Urban underground construction in water-rich sandy strata produces large quantities of high-fluidity pipe-jacking spoil whose high water content, residual conditioning agents and heavy metal contaminants make conventional dewatering and landfilling increasingly unsustainable under carbon peaking and neutrality targets. This study explores a low-carbon route that converts such spoil into CO<sub>2</sub> foamed concrete through a coupled alkali activation–CO<sub>2</sub> foaming process. Ground granulated blast furnace slag and fly ash are used as geopolymer precursors, while a CO<sub>2</sub>-based aqueous foam is introduced as both a pore-forming phase and carbon source. Single-factor tests and an L16(4<sup>4</sup>) orthogonal design are conducted to quantify the effects of CO<sub>2</sub> concentration, foam volume fraction, geopolymer dosage and alkali activator content on fluidity, setting time and compressive strength. Scanning electron microscopy (SEM) is employed to examine pore structure, gel morphology, carbonate precipitation and the interfacial transition zone around spoil particles. The results identify an optimum mix window (CO<sub>2</sub> 60–80%, foam 70–80%, geopolymer ≈ 20% and alkali activator ≈ 10% of solids) that delivers a fluidity above 210 mm, 28-day strength exceeding 3.0 MPa and a uniform closed-pore network. A multi-scale mechanism is proposed in which physical foaming, chemical carbonation and spoil particle immobilization act synergistically to form a dense gas–solid–soil composite suitable for in situ backfilling.
Abstract Enhancing the hydrogen embrittlement (HE) resistance of alloys caters to the urgent needs of engineering safety and long-distance hydrogen transportation. Highly dense precipitates in the alloys act as H traps, however, some of them cannot strongly trap H thus failing to prevent its accumulation at the critical regions. Experimentally, it is challenging to expeditiously identify and generate phases causing strengthening and acting as strong H traps. Here, we demonstrate a computation-based design strategy to generate precipitates strongly trapping H. Based on the quantum machine learning Al-Sc-Cu potential, the optimal processing parameters of strong H trapping phases are determined, even though they are metastable in nature. Elemental mapping in electron microscope and atom probe tomography confirms the presence of Cu in Al3Sc and its strong interaction with H. Hence, we envisage the proposed strategy will accelerate the design of HE-resistant microstructures of various technologically relevant materials via identification of desirable phases.
Tunnel entrance slopes in mountainous regions are highly susceptible to rainfall-induced instability due to excavation-induced fractures, which accelerate infiltration and weaken slope materials. However, existing models often lack accuracy in capturing fracture-governed hydro-mechanical interactions. This study proposes an advanced coupled hydro-mechanical model integrating random fractures, surface runoff (shallow water equations), and subsurface infiltration (Richards' equation). A 2D random fracture model and a 3D explicit fracture model were developed to quantify the influence of fracture networks on pore water pressure and slope failure, while simulating surface–subsurface flow interactions under rainfall. A local factor of safety (LFS) method is used to assess rainfall–fracture–slope interactions. Sensitivity analyses show that increases in fracture width and depth markedly reduce the average LFS, while ponding has a more severe destabilizing effect than rainfall infiltration. When the mean fracture width is below 0.11 m, additional fracture depth has little effect on shallow instability. Results show that fractures intensify pore pressure buildup and stress redistribution, reducing LFS and aggravating instability. The 3D model reveals effective saturation above 90 % in fractured zones and over 30 % reduction in LFS, compared to unfractured conditions. Simulated failure zones (∼50 m long, 5.6 m deep) closely match observed landslides, demonstrating strong predictive accuracy and engineering relevance. Based on the model outcomes, mitigation strategies such as fracture sealing and targeted grouting were proposed to improve slope safety near tunnel entrances. These findings offer practical insights for disaster prevention during tunnel construction in fractured, rainfall-prone terrains.
