Journal of Science and Transport Technology

GIS Based Soil Erosion Susceptibility Assessment Using Deep Learning Models: A Case Study in the Mountainous Region of Nghe An, Vietnam

2025-12-18T03:26:48+00:00

In this study, the main objective is to evaluate soil erosion susceptibility in the mountainous region of Nghe An Province using three deep learning models: Long Short-Term Memory (LSTM), Deep Neural Network (DNN), and Deep Attention Network (DaNet). A total of 685 erosion points were identified from field surveys and satellite imagery, and nine spatial conditioning factors were used, including slope, aspect, curvature, elevation, rainfall, Normalized Difference Vegetation Index (NDVI), soil type, distance to faults, and geology. The dataset was split into 70% for training and 30% for validating. Various validation metrics including area under the ROC curve (AUC) were used for validation and comparison of the models. The results show that among the tested models, DaNet showed the highest predictive performance, achieving an AUC of 0.936 on the training dataset and 0.852 on the validating dataset compared with other deep learning models (DNN and LSTM). The susceptibility map produced by DaNet demonstrated strong spatial alignment with real-world erosion occurrences, with 59.88% of observed erosion points located in the very high susceptibility class and 18.52% in the high class, totaling 78.4% of all erosion events. These results confirm DaNet’s effectiveness in capturing complex spatial patterns and delivering reliable erosion risk predictions, supporting its use for land-use planning.

Design and Inverse Kinematics of Continuum Robots

2025-12-01T14:04:05+00:00

Continuum robots, characterized by their hyper-redundant and flexible structures, have gained significant attention in fields such as minimally invasive surgery, remote inspection, and soft manipulation. Their complex design and highly nonlinear kinematic behavior present substantial modeling and control challenges. This paper presents a complete framework encompassing the design, modeling, and inverse kinematics solution for a novel two-segment, tendon-driven continuum robot utilizing an elastic spring backbone for enhanced compliance and structural simplicity. A constant curvature forward kinematic model is presented. Subsequently, an efficient numerical approach for solving the challenging inverse kinematics problem is introduced by adapting the Jacobian-based Newton-Raphson method and incorporating the Moore-Penrose Pseudoinverse. This strategy effectively manages the robot's redundancy, ensuring smooth and reliable trajectory generation. Experimental verification confirms the robot's feasibility, demonstrating that the system successfully navigates along the trajectories computed by the inverse kinematics, thus validating the reasonableness of the kinematic model and ensuring seamless and smooth operation. These findings provide a robust foundation for improving motion planning of simple continuum robot platforms in practical applications.

Vehicle Classification Using Combined Laser Rangefinder and Pyroelectric Infrared Sensors in a Real Dynamic Environment

2025-12-11T08:34:22+00:00

This paper presents a vehicle classification approach for real-world dynamic environments based on sensor fusion between a laser rangefinder (LRF) and a pyroelectric infrared (PIR) sensor. By integrating geometric shape information from the LRF with thermal distribution patterns captured by the PIR sensor, the system extracts distinctive features that effectively suppress noise introduced by external environmental variations. A lightweight neural network is developed for classification, achieving a minimum accuracy of 91% for specific vehicle types and an average accuracy of 94% across all categories. Owing to its high accuracy and low computational cost, the proposed model is well-suited for implementation in portable embedded platforms, functioning as intelligent measurement nodes within Intelligent Transportation Systems (ITS).

Durability of reinforced geopolymer concrete using coral aggregates and seawater under accelerated corrosion

2025-09-25T08:26:50+00:00

This study evaluates the corrosion resistance of geopolymer concrete made with coral sand, coral rock, and seawater, materials abundant on offshore islands. Three mixtures were designed using varying proportions of fly ash and blast furnace slag as binders. Compressive strengths after 28 days were 20.11 MPa, 25.32 MPa, and 30.15 MPa for GPS-1, GPS-2, and GPS-3, respectively. Accelerated corrosion tests (NT Build 356) revealed that higher strength samples exhibited lower average current intensity before cracking (5.49 mA for GPS-1 vs. 4.68 mA for GPS-3) and longer times to cracking (204 h vs. 324 h). Chloride ion concentrations near the steel at the onset of cracking were similar across mixtures (0.53 % – 0.58 % by dry concrete weight), but initial chloride contents were lower in high-strength samples (0.08 % in GPS-3 vs. 0.22 % in GPS-1), indicating better resistance to ion penetration. Microstructural analyses (SEM, FTIR, XRD) confirmed that GPS-3 had a denser matrix, more developed geopolymer gel, and reduced presence of unreacted crystalline phases. These results demonstrate that optimizing binder content and microstructure enhances both mechanical performance and corrosion resistance, making geopolymer concrete with marine-derived materials a viable solution for durable coastal infrastructure.

