Shen-En Chen

Decarbonizing the transportation sector requires practical pathways for reducing fossil-fuel consumption and greenhouse gas emissions from existing rail systems. Although railway electrification through catenary systems offers an effective pathway toward low-emission rail transportation, its widespread implementation on existing non-electrified corridors can be constrained by substantial capital costs, infrastructure modifications, and deployment complexity. Battery-electric and battery-supported locomotive technologies provide an alternative pathway for rail decarbonization by enabling progressive fleet electrification while reducing dependence on extensive wayside electrical infrastructure. This study presents a route-level energy assessment to determine the onboard battery energy storage system (BESS) capacity required to retrofit an F59PH diesel passenger locomotive for battery-electric operation on the Piedmont corridor. The journey is simulated using the Single Train Simulator (STS), incorporating route characteristics, speed restrictions, station stops, and vehicle configuration. The resulting traction-energy demand provides the basis for evaluating commercially available battery technologies under battery-only and hybrid battery-supported operating scenarios. The simulation indicates a route-level energy consumption of 25.2 kWh/km. Among the evaluated battery technologies, the configuration requiring the fewest packs for baseline battery-only operation consisted of 142 packs, sufficient to satisfy the total route energy demand. Incorporating the added battery mass into the vehicle-energy model modestly increased this requirement to 146 packs. Under the hybrid battery-supported scenario, approximately 1305 kWh of regenerative braking energy was recoverable, with the lowest-pack-count configuration (Type D) providing sufficient capacity to enable nearly 40 min of battery-only operation. These results demonstrate how onboard energy storage and regenerative braking can support incremental rail decarbonization by reducing diesel operation, recovering otherwise dissipated braking energy, and potentially lowering the infrastructure burden associated with conventional corridor-wide electrification. As a route-level case study, the proposed methodology provides a transferable framework for evaluating onboard battery integration, identifying appropriate battery capacity, and supporting decarbonization strategies across passenger, light-rail, and heavy-rail applications.

Rollover stability is a critical concern for prestressed concrete bridge girders, particularly during handling, transportation, and erection when lateral restraint is limited. The growing use of long-span, slender sections has increased their susceptibility to rollover instability, especially under the combined influence of geometric imperfections, thermal gradients, eccentric loading, and inclination and deformation of bearing surfaces. Although these effects are well recognized in practice, current codes and design guidelines offer limited guidance, and many analytical and numerical models use simplifying assumptions that incompletely capture important nonlinear behaviors. Furthermore, experimental investigations remain scarce due to the complexity and cost of full-scale testing. This paper presents a comprehensive review of rollover stability of prestressed concrete bridge girders supported on elastomeric bearings. The theoretical foundations and governing mechanisms are first summarized, including a discussion of the effects of geometric imperfections, temperature variations, wind, support conditions, and bearing rotational stiffness. Existing analytical, numerical, and experimental studies are critically examined, and key knowledge gaps are identified to guide future research and code development.

Lithium-ion battery (LIB)-powered locomotives have emerged as a promising alternative to conventional rail electrification by reducing dependence on overhead catenary systems, lowering infrastructure costs, and improving operational flexibility. However, train failures involving power loss or mechanical faults present significant challenges for battery-powered rail systems, particularly on single-track corridors where alternate route options are limited. Consequently, the implementation of a reliable limp-home strategy is more complex than in road electric vehicles (EVs), requiring consideration of battery availability, rescue logistics, and track accessibility. This study investigates limp-home operation and rescue planning for a battery-powered historic trolley operating on a 20 km heritage route in North Carolina. The trolley is powered by a dedicated LIB trailer and supported by battery-charging (BC) infrastructure based on inductive power transfer (IPT) technology. A comprehensive framework is developed that integrates time–space analysis, cellular automata (CA)-based failure-risk modeling, and battery-energy assessment to evaluate train-failure scenarios and recovery strategies. The results identify critical failure regions along the route and demonstrate the benefits of strategically deploying additional LIB rescue trailers and wireless power transfer (WPT) infrastructure. A revised rescue strategy incorporating two additional LIB trailers, together with static and dynamic WPT systems, substantially reduces recovery time and improves the likelihood of maintaining scheduled excursions. Emergency energy analysis further shows that WPT-assisted operation can significantly extend limp-home capability under low state-of-charge conditions. Based on the integrated analysis, a tiered limp-home decision framework is developed to support operator decision-making during train failures. The proposed methodology provides a practical approach for enhancing the resilience, recoverability, and operational reliability of battery-powered heritage rail systems and can serve as a foundation for future battery-electric rail applications.

