Dr. Mohammad F Tamimi, a structural engineering specialist, holds a Bachelor's degree from Yarmouk University (2016) and a Master's degree from Jordan University of Science and Technology (2018). He completed his Ph.D. at Oklahoma State University (USA) in 2022, where he worked under Dr. Mohamed Soliman between 2018 and 2022. His research during this period was primarily centered on computational analysis related to the reliability and sensitivity of civil and maritime structures, with the unique approach of using machine learning-assisted simulations. His research integrates large-scale testing, experimental mechanics, and probabilistic analysis to characterize system performance under combined actions of gradual deterioration and sudden extreme events. In 2023, Dr. Tamimi joined the faculty of Yarmouk University (in Jordan), continuing his groundbreaking work in the field. His research topics have spanned the analysis of fatigue and crack propagation in structures, assessment of the reliability and potential risks of various structural systems, and understanding the impact of environmental conditions, particularly climate change, on the structural integrity of maritime structures.
AbstractPredicting the crack propagation behavior in welded stiffened panels, which are often used in marine, civil, and aerospace structures, has been challenging. The presence of welded stiffeners creates complex stress conditions that need to be properly considered while predicting the crack growth. Another challenge is the presence of significant uncertainties, the variability in the mechanical properties of materials and geometric parameters are among the main contributors to these uncertainties. In this context, accurate quantification of uncertainties associated with these parameters is crucial for the accurate prediction of the fatigue service life and for ensuring structural integrity and operational reliability throughout the service life. This paper conducts a sensitivity analysis to evaluate the effect of relevant input parameters, covering geometric and mechanical properties, on crack propagation behavior. The geometric parameters include the main panel thickness and stiffener characteristics while the mechanical properties cover the crack propagation regression parameters, modulus of elasticity, and yield strength. Furthermore, the effect of the geometric uncertainties on the fatigue reliability of these panels is also quantified. A 3-D finite element analysis, an artificial neural network, and an elastic-plastic crack growth model are integrated to predict crack propagation under cyclic loading. The sensitivity and reliability assessment approach is illustrated on stiffened panels with T- and L-shape stiffeners subjected to axial tensile fatigue loading. The proposed approach is validated using experimental test data reported in literature.
|Room 6||Wednesday 29th November||15:30-16:00||Mohammad Tamimi|
S02-2 Big Data and Artificial Intelligence
100 - Uncertainty Quantification of the Crack Propagation Behavior in Welded Stiffened Panels Using a Hybrid System Integrating Artificial Neural Networks and Finite Element Analysis