To address the limitation of existing models for selecting freight security inspection modes and determining secondary inspection ratios which often overlook multi-node collaborative risk assessment, this study focuses on the security inspection service decisions for outbound air cargo in multimodal transport originating from remote off-airport cargo terminals. A "two-point, one-line" security inspection service decision-making model is constructed, which includes remote landside freight stations, interline transportation, and airport airside cargo terminals. Based on system safety risk assessment principles, the model determines the changes of the quantified value of safety according to different factors such as the credit rating of freight agents, types of freight station security inspections, and the failure probability of the intermodal transportation system. Taking a land-air multimodal transport scenario as an example, simulation experiments on the change of safety quantitative value are conducted on the freight agents with different credit grades, and the final safety quantitative value and the safety standard of airport cargo and mail installation are relatively analyzed. Results show that the proposed decision model is both effective and practical. It provides scientifically grounded recommendations for security inspection service decisions and helps reduce the workload of secondary inspections for outbound air cargo via multimodal transport from remote cargo stations.
Arika Ligmann-Zielinska, Igor Vojnovic, Timothy F. LeDoux
This paper presents a data-driven agent-based model that simulates the weekly grocery shopping behavior of disadvantaged consumers in highly segregated lower eastside neighborhoods of Detroit, Michigan. We focus on neighborhoods experiencing severe disinvestment to analyze the shopping behavior of residents after all major regional and national supermarket chains abandoned the city. The presented model is unique in that it utilizes detailed shopping behavior data collected to examine travel in marginalized communities, specifically among residents in severe poverty who are often overlooked in the travel behavior literature. The research shows that in extreme socio-economic decline, sociodemographic variables (such as class) can become more relevant than the built environment (land-use mix, density, and street connectivity) in determining access and influencing mobility. After identifying unique groups of household agents, we design rules that utilize probability distributions generated from survey responses. The decision-making of agents that emulate households is habitual rather than utility driven. Modeled behavior is designed based on stated preferences, which may contradict premises such as the “shortest distance to the nearest shop” approach, a common assumption in the literature. We also report on three what-if scenarios to evaluate how major population changes would affect the results.
Transportation engineering, Transportation and communications
Tayyab Akhtar, Edward Canepa, Andrea Cattanei
et al.
This work presents an experimental study of the effect of blade count on the flow field and the radiated noise in a low-speed axial fan with a rotating shroud. A two-component Laser Doppler Velocimetry (LDV) system and Particle Image Velocimetry (PIV) instrumentation have been employed to investigate the flow in the gap region and in front of the rotor blades. Additionally, the fan has been installed in a hemi-anechoic chamber and far-field acoustic measurements have been taken with a microphone mounted on-axis upstream of the rotor to show changes in the spectral features of the radiated noise. The tested rotor is a variable-geometry one that has allowed for studying rotor configurations with different numbers of blades of the same chord and shape, i.e., of the same geometry but different solidity. Rotor pressure rise and flow rate are average quantities that have a relevant effect on the leakage flow. Keeping them fixed while varying solidity allows us to highlight the local effects of circumferential pressure non-uniformity caused by differing blade loading. The results show that, at low solidity, the flow leaving the gap is mainly directed radially outward and follows a longer path before being ingested by the rotor, thus losing strength due to mixing with the main flow. As solidity increases, the flow becomes less radial and is more rapidly ingested by the rotor. In all cases, the sound pressure level spectrum shows marked subharmonic humps and peaks originating from the interaction between the leakage flow and rotor. The departure of such peaks from the blade passing frequency increases with the solidity, while the associated energy increases up to seven blades and then decreases.
Armira Kontaxi, Haris Sideris, Dimitris Oikonomopoulos
et al.
