Journal of Science and Transport Technology

Development and Evaluation of DUT Vibro: A High-Precision Vibrating Wire Sensor Readout

Van-Lam Cao — 2025-07-22

This study presents the development and evaluation process of a vibrating wire sensor readout named DUT Vibro, with both hardware and software entirely developed by Vietnamese researchers. The primary objective of this paper is to evaluate the efficacy of two methods to determine the resonant frequency of the vibrating wire to facilitate precise strain calculations. In this study, parabolic interpolation is investigated to improve the accuracy of the resonant frequency of a steel wire, addressing the limitation of Fast Fourier Transform (FFT) constrained by the storage capacity of the microcontroller. In addition, the determined resonant frequency of DUT Vibro is compared with a commercial DIGIANGLE (DAS). Furthermore, two stimulation signals—sine and square waves—were employed to compare their impact on measurement accuracy. The results indicate that the parabolic interpolation method yields the lowest standard deviation, closely aligning with the DAS readout, and demonstrates stability across both low and high load conditions. In contrast, the FFT method exhibits greater error variability, particularly in the medium load range, due to the influence of noise and non-linearities in the response signal. The sine wave stimulus combined with parabolic interpolation achieves the highest accuracy. The measurement system maintains high linearity, with linearity errors below 0.5% of full scale (FS), and the lowest linearity error is 0.129% FS when using a sine wave stimulus. Linear regression analysis reveals a slope coefficient of approximately 0.052, reflecting a linear relationship between load and measured strain. Based on these findings, the parabolic interpolation method has been integrated into the DUT Vibro readout, meeting stringent accuracy requirements for strain measurement applications.

Accurate and Interpretable Prediction of Marshall Stability for Basalt Fiber Modified Asphalt Concrete using Ensemble Machine Learning

Huong Giang Thi Hoang — 2025-07-22

Marshall Stability (MS), a parameter that reflects the load-bearing capacity and deformation resistance of asphalt concrete, is critical for pavement performance and durability. This study assesses the predictive capability of five tree-based machine learning (ML) algorithms - Decision Tree Regression, CatBoost Regressor, Random Forest Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Machine - in estimating the MS of basalt fiber - modified asphalt concrete (BFMAC). A compiled database of 128 samples was used for model training. Models were optimized with GridSearchCV and 5-fold cross-validation (CV), assessed via multiple statistical metrics, while SHAP analysis provided model interpretability. Among the tested models, Random Forest Regression (RFR) demonstrated the highest predictive accuracy (R² ≈ 0.922, RMSE ≈ 0.748 on the test set) and exhibited strong generalization capability. Interpretability analysis revealed that aggregate gradation (specifically, percentage of aggregate passing 2.36 mm and 4.75 mm sieves) and binder penetration were the most significant factors influencing MS prediction, followed by fiber content. This research underscores the potential of interpretable ML models, such as RFR, in accurately predicting MS, offering a viable alternative to conventional experimental methods for pavement material assessment.

Capillary Wick Irrigation Technique: A Sustainable Hydraulic Innovation for Water-Efficient and Climate-Resilient Infrastructure in Arid Regions

Uttamkumar Vyas — 2025-07-22

Groundwater is a vital resource supporting agriculture, industry, and rural livelihoods. However, changing climatic patterns, erratic rainfall, and unsustainable human activities have accelerated groundwater depletion, posing major challenges to sustainable water management. In response, this study introduces the Capillary Wick Irrigation Technique (CWIT) an innovative, passive irrigation system designed to enhance water use efficiency and promote sustainable agricultural infrastructure, particularly in arid and saline-prone environments. Unlike conventional drip systems, CWIT utilizes capillary action through specially engineered wick structures embedded in a subsurface pipe network, eliminating the need for external energy or technical operation. Experimental trials on fennel crops under controlled saline conditions revealed a distinct hemispherical wetting front, extending vertically up to 50 cm and horizontally up to 30 cm, with soil moisture retained for up to 12 days without additional irrigation. Field studies conducted in Velavadar, Surendranagar District, Gujarat, further validated the technique, showing approximately 17.4% water savings over drip irrigation and nearly 85% compared to traditional surface methods. CWIT also enhanced crop yield efficiency, reduced evaporation and runoff, and supported soil conservation and groundwater recharge. Offering a low-cost, scalable, and environmentally resilient solution, CWIT presents strong potential for integration into rural water systems and climate-resilient farming, particularly in water-scarce regions.