KAUST Professor J. Carlos Santamarina and postdoctoral fellow Junghee Park received the 2019 Tarek Al-Kasabi Award for Excellence in Civil Engineering in Saudi Arabia. The award, administered by King Saud University, recognizes outstanding contributions to civil engineering within the Kingdom. Santamarina and Park were nominated by Dr. Abdullah Almajed from King Saud University and received a plaque, certificate, and a cash prize of SAR 100,000. Why it matters: Recognition of KAUST faculty highlights the university's contribution to civil engineering research and development in Saudi Arabia.
This paper presents a 2-D convolutional neural network (CNN) approach for damage detection in steel frame structures, using raw acceleration signals as input. The method employs a network of lightweight CNNs, each optimized for a specific element, to enhance accuracy and speed. The proposed framework is validated using the Qatar University Grandstand Simulator (QUGS) benchmark data. Why it matters: The research offers a promising AI-driven solution for real-time structural health monitoring, with potential applications for infrastructure maintenance and safety in the GCC region.
This paper introduces a hybrid deep learning and machine learning pipeline for classifying construction and demolition waste. A dataset of 1,800 images from UAE construction sites was created, and deep features were extracted using a pre-trained Xception network. The combination of Xception features with machine learning classifiers achieved up to 99.5% accuracy, demonstrating state-of-the-art performance for debris identification.
J. Carlos Santamarina, a Professor of Earth Science and Engineering at KAUST, is researching geomaterial behavior and subsurface processes. His work focuses on energy geo-engineering, resource recovery, and geological storage of energy waste. He uses particle-level experiments, numerical methods, and monitoring systems to understand coupled thermo-hydro-bio-chemo-mechanically processes. Why it matters: This research contributes to energy sustainability and addresses global energy challenges through advanced geotechnology.
Researchers at KFUPM have developed a system for pothole detection and characterization using a YOLOv8-seg model and depth estimation. A new dataset of images and depth maps was collected from roads in Al-Khobar, Saudi Arabia. The system combines segmentation and depth data to provide a more comprehensive pothole characterization, enhancing autonomous vehicle navigation and road maintenance.
This paper details an autonomous cooperative wall-building system using UAVs developed for Challenge 2 of the MBZIRC 2020 competition. The system employs scanning, RGB-D detection, precise grasping, and multi-UAV coordination to place bricks on a wall. The CTU-UPenn-NYU approach achieved the highest score in the competition by correctly placing the most bricks. Why it matters: This demonstrates advanced capabilities in robotics and autonomous systems relevant for construction and infrastructure development in challenging environments.
KAUST held an Engineering Forum event on January 23. The event recognized members of the Technical Committee for their support in completing KAUST standards. No further details about the content of the forum are provided. Why it matters: Such events likely contribute to the development of engineering expertise and standards within Saudi Arabia.
KAUST and Partanna have launched a 12-month R&D partnership to enhance CO2 removal in concrete manufacturing. The collaboration will integrate Partanna’s formula with KAUST’s Direct Air Capture (DAC) technology. Partanna's patented concrete avoids Portland Cement and uses a binder made from natural and recycled materials, enabling CO2 absorption. Why it matters: This partnership highlights Saudi Arabia's commitment to sustainable construction and carbon-negative technologies, potentially revolutionizing building practices in the region and beyond.