Understanding how drivers respond to feedback and incentives is crucial for designing data-driven interventions that enhance road safety. This study investigates driver profiling and behavioral change using high-resolution telematics data collected through the OSeven DrivingStar smartphone application within the O7Insurance project. The naturalistic driving experiment was divided into two main phases: a baseline period with personalized feedback (Phase A) and an incentive-based phase (Phase B) comprising two gamified driving challenges with distinct reward criteria. Using data from 86 active participants, K-means clustering identified three driver profiles—Low-Exposure Cautious, Balanced/Average, and High-Risk Drivers—based on exposure, harsh events, speeding, and mobile phone use. The Balanced/Average group exhibited statistically significant improvements during both challenges, reducing speeding frequency and intensity (e.g., from 4.8% to 3.7%, <i>p</i> < 0.01), while High-Risk Drivers achieved moderate reductions in speeding intensity (from 6.4 to 5.3 km/h, <i>p</i> < 0.05). Low-Exposure Cautious Drivers maintained stable, low-risk performance throughout. These findings demonstrate that incentive-based telematics schemes can effectively influence driving behavior, particularly among drivers with moderate risk levels. The study contributes to the growing body of research on gamified driver feedback by linking behavioral clustering with responsiveness to incentives, providing a foundation for adaptive and personalized road safety interventions.
In response to the growing demand for efficient and eco-friendly golf carts, this paper presents an optimized design of a permanent magnet synchronous machine (PMSM) for multiple operating conditions. The application scenarios of the golf cart were first analyzed, identifying the power requirements under three driving conditions such as unloaded on flat roads, fully loaded on flat roads, and fully loaded on slopes. Then, a 36-slot 8-pole interior PMSM is developed, and a systematic two-stage optimization strategy using a Multi-Objective Genetic Algorithm (MOGA) is applied to enhance both no-load and rated-load performance. By adjusting key rotor parameters to balance competing objectives, the optimized machine demonstrates notable improvements in cogging torque reduction, output torque, torque ripple minimization, and operational efficiency. Specifically, the results show that the optimized machine achieves a cogging torque reduction of over 60%, an increase in maximum output torque by 7.3%, and a peak efficiency improvement of 1.2 percentage points under high-load conditions. Experimental results validate the effectiveness of the design and confirm its suitability for the complex operating conditions of golf carts.
Abstract The study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self‐supervised vessel trajectory segmentation method (SS‐VTS), which segments VTs based on their inherent spatio‐temporal semantics. SS‐VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi‐dimensional feature sequence of the VTs using self‐supervised learning. Finally, spatio‐temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS‐VTS achieves state‐of‐the‐art segmentation results compared to seven baseline methods.
The nature of renewable energy resources (RERs), such as wind energy, makes them highly unstable, unpredictable, and intermittent. As a result, they must be optimized to reduce costs and emissions, increase reliability, and also to find the optimal size and location for RERs and energy storage systems (ESSs). Microgrids (MG) can be modified using ESSs to gradually reduce traditional energy use. In order to integrate RERs in a financially viable scheme, ESSs should be sized and operated optimally. The paper presents an enhanced biogeography-driven optimization algorithm for optimizing the operations and sizes of battery ESSs (BESSs) taking into account MGs that experience wind energy penetration in a way that migration rates are adaptively adjusted based on habitat suitability indexes and differential perturbations added to migration operators. An optimization problem was applied to a BESS to determine its depth of discharge and lifespan. This paper considers three different scenarios in using simulations and compares them to existing optimization methods for the purpose of demonstrating the effectiveness of the offered scheme. Out of all the case studies examined, the optimized BESS-linked case study was the least expensive. We also show that a BESS must be of an optimum size to function both economically and healthily. For economic and efficient functioning of MGs, it has been shown that finding the optimum size of the ESS is important and potentially extends battery lifespan. The IBBOA obtained a more precise size for BESS’s volume, and the final outcomes are compared in this paper with other methods.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Horizontal curves of rural roads are accident-prone segments of the route. Sharp curves, steep slopes, and reduced visibility due to the mountainous environment greatly affect the driver behavior and performance. Lane-keeping ability, which is quite crucial in head-on road collisions, is a lateral driver behavior examined in a number of previous studies. This study, which is aimed to examine the naturalistic behavior, has employed the “aerial video recording” to investigate the drivers' lane-keeping ability in horizontal curves.To address the risk of encroachment (enc) into the opposite lane, this paper has developed a logistic regression model to predict the probability of a head-on collision with an enc > 0 cm threshold by exploring the relationships between road features (geometric, traffic, pavement conditions, etc.) and driver encroachment into the opposite lane. To this end, use was made of the data of 785 vehicles in 11 horizontal curves (in Kashmar-Neyshabor and Siahkal-Deylam mountainous routes) with radii in the 30–150 (m) range, deflection angles in the 80°-150° range, and slopes in the 0–8% range. The explanatory variables used in the model included the start point position (sp), road slope (Gr), sufficient stopping sight distance (Sd) and difference between the posted and vehicle speeds in mid-curves (DPS). According to the results, speeding and curve rising of 70° increased the encroachment probability, and steep upgrades exacerbated it; at a sufficient stopping sight distance, it reached 85%.
