a b s t r a c tElastic, anisotropic, non-homogeneous, prismatic beams are solved through a semi-analytical formulation. The resulting variational formulation is solved with a finite element discretization over the cross-section, leading to a set of Hamiltonian ordinary differential equations along the beam. Such a formulation is characterized by a group of generalized eigenvectors associated to null eigenvalues, which are shown to combine rigid body motions and the classical De Saint-Venant's beam solutions. The related generalized deformation parameters are identified through the amplitude of the deformable generalized eigenvectors. Results obtained from the analysis of both isotropic and composite beams are presented.
In Spring 2020, the College of Engineering at San José State University (SJSU) conducted a comprehensive analysis of the impact of COVID-19 on faculty who were forced to transition to an online learning environment. The purpose of this study is to assess the impact of COVID-19 on faculty teaching methods, assessment methods, and personal well-being. The study was a combination of a quantitative survey and a qualitative study using interviews of engineering faculty teaching in Spring 2020. In the first part, we surveyed all faculty teaching during Spring 2020 in the SJSU College of Engineering about their experiences after the move to 100% online instruction in March 2020. In the second part of the research, we interviewed 23 faculty members to obtain a more in-depth understanding of their experiences during the move online in Spring 2020. Overall, 98 faculty participated in the survey: lecturers (58), tenure-track (18), tenured (13), adjunct (1), and Teaching Associates (1). The faculty reported being worried about their family and their students’ well-being. In addition, 65% of faculty members reported either a moderate or a great deal of stress related to the shelter in place, and this percentage was higher for female faculty (74%) and for tenure-track faculty (83%). Overall, faculty members felt that they had their classes under control most of the time and that the transition to online teaching was positive, even if they felt they had too much work to do and felt always in a hurry and under pressure. From a teaching perspective, the interviews highlight that faculty members’ main concerns focus on testing and assessment and students’ engagement. Overall, SJSU College of Engineering faculty members felt under stress in the transition to online teaching, especially the tenure-track faculty members, but were able to transition their classes with ease.
Current maintenance intervals of mechanical systems are scheduled a priori based on the life of the system, resulting in expensive maintenance scheduling, and often undermining the safety of passengers. Going forward, the actual usage of a vehicle will be used to predict stresses in its structure, and therefore, to define a specific maintenance scheduling. Machine learning (ML) algorithms can be used to map a reduced set of data coming from real-time measurements of a structure into a detailed/high-fidelity finite element analysis (FEA) model of the same system. As a result, the FEA-based ML approach will directly estimate the stress distribution over the entire system during operations, thus improving the ability to define ad-hoc, safe, and efficient maintenance procedures. The paper initially presents a review of the current state-of-the-art of ML methods applied to finite elements. A surrogate finite element approach based on ML algorithms is also proposed to estimate the time-varying response of a one-dimensional beam. Several ML regression models, such as decision trees and artificial neural networks, have been developed, and their performance is compared for direct estimation of the stress distribution over a beam structure. The surrogate finite element models based on ML algorithms are able to estimate the response of the beam accurately, with artificial neural networks providing more accurate results.
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