July 14, 2024
Amin Torabi Jahromi

Amin Torabi Jahromi

Academic Rank: Assistant professor
Degree: Ph.D in Electrical Engineering
Phone: 09171023389
Faculty: Faculty of Intelligent Systems and Data Science


Damage detection in marine structures using environmental signal processing with neural networks
Type Thesis
شناسايي خرابي - سيستمهاي مقاوم در برابر خطا - شبكه عصبي - تشخيص و پيش بيني خطا - شبكه كانولوشني - نظارت بر سالمت سازه؛ شناسايي آسيب مبتني بر ارتعاش؛ شناسايي زير فضا تصادفي؛ شبكه عصبي كانولوشن يك بعدي؛ شبكه عصبي فازي پويا
Researchers ahmad gholami (Student) , Ehsan Bahmyari (Primary advisor) , Amin Torabi Jahromi (Primary advisor) , Valiollah Ghaffari (Advisor)


Monitoring the health of structures and detecting damage has been an interdisciplinary field of interest across various engineering domains. Nowadays, structural health monitoring systems aim to enhance the performance of structures, ensuring human safety, reducing the need for frequent inspections, and cutting maintenance costs. Early and accurate damage detection has always been a primary goal of structural health monitoring programs. In this regard, significant efforts have been dedicated to vibration-based methods, utilizing structural vibration responses to assess their conditions and identify damages. In this study, two methods are employed for structural damage detection. Initially, a subspace-based approach is used for damage detection in the structure, followed by a combination of one-dimensional Convolutional Neural Networks (CNN) and Dynamic Fuzzy Neural Networks (DFNN) for more accurate damage detection and localization. Among various methods for modal parameter identification, subspacebased techniques are considered as one of the most favored and effective choices. These methods exhibit remarkable capabilities in detecting changes in the structure's behavior and specific characteristics, managing both input and output data, and accurately extracting modal parameters. Traditional systems for structural damage detection typically consist of two stages: feature extraction and classification. The performance of such classical systems heavily relies on the selection of features and the dependent classifier. While manually crafted features might be unsuitable for specific structures, they often demand high computational power, leading to unstable performance in classification. Addressing these challenges, this research presents a data-driven solution using deep learning techniques to tackle structural damage detection problems without the need for human-defined physical features. In this approach, abstract features are extracted from the data, embedding profo