Space–time prism (STP) is a fundamental concept in time geography and has been predominately constructed in unimodal transportation networks. Due to the vast trip chaining options by private vehicles and public transportation, it was challenging to construct STP over multimodal transportation networks. We previously put forward an efficient method to narrow down the action space for trip chaining and construct STPs effectively in a multimodal supernetwork. This study applies multimodal STP modeling to measure space–time accessibility and equality of daily activity opportunities. Two equality measures (Gini coefficient and 20:20 ratio) are derived from two space–time accessibility measures based on delineated STPs. We examine the equality of access to shopping and leisure opportunities in the Rotterdam–The Hague metropolitan area, the Netherlands. The results show the effects of various factors on accessibility and equality. It is found that although the study area has relatively low inequality using single modes, multimodal trip chaining further reduces inequality to access the distributed space–time opportunities.
In this paper, both numerical and experimental methods are adopted to study the fluid–structure interaction (FSI) problem of a wedge structure with stiffeners impacted with water during the free-falling water entry process. In the numerical model, a partitioned two-way couple of CFD and FEM solvers is applied to deal with the FSI problem, where the external fluid pressure exported from the CFD simulation is used to derive the structural responses in the FEM solver, and the structural deformations are fed back into the CFD solver to deform the mesh. Moreover, a tank experiment using a steel wedge model that has the same structural properties is also conducted to compare with the numerical results. Verification and validation of the numerical results indicate that the CFD-FEM coupled method is feasible and reliable. The slamming response results by numerical simulation and experiments, including displacement, velocity, acceleration, slamming pressure, deformation, structural stresses and total forces on the wedge, accounting for hydroelasticity effects in different free falling height conditions are comprehensively analyzed and discussed.
Chiara Guido, Dario Di Maio, Pierpaolo Napolitano
et al.
Approaching Euro VII regulation limits for natural gas engines represents an arduous but not impossible challenge.The technological improvement and research progress in propulsion systems can offer strong tools in the achievement of next emission target. The efforts in this field are justified by the interests in natural gas powertrains that represent an alternative solution mainly in case of Heavy-Duty engines for off-road and transport applications. One of the aspects involving the OEMs effort is the particle number emissions abatement, that, due to the next future stringent limits involving sub-23 nm particles, represents a challenge also in case of natural gas fueled engines.The present paper describes the potentiality offered by different strategies in the control of particles emitted by Heavy-Duty natural gas engines. Three approaches will be described and analyzed using experimental characterization carried out by the authors with the use of an engine test bench. Assuming that the major source of particles derives from the lube oil combustion, firstly the effect of improved piston ring pack design has been assessed, comparing two versions of the same engine.Secondly the quality of lubricant oils in particle formation has been evaluated, testing oils with improved chemical/physical properties.A last aspect analyzed was the evaluation of the particulate filter technology capability when applied to a natural gas engine, a novelty in the scientific literature.All the investigated approaches proofed powerful tools in the control of sub-23 particle, paving the way for natural gas engines compliant with Euro VII PN limits.