Vehicle and time specific crash modelling on selected rural highway curves using geometric and speed parameters: A transformed linear regression approach

2025-10-28T09:13:52+00:00

This study develops vehicle and time-specific crash rate prediction models for rural highway curves using high-resolution geometric and speed data. A 30km segment of State Highway-1 in Karnataka, India, encompassing 32 horizontal curves, served as the study site. Detailed data collection included 10 years of crash records, traffic volume count, LiDAR-based geometric features, and spot speeds recorded from laser speed cameras. Distinct models were built for motorized two-wheelers (MTW), passenger cars (CAR), heavy commercial vehicles (HCV), and for both daytime and nighttime conditions. The study offers a novel contribution by incorporating nighttime crash rate modelling rarely addressed due to challenges in data availability, and by developing disaggregated models for multiple vehicle classes. A backward stepwise regression (BSR) approach with square root transformation was employed, ensuring model transparency and interpretability. Sight-distance deficiency consistently emerged as the most influential predictor of crash rate, highlighting the critical role of visibility on curved segments. Validation through Leave One Out Cross Validation (LOOCV) confirmed acceptable predictive performance (R² = 0.43-0.80), with residuals exhibiting normal distribution. The findings underscore the importance of curve geometry and visibility in crash risk and provide actionable insights for design audits and safety interventions on rural highways.

Free vibration and buckling analysis of bidirectional functionally graded plates with integrated piezoelectric layers

2025-11-23T09:02:35+00:00

In this paper, the vibration and buckling of functionally graded material plates with integrated piezoelectric layers are investigated. The material properties of the plate are assumed to vary along both the length and thickness directions (2D-FGM) according to a power-law while the electric potential in each piezoelectric layer varies linearly along the thickness direction. To perform this analysis, a finite element model based on the higher-order shear deformation refined plate theory (HSDT-4) and four-node rectangular element is developed. The governing equations are obtained by applying Hamilton's principle. The numerical results have demonstrated the convergence, accuracy and reliability of the established model. In addition, the influence of material parameters, geometry and mechanical boundary conditions on the vibration frequency and critical force of the 2D-FGM/PIE plate under both closed-circuit (Clocc) and open-circuit (Opcc) conditions is investigated and evaluated in detail.

Investigation of the control system for air supply during transient operation of a turbocharged diesel engine

2025-12-13T07:22:15+00:00

In the context of increasingly stringent emission standards and higher fuel efficiency requirements for turbocharged diesel engines, optimizing air-path control, particularly during transient operating conditions, has become essential. This study focuses on the design and evaluation of a dedicated air supply controller for transient operation to enhance the response and stability of a turbocharged diesel engine. The controller was developed to regulate the intake airflow dynamically through an auxiliary bypass line, compensating for turbocharger lag and maintaining stable boost pressure during rapid load or speed changes. Experimental results demonstrate that the proposed air supply controller significantly improves transient performance. Specifically, the transition time was reduced from 3 seconds to 1.5¸2 seconds, corresponding to an improvement of 16.6¸40%, while speed fluctuation Δn decreased by 8.5¸24%, depending on the load level. The best improvement was achieved at medium to high loads (50¸65%), indicating rapid and stable turbocharger response with the assistance of the supplemental air supply system. At low loads, the controller’s impact was less pronounced due to lower boost pressure and airflow rates. The results confirm that the proposed air-path controller for transient operation is a feasible and effective solution to shorten transient response time, reduce speed fluctuation, improve engine stability, and potentially reduce emissions in turbocharged diesel engines operating under rapidly varying conditions.

GIS-Based Flow-R Model for Debris Flow Susceptibility Mapping: A Case Study from Muong Bo, Lao Cai, Vietnam

2025-12-16T01:28:42+00:00

Landslides and debris flows are frequent hazards in Vietnam’s mountainous regions, causing severe socio-economic damage that has intensified under climate change and extreme rainfall conditions. This study applies the open-source GIS-based Flow-R model to assess debris-flow susceptibility and runout characteristics in Muong Bo Commune, Lao Cai Province, northwestern Vietnam (specifically focusing on the highly vulnerable area around the Nam Cang stream). The methodology involved developing a geospatial database and simulating potential initiation and propagation zones. A major debris-flow event that occurred on 12 September 2023 was used for model calibration and validation. Field validation confirmed the model’s robust capability to simulate flow runout distances based on observed data. Seventy percent of the mapped debris-flow initiation points were used for calibration, and 30% for validation[/RED], and model performance was evaluated using the Area Under the ROC Curve (AUC). The Flow-R model achieved an AUC value of 0.868, indicating good predictive capability[/RED], while 48.5% of observed debris-flow initiation points were correctly predicted. Results demonstrate that Flow-R effectively delineates high-susceptibility source zones and plausible debris-flow runout paths, particularly for medium- to large-scale events. The novelty of this study lies in the first integrated application of Flow-R, combined with systematic field validation in northwestern Vietnam, and the coupling of detailed geological–geomorphological characterization with susceptibility and runout modeling, providing a transferable framework for debris-flow assessment in data-scarce mountainous regions.