Browsing by Author "Nasraldeen, Shvan Tahir"

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Citation - WoS: 1
Citation - Scopus: 1
Comparative Evaluation of YOLO and Gemini AI Models for Road Damage Detection and Mapping
(MDPI, 2025) Demirel, Zeynep; Nasraldeen, Shvan Tahir; Pehlivan, Oyku; Shoman, Sarmad; Albdairi, Mustafa; Almusawi, Ali
Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms-specifically YOLOv8 and YOLOv11-for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform's uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments.
Citation - WoS: 3
Citation - Scopus: 3
Viscoelastic and Fatigue Performance of Modified Bitumen Using Polymer and Bio-Based Additives: a Comparative Study
(Mdpi, 2025) Almusawi, Ali; Nasraldeen, Shvan Tahir; Albdairi, Mustafa; Norri, Hussein H.
This study investigates the performance and viscoelastic characteristics of unmodified and modified bitumen using Performance Grading, Frequency Sweep, and Linear Amplitude Sweep tests. The bitumen modifications include styrene-butadiene-styrene at 4% and 5%, animal bone powder at concentrations of 4%, 5%, and 6%, and waste cooking oil at 3%, 4%, and 5%. Performance Grading tests were conducted to evaluate the high-temperature performance of bitumen samples. Frequency Sweep tests were used to analyze the complex shear modulus and phase angle, providing insights into stiffness and elasticity. The Linear Amplitude Sweep tests assessed fatigue resistance by monitoring the degradation of the complex shear modulus under cyclic loading. Styrene-butadiene-styrene and animal bone powder significantly enhanced stiffness, elasticity, and fatigue resistance, with styrene-butadiene-styrene-modified samples achieving the highest performance grades and fatigue resistance. Waste cooking oil-modified bitumen reduces stiffness and fatigue resistance, indicating it primarily acts as a plasticizer. Styrene-butadiene-styrene and animal bone powder are effective modifiers for improving bitumen's mechanical and fatigue properties and are suitable for demanding applications. In contrast, waste cooking oil compromises structural performance despite its environmental benefits, making it less suitable for high-performance use.