Improving construction project management through the application of AI, BIM and GIS at the  construction investment project management board: The case of Viet Nam

Appears in:

tsoe 26(1):

Authors:

Van-Nam Vo, Thanh-Tu Huynh

Published:

14th Jul 2026

Abstract:

This study investigates the impact assessment model of AI, BIM, GIS, and organizational capacity on project management efficiency at regional construction investment project management boards in Vietnam. Data was collected from 250 valid survey responses and analysed using Cronbach's Alpha, EFA, and multiple linear regression. The results show that the model explains 76.6% of the variation in project management efficiency (R² = 0.766). BIM has the strongest impact (β = 0.318), followed by AI (β = 0.242), PME (β = 0.215), GIS (β = 0.181), and organizational capacity (β = 0.153). The study confirms the central role of BIM and the decision-making support role of AI and GIS in the digital project management ecosystem.  

---

Simulation and analysis of vehicle dynamics using Simcentre Amesim

Appears in:

tsoe 26(1):

Authors:

Van Dong Doan, Van Chien Pham, Huu Phuoc Nguyen, Van Hoan Cao, Thanh Sa Nguyen

Published:

15th Apr 2026

Abstract:

INTRODUCTION: Vehicle dynamics analysis is a fundamental aspect of modern automotive engineering, directly influencing vehicle stability, ride comfort, and handling performance. With the increasing complexity of vehicle systems, simulation-based approaches have become essential tools for evaluating dynamic behaviour under diverse operating conditions while reducing the cost and time associated with physical testing.  OBJECTIVES: The objective of this paper is to develop and validate a comprehensive vehicle dynamics simulation framework for a B-segment city car, capable of reproducing manufacturer-level ride and handling assessments. The study aims to evaluate key dynamic responses, including yaw behaviour, roll characteristics, tire load transfer, steering effort, and trajectory stability, using an integrated multi-domain modelling approach. METHODS: A full-vehicle model was developed using a multi-domain simulation environment, integrating chassis, suspension, powertrain, braking, and steering subsystems. We represented the chassis using a multi-degree-of-freedom formulation. It incorporates suspension kinematics, elastokinematic compliance, and aerodynamic effects. Functional modelling approaches were employed for the powertrain, braking, and steering systems to ensure numerical robustness. A series of standardized driving manoeuvres, including acceleration, braking, steady-state cornering, crosswind disturbance, and quasi-static steering sweeps, was simulated to assess both transient and steady-state vehicle dynamics responses. RESULTS: The simulation results show that the characteristic velocity reaches approximately 22 m/s, while the roll gradient and understeer gradient are estimated at 0.56°/(m/s²) and 0.31°/(m/s²), respectively. These values fall within the reported ranges for B-segment passenger vehicles, indicating that the model captures key handling characteristics with a relative deviation below 5%. Vertical tire force distribution remained balanced throughout lateral manoeuvres, ensuring stable tire–road contact. Steering torque levels were smooth and monotonic, providing predictable driver feedback, and the vehicle trajectory showed stable and well-controlled path-following behaviour across all tested scenarios. CONCLUSION: The study confirms that the proposed simulation framework is capable of accurately reproducing key ride and handling characteristics of a B-segment city car. The results validate the effectiveness of multi-domain vehicle dynamics modelling for early-stage design evaluation and virtual testing. The approach provides a reliable foundation for vehicle dynamics assessment and can be extended to future studies involving advanced chassis control systems, active suspensions, and electrified powertrains.

---

Design and Manufacture of a Dry HHO (Oxyhydrogen) Electrolysis Module for Emission Reduction in Light Trucks

Appears in:

tsoe 26(1):

Authors:

Trung N. Q., Vang H. B., Dao N. V., Vu V. A., Khac Binh Le

Published:

22nd Jan 2026

Abstract:

Oxyhydrogen (HHO), a mixture of hydrogen and oxygen generated by water electrolysis, has been examined as a combustion-supporting additive due to its clean-burning characteristics. This study describes the design and fabrication of an HHO generation and supply system for Thaco Towner 750 vehicles. The system employs a dry-type electrolyser, achieving an electrolysis efficiency of 89.8% and a gas production rate of approximately 613.3 ml/min at an operating current of 11 A, ensuring stable gas delivery during engine operation. Engine tests were performed to evaluate the effects of HHO supplementation on exhaust emissions when used with RON95 gasoline. Compared to baseline operation, average reductions of 8.7% in CO, 19.57% in CO₂, and 21.1% in HC were observed. Additional measurements conducted under two idle operating modes showed that, at low idle speed, CO, CO₂, and HC concentrations decreased by 19%, 14%, and 24%, respectively. At high idle speed, emission levels were also reduced, though to a lesser extent, with CO, CO₂, and HC decreasing by 6%, 4%, and 9%. These results indicate that HHO addition leads to measurable changes in exhaust gas composition under idle and high-idle conditions. Further investigations involving fuel consumption, engine performance, and standardized driving cycles are necessary to evaluate its broader impact on emission reduction.

---

Development of a Fire Risk Prediction System Based on Deep Learning

Appears in:

tsoe 26(1):

Authors:

Minh Vuong Nguyen, Thanh Duy Nguyen

Published:

14th Jan 2026

Abstract:

Sensor-based safety monitoring systems play an important role in early detection and prevention of fire and explosion incidents at industrial pumping stations. Modern stations integrate multiple sensors such as temperature, humidity, gas, dust, air quality and fire sensors to assess operational status. In practice, continuous monitoring and coordinated analysis of multi-sensor data conducted by experienced professionals can significantly reduce the risk of fire and explosion. However, sustaining continuous expert-based monitoring is challenging due to high operational costs, manpower demands, and practical constraints. Consequently, automated monitoring and forecasting systems are required to deliver continuous and timely risk assessments while minimizing dependence on manual supervision. However, sensor signals often contain noise that is nonlinear and susceptible to environmental influences, making traditional threshold comparison methods unstable. This paper proposes a fire monitoring and forecasting system based on time series data and deep learning model with three status levels including safe, warning and dangerous. The models used and compared include decision trees, artificial neural networks, and long short-term memory networks with a twelve-step time window. Multi-sensor data are normalized and organized into time series windows to reduce noise and reflect fluctuations in real-world conditions. Experimental results on sensor data collected at the pumping station show that the long short-term memory network achieves higher accuracy and precision than the other two models. Contributing to improving the reliability of safety monitoring at the pumping station and creating a basis for practical implementation.  

---

Appears in:

tsoe 25(1):

Authors:

None

Published:

16th Dec 2025

Abstract:

---

Appears in:

tsoe 25(1):

Authors:

None

Published:

15th Dec 2025

Abstract:

---

Appears in:

tsoe 25(1):

Authors:

None

Published:

11th Dec 2025

Abstract:

---

Appears in:

tsoe 25(1):

Authors:

None

Published:

10th Dec 2025

Abstract:

---

Appears in:

tsoe 25(1):

Authors:

None

Published:

8th Dec 2025

Abstract:

---