RMUTL Engineering Journal

Comparison of Elevation in a straight line from Leveling with Level and Global Navigation Satellite Systems

2025-07-19T09:09:43+07:00

Accurate elevation determination is essential in civil engineering for ensuring the structural integrity of constructed facilities. Traditional optical leveling, while highly precise, requires transferring elevation values from existing benchmarks, which can be time-consuming when the benchmarks are located far from the project area. Recent advances in Global Navigation Satellite Systems (GNSS) technology-especially using static surveying techniques with post-processing and geoid correction-have enabled the possibility of determining elevation data more efficiently. This study evaluates the accuracy of GNSS-based elevation measurements compared with conventional third-order leveling methods, focusing on a straight-line transect of 2,201 meters at the Agricultural Technology Research Institute, Rajamangala University of Technology Lanna. The GNSS survey was conducted using the static method, with observed data post-processed and converted from ellipsoidal to orthometric height using the Thailand Geoid Model 2017 (TGM2017). The optical leveling was performed to third-order standards using both aluminum and Invar leveling rods. Results show that the elevation difference obtained from GNSS measurements deviated by only 6 millimeters from the optical leveling result, which falls within the allowable error margin for third-order leveling standards. The findings confirm that GNSS, when used with a validated geoid model, is a viable alternative for elevation determination in engineering applications under the Thai vertical datum, particularly for establishing local control benchmarks with reduced fieldwork time and cost.

Production Improvement through Fixture Design: A Case Study of an Agricultural Machinery Parts Manufacturer

2025-06-04T08:26:39+07:00

This case study focuses on a manufacturing company producing agricultural machinery parts. The company faced a significant challenge: it could not meet customer demand due to inefficiencies in the production process. The primary objective of this study was to enhance the production process by implementing a new Jig Fixture and leveraging the principles of karakuri kaizen. Through a thorough analysis of the production process, the Customer revealed that the daily demand was 350 units, while the company could only produce 280 units per day. The researchers employed engineering principles to address this issue, including 7 QC Tools and Failure Mode and Effects Analysis (FMEA). By identifying the root causes of the production bottlenecks, the team developed and implemented solutions involving a new workpiece holding device and Karakuri Kaizen automation. The results of these improvements were substantial. The cycle time per unit decreased from 96.22 seconds to 75.52 seconds, enabling the company to produce 362 daily units. The total production distance was reduced from 30.2 meters to 27.7 meters.

Time Series-Based Predictive Modeling of PM2.5 Levels in Chiang Mai, Thailand

2025-04-18T11:23:38+07:00

The purpose of this study is to use a variety of models to create prediction models for Chiang Mai's PM2.5 levels. To improve the accuracy of our predictions, we take into account outside variables that might influence PM2.5 levels. Among the variables that we include in the data are PM2.5 concentrations, temperature, wind speed, precipitation, cloud cover, relative humidity, and other external factors. Before using the model, the researcher used basic statistical analysis, seasonal analysis, and stationary analysis to assess the data. The team of researchers carried out both data transformation and data cleansing. We tested the ARIMA, SARIMA, and SARIMAX forecasting models. First, we use ARIMA to forecast and assess results. The SARIMA model more accurately captured the seasonal connection in the data when we included a seasonal component. The model was able to forecast PM2.5 levels more precisely at times when seasonal patterns recurred thanks to this improvement. As the last step, we used the SARIMAX model to improve performance by adding exogenous variables. In the end, we assessed the accuracy and performance of each forecast using the MAE and RMSE numbers. The ARIMA model yielded MAE values of 7.34 and RMSE 7.95. The SARIMA model MAE values of 5.76 and RMSE 6.54. The SARIMAX model, when incorporating humidity, had the lowest MAE values of 4.36 and RMSE 5.25, representing improvements MAE of 40.6% and RMSE 34% compared to ARIMA.

