art in developing SCAVs, identifying key concepts, methodological approaches, and research gaps. An informal expert interview was also conducted to discuss the literature review results and identify critical research gaps and expectations towards SCAVs. Based on the scoping review and expert interview input, a conceptual framework is proposed for the development of SCAVs. The conceptual framework is evaluated using an online survey targeting researchers, technicians, policymakers, and other relevant professionals worldwide. The survey results provide valuable validation and insights, affirming the significance of the proposed conceptual framework in tackling the challenges of integrating AVs into mixed-traffic environments. Additionally, future research perspectives and suggestions are discussed, contributing to the research and development agenda of SCAVs.