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Title: Design and Implementation of Experiential Learning Modules for Structural Analysis
Structural analysis is the foundation of a structural engineer’s education, which generally includes topics ranging from basic statics to solving complex indeterminate structures. Most courses focus on theoretical approaches to solving and understanding problems with a large focus on determining internal forces, external reactions, and displacements. In many cases, courses also include concurrent discussion about deflected shapes and actual behavior compared to theoretical assumptions. Courses may also use pictures, videos, simulations, and small, table-top models to illustrate such behavior. However, students still struggle with understanding structural behavior and the effects of as-built versus theoretical connections. Such differences are difficult to convey simply using photos, videos, simulations, and small, table-top models. Students never have the opportunity to physically feel the differences and experience large-scale models that illustrate such behavior mainly due to cost, fabrication complexity, and material stiffness. The authors from University A and University B designed large-scale, lightweight models for in-class use that allow students to experience structural behavior and feel the differences between various types of as-built connections. This paper provides a detailed overview of the design, fabrication, and implementation of four large-scale experiential learning modules for an undergraduate structural analysis course using lightweight and flexible fiberglass reinforced polymer (FRP) more » structural shapes. The first module focuses on the behavior of beam-to-column connections compared to theoretical assumptions; the second module focuses on load paths and tributary areas related to a typical floor system; the third module focuses on the deflected shapes of determinate and indeterminate beams; and the fourth module focuses on the behavior of a portal frame subjected to both vertical and horizontal loads with various support configurations. The four modules were used throughout the structural analysis course at University A and University B to illustrate structural behavior concurrent to the presentation of various structural analysis concepts. « less
Authors:
Award ID(s):
1726621
Publication Date:
NSF-PAR ID:
10170890
Journal Name:
ASEE Annual Conference proceedings
ISSN:
1524-4644
Sponsoring Org:
National Science Foundation
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The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. 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