Machine Learning Model Development for Water Level Forecasting at P.1 Station, Chiang Mai Province

2025-05-27T10:26:30+07:00

This research applies machine learning models to forecast water levels at the P.1 station (Nawarat Bridge), Chiang Mai Province, both for 6 and 9 hours in advance. The objectives are to identify suitable variables and to create models for forecasting water levels at the P.1 station. The study utilizes historical hourly water level data from the P.1 and P.67 stations, combined with Moving Average (MA) and Exponential Moving Average (EMA) data covering the years from 2017 to 2024, which has amounted to a total of 66,180 records. The dataset is divided into a training set (80%) and a testing set (20%). The experiment design involves creating artificial neural network models based on historical data from one station (P.1) and two stations (P.1 and P.67). The models consist of those using only historical data, those using historical data combined with MA, and those using historical data combine with EMA, resulting in a total of 12 models. The structure of each model was optimized to achieve the best forecasting results. The results indicate that the best model for the 6-hour forecasting is the P.1_6 + P.67_6 + EMA model. This model utilizes 18 input variables, with 6 and 2 nodes in the first and second hidden layers, respectively, and 1 output node. This model achieved a Mean Absolute Error (MAE) of 0.0405, a Root Mean Square Error (RMSE) of 0.0578, and a coefficient of determination (R²) of 0.9859. For the 9-hour forecasting, the best model is the P.1_9 + P.67_9 + EMA model, which also employs 18 input variables, with 5 and 4 nodes in the first and second hidden layers, respectively, and one output node. This model achieved a MAE of 0.0562, an RMSE of 0.0776, and an R² of 0.9746. Both models utilize data from two stations combined with EMA.

Design and Analysis of an SME-Level Pulsed Electric Field Device for Extracting Bioactive Compounds from Black Rice

2025-11-06T12:13:22+07:00

This research developed a small-scale Pulsed Electric Field (PEF) machine to extract bioactive compounds from black rice grown in Doi Saket and assessed its extraction efficiency. The primary voltage ranged from 0 to 220 V, with secondary high voltage AC and DC outputs spanning from 0.68 to 15.00 kV and 0.96 to 21.21 kV, respectively. The experiment used a ratio of 1 kg of black rice to 2 L of water, with electric field strengths of 4, 5, and 6 kV/cm at a frequency of 1 Hz, varying the number of pulses between 1,000, 3,000, and 5,000. Results showed that 6 kV/cm and 5,000 pulses yielded the highest anthocyanin content (3.23±0.04 mg/L), which significantly differed from other conditions (p<0.05). The highest antioxidant levels were observed at 4 kV/cm for 1,000 pulses and 5 kV/cm for 1,000 pulses (77.86±0.67% and 76.91±0.71%, respectively), though these levels decreased in comparison to traditional extraction, showing statistical significance (p<0.05). However, a higher pulse count led to an increase in anthocyanin content. Furthermore, increased electric field intensity raised antioxidant yields, though this effect plateaued beyond a certain point. Optimal extraction conditions were achieved at 5 kV/cm and 3,000 pulses, yielding anthocyanin and antioxidant contents of 1.02±0.04 mg/L and 59.72±0.34%, respectively. The extraction process was most effective when temperatures remained below 50°C (without a cooling system) and pressure was kept at 1 atm. Additionally, the study developed a PEF prototype for bioactive compound extraction from black rice.

Monitoring Water Turbidity in the Chiang Rai Reach of the Mekong River Using Sentinel-2 NDTI

2025-09-23T15:19:45+07:00

The Mekong River plays a vital role in regional ecology, food security, and water governance, yet turbidity monitoring in its upper reaches remains limited. The Chiang Rai sector, located at the first upstream entry point of the Lower Mekong Basin, functions as a sediment and flow gateway to downstream countries; however, no systematic satellite-based turbidity assessment has previously been conducted for this area. This study addresses this gap by applying the Normalized Difference Turbidity Index (NDTI) derived from Sentinel-2 MSI imagery to investigate spatial and seasonal turbidity dynamics from 2019 to 2024. Multi-temporal satellite datasets were processed using Google Earth Engine and integrated with monthly rainfall (TerraClimate) and water surface area extracted using the Modified Normalized Difference Water Index (MNDWI). Results revealed a clear monsoonal pattern, with turbidity peaking between June and September and declining during the dry season (November-April). Regression analysis showed a moderate correlation between rainfall and turbidity (R² = 0.37), while a stronger association with water surface area (R² = 0.514) indicates the dominant influence of hydromorphological processes such as sediment resuspension and floodplain connectivity. Although the absence of field-based turbidity measurements represents a key limitation, the findings demonstrate the potential of Sentinel-2-based NDTI as a cost-efficient monitoring tool in data-scarce transboundary basins. This study provides the first satellite-derived turbidity baseline for the northern Mekong and offers a practical framework to support basin-wide water-quality monitoring and policy decision-making under increasing hydropower regulation and climate variability.