This conceptual model, inspired by the Rose Window in Gothic architecture, embodies intricate geometric patterns that reflect divine harmony and balance. Its symmetrical structure and the interplay of light passing through stained glass create a sense of movement, enhancing the sacred and mystical atmosphere. The composition evokes a celestial presence, like a window to heaven.
โครงการนี้เกิดจากแรงบันดาลใจในการสำรวจความสัมพันธ์ระหว่างศิลปะสถาปัตยกรรมกอทิกและการออกแบบสมัยใหม่ โดยนำกระจกสีมาใช้เป็นแรงบันดาลใจในการศึกษาความสมดุลและความศักดิ์สิทธิ์ของลวดลายและแสง เพื่อแสดงให้เห็นถึงความสำคัญของแสงและวัสดุในการสร้างบรรยากาศที่มีความลึกและความเคลื่อนไหว
คณะวิทยาศาสตร์
Air pollution, particularly PM2.5, is a major environmental and public health concern in Bangkok. Instead of predicting PM2.5 levels, this project aims to identify the most significant factors influencing PM2.5 concentration. By analyzing historical air quality, weather, and other environmental data, we will determine which variables—such as temperature, humidity, wind speed, or other pollutants—have the greatest impact on PM2.5 fluctuations.
วิทยาเขตชุมพรเขตรอุดมศักดิ์
Durian is an important economic crop in Thailand that is affected by foliar diseases such as rust, leaf blight, and leaf spot. These diseases reduce the quality of the yield and increase management costs. This research focuses on developing AI software for screening durian leaf diseases by applying deep learning technology to classify different types of leaf lesions.
คณะวิศวกรรมศาสตร์
This research suggested natural hemp fiber-reinforced ropes (FRR) polymer usage to reinforce recycled aggregate square concrete columns that contain fired-clay solid brick aggregates in order to reduce the high costs associated with synthetic fiber-reinforced polymers (FRPs). A total of 24 square columns of concrete were fabricated to conduct this study. The samples were tested under a monotonic axial compression load. The variables of interest were the strength of unconfined concrete and the number of FRRlayers. According to the results, the strengthened specimens demonstrated an increased compressive strength and ductility. Notably, the specimens with the smallest unconfined strength demonstrated the largest improvement in compressive strength and ductility. Particularly, the compressive strength and strain were enhanced by up to 181% and 564%, respectively. In order to predict the ultimate confined compressive stress and strain, this study investigated a number of analytical stress–strain models. A comparison of experimental and theoretical findings deduced that only a limited number of strength models resulted in close predictions, whereas an even larger scatter was observed for strain prediction. Machine learning was employed by using neural networks to predict the compressive strength. A dataset comprising 142 specimens strengthened with hemp FRP was extracted from the literature. The neural network was trained on the extracted dataset, and its performance was evaluated for the experimental results of this study, which demonstrated a close agreement.