Geospatial Artificial Intelligence (GeoAI) and the rapid advancement of 3D data acquisition technologies (e.g., LiDAR) have enabled scalable semantic interpretation of georeferenced large-scale 3D point clouds across different applications. 3D deep learning-based semantic segmentation on large-scale 3D point clouds typically relies on data partitioning and sampling strategies to address computational constraints, resulting in a substantial proportion of points not being directly predicted by deep neural networks. These points not directly predicted by the model require a processing step for label propagation, which is commonly handled using simple spatial proximity-based rules. While this simplification may be acceptable for some applications, it becomes critical in tasks that require precise spatial measurements and accurate object delineation, where propagation errors can directly affect downstream analyses. To bridge this research gap, this study views this process as a spatial surrogate modeling problem, where predictions from deep learning models are used to infer labels for underrepresented points based on spatial relationships. We adopt inverse distance weighting (IDW) as a transparent, deterministic, and training-free spatial post-processing strategy to examine whether explicitly incorporating spatial relationships improves segmentation outcomes. We evaluate the proposed method within an existing 3D semantic segmentation workflow for bridge inspection, a practical application requiring accurate spatial measurement and reliable object delineation. In the experiments, we use self-collected terrestrial LiDAR point clouds of bridges and associated hydraulic structures and systematically assess how neighborhood size and distance-decay parameters affect model performance on the segmentation task. Results show that this spatial post-processing step provides a modest enhancement over the conventional nearest-neighbor propagation baseline, with notable gains especially on spatially sparse or geometrically complex classes. The results also reveal class-dependent spatial effects, suggesting that different semantic classes exhibit distinct spatial dependencies during label propagation. These findings highlight the practical importance of accounting for spatial context when propagating semantic information to those points not directly predicted by deep neural networks. This is particularly important for downstream applications that require highly accurate spatial measurement and object delineation. The practical value of this post-processing step becomes more apparent in data-scarce application domains such as hydraulic-structure inspection, where large labeled point-cloud datasets and public benchmarks remain limited. In such settings, users may rely on domain-specific pre-trained models, while the original training data may not be publicly available for retraining or extensive model modification. Using a practical case study in the hydraulic domain, this study shows that deterministic post-inference label propagation can improve complete point-wise prediction from an existing model, thereby supporting the reuse of available models for domain-specific applications. The resulting response surfaces support two practical uses: site-specific calibration when limited labeled target data are available, and empirically informed initial settings, a rule of thumb, for comparable bridge-LiDAR applications when target-site labels are unavailable.

The catastrophic impact of Hurricane Helene proved that standard FEMA flood maps are often inadequate for assessing risk in complex mountainous terrain. Using Buncombe County, North Carolina, as a case study, this research introduces a replicable framework for siting emergency shelters based on a multi-dimensional Flood Risk Index. By synthesizing HAND-derived inundation data, land-use intensity, and a machine learning-based Socio-Economic Vulnerability Index (SEVI), we mapped the intersection of hazard and vulnerability. Our analysis reveals a significant misalignment—a large portion of the current shelter network sits in high-risk zones, while safer upland corridors in the north and west remain underutilized. This study delivers a data-driven roadmap for disaster preparedness, ensuring that future shelter placement is not only safe from terrain-driven floods but also strategically and equitably located.

Lithium-ion battery (LIB) wireless charging using inductive power transfer (IPT) represents a transformative pathway for transportation electrification. While applications in railway systems remain limited, early studies highlight significant promises for implementation. This paper presents a hybrid energy-supply framework integrating LIB, inductive battery charging (BC) charging, and battery swapping (BS) to support a 20 km heritage trolley excursion between Belmont and Gastonia, NC. A kinematic simulation was developed to estimate traction energy demand, yielding 56 kWh per trip, or 112 kWh for two daily round trips. Finite element analysis (FEA) was conducted to design an LCL-s compensated 3 kW IPT system. Two transmitter configurations were evaluated: W–I ferrite cores (peak coupling ~0.22) and magnetic concrete slabs (~0.20). Although ferrite offers higher efficiency, magnetic concrete demonstrates superior durability and integration potential. Simulation results indicate that wireless charging alone, whether static or dynamic, is insufficient; similarly, a single daily BS strategy provides only 96 kWh. Seven BC-BS hybridization scenarios were evaluated, showing that mid-day swaps combined with either static or dynamic IPT produce a 12–16 kWh surplus. The most practical approach is a one-pack swap supplemented by uniformly distributed static pads, providing energy neutrality. This hybrid pathway ensures operational sufficiency, structural resilience, and compatibility with heritage rail preservation.

Contextual features play a critical role in geospatial object detection by characterizing the surrounding environment of objects. In existing deep learning-based studies of 3D point cloud classification and segmentation, these features have been represented through geometric descriptors, semantic context (i.e., modeled by an attention-based mechanism), global-level context (i.e., through global aggregation), and textural representation (e.g., RGB, intensity, and other attributes). Even though contextual features have been widely explored, spatial contextual features that explicitly capture spatial autocorrelation and neighborhood dependency have received limited attention in object detection tasks. This gap is particularly relevant in the context of GeoAI, which calls for mutual benefits between artificial intelligence and geographic information science. To bridge this gap, this study presents a spatial autocorrelation encoder, namely SA-Encoder, designed to inform 3D geospatial object detection by capturing spatial autocorrelation representation as types of spatial contextual features. The study investigated the effectiveness of such spatial contextual features by estimating the performance of a model trained on them alone. The results suggested that the derived spatial autocorrelation information can help adequately identify some large objects in an urban-rural scene, such as buildings, terrain, and large trees. We further investigated how the spatial autocorrelation encoder can inform model performance in a geospatial object detection task. The results demonstrated significant improvements in detection accuracy across varied urban and rural environments when we compared the results to models without considering spatial autocorrelation as an ablation experiment. Moreover, the approach also outperformed the models trained by explicitly feeding traditional spatial autocorrelation measures (i.e., Matheron’s semivariance). This study showcases the advantage of the adaptiveness of the neural network-based encoder in deriving a spatial autocorrelation representation. This advancement bridges the gap between theoretical geospatial concepts and practical AI applications. Consequently, this study demonstrates the potential of integrating geographic theories with deep learning technologies to address challenges in 3D object detection, paving the way for further innovations in this field.