Journal of Brilliant Engineering (BEN)

Muhammet Kaan Yesilyurt Ömer Çomaklı

This study aimed to obtain a heat transfer fluid (HTF) using water as the carrier fluid by encapsulating a phase change material (PCM) and to perform hydrothermal characterization of the aqueous solution containing encapsulated PCM (ePCM) in different volume ratios (ePCM-S) as a heat transfer fluid (HTF). Lauric acid (LA) was used as the core PCM in this study due to its melting temperature falling within the nominal operating cell temperature range. LA, encapsulated with a melamine formaldehyde (MF) shell via the in-situ polymerization method, was incorporated into the carrier fluid (water) at volume concentrations of 1%, 1.5%, and 2%. With 1% MF/LA ePCM-S, the specific heat capacity of the HTF in the melting range at 318 K could be improved by 7.58% at the expense of a marginal 2.9% increase in viscosity. It has been demonstrated that ePCMs improve the thermal properties of CF and that ePCM-S can be used as an enhanced HTF when the inlet and outlet temperature range matches or falls within the melting range of the PCM.

https://doi.org/10.36937/ben.2026.41082

Md Mahabub Rahman Md. Belal Hossain

The main aim of the present research is to assess the potential for soil liquefaction in the subsoil in twelve selected locations belonging to the Bogura Sadar area, using field-sponsored geotechnical data. This assessment utilises the well-known, semi-empirical method developed by Seed and Idriss that considers earthquake magnitude, overburden pressure, the corrected value of SPT-N, peak ground acceleration (PGA), soil unit weight and depth of water table. The outcome is that, for a 6.5 MW earthquake, two zones have very high and seven zones have moderate susceptibility to liquefaction, and the remaining areas are less susceptible. The profiles of the factor of safety (FS) show that these values are greater than 1.0 at depths of 1.5 and 12 m for all the zones. However, at depths from 3 m to 9 m, most layers have FS values of less than or equal to 1.0, suggesting that the same seismic event may cause a liquefaction risk. In order to prevent or reduce this risk, surcharge loading is used as an efficient defensive action. The findings confirm that 400 kPa of surcharge guarantees to be on the safe side with respect to liquefaction in all considered zones. In general, the results contribute useful information for mitigating geotechnical hazards and realising seismic resilience in the study region.

https://doi.org/10.36937/ben.2026.41092

Çağlar Özer

Earthquake prediction has been investigated for many decades through multidisciplinary research efforts. Specifically, animal behavioral changes (e.g., ant, dog, frog, horse, mouse, etc.) atmospheric (e.g., cloud movement, thermal infrared, total electron content, very low frequency), geochemical (e.g., CO2, radon, and other gases), geodetic (e.g., GPS, GNSS, InSAR), hydrogeological (e.g., changes in groundwater level), seismic hazard analyses, seismic quiescence period, seismic velocity and seismic velocity ratio, stress analysis, and statistical seismology (e.g., b-value, foreshock distribution, fractal dimension) observations have been extensively applied in studies addressing earthquake prediction. Simultaneously, the development of artificial intelligence and the increase in scientific data, along with the improvement of data evaluation techniques, have further accelerated these studies. This study synthesizes some existing research to highlight the challenges and potential opportunities in short-medium-long term earthquake prediction. Rather than a descriptive approach, this overview presents a structured, concise synthesis of earthquake prediction research by classifying indicators in the literature according to their physical foundations, time-dependent variations within earthquake recurrence intervals, and methodological outcomes. Furthermore, this review is designed primarily for scientists, particularly researchers in earth sciences disciplines, and aims to synthesize the strengths and weaknesses of currently used methodologies, promoting informed evaluation rather than direct deterministic prediction. Recent developments in machine learning and deep learning techniques, particularly those integrating multiple parameters as inputs, are expected to significantly enhance the accuracy of earthquake prediction.

https://doi.org/10.36937/ben.2026.41097

Milufarzana

Maize cultivation is expanding rapidly in Bangladesh due to its importance as both human food and a major feed ingredient for the poultry industry. However, maize planting is still predominantly performed manually, resulting in high labor demand, low efficiency, and increased production costs. To address these challenges, this study presents the design, fabrication, and performance evaluation of a low-cost, manually operated two-row maize planter suitable for smallholder farming systems. The planter employs independent vertical plate roller-type seed metering mechanisms, synchronized with ground drive wheels, to ensure controlled seed delivery and consistent inter-seed spacing. Performance evaluation through laboratory calibration and field testing demonstrated acceptable seed rate regulation, improved spacing uniformity, and stable operational performance at typical walking speeds. The average missing rate was 19.58%, seed damage was 12.02%, multiple seed dropping was 14.63%, and spacing efficiency of the machine was 86.78%. The machine exhibited a theoretical field capacity of 0.238 ha/hr and an effective field capacity of 0.173 ha/hr, resulting in a field efficiency of 72.83%. The operating cost of the machine was estimated to be 398.61 Tk/hr. Economic assessment revealed low fabrication and operating costs, leading to a substantial reduction in maize planting cost compared to manual methods. The results indicate that the developed planter provides a technically sound, economically feasible, and locally adaptable mechanized solution for enhancing maize establishment efficiency in small-scale agricultural systems.

https://doi.org/10.36937/ben.2026.41107

Nwzad Abdulla

This experimental study assessed the ultimate shear capacity of fifteen small-scale deep beams. To isolate the contribution of concrete tensile and compressive mechanisms to shear resistance in the absence of reinforcement, all the beams were made only from plain concrete and subjected to flexural three- and four-point loading. The main variables included the type of load, and the ratio of shear span to overall depth (a/h). Test results showed that as the a/h ratio decreased, the experimental shear's variability increased. The shear capacity as the absolute peak of tested specimens was most effectively increased when the a/h ratio was 1. Strength improved less when the a/h ratio was increased over this threshold. Moreover, only thirty-five models among the existing shear equations for normal reinforced concrete beams were suitable to predict the shear capacity of small-sized plain concrete deep beams. The majority of the thirty-five shear models’ predictions are conservative. Nevertheless, the remaining models produced predictions that were unsafe. All the 35 shear models failed to capture the experimental behavior of group A-3 and reflect the effect of keeping a/h constant on the predicted shear values. Finally, a simplified strut-and-tie-based shear model (STM)is proposed. Moreover, the predictive STM expression was modified, calibrated for the tested beam range in group three. The accuracy of the proposed and modified STM model was verified by comparison with experimental testing results, yielding better predictions compared with the existing 35 shear models with an absolute average error value of only 12.9%.

https://doi.org/10.36937/ben.2026.41109