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1. U.S.-Japan Collaboration for Shake Table Testing of a Frame-Spine System with Force-Limiting Connections

   Fahnestock, L. ( January 2021 , 17th World Conference on Earthquake Engineering, 17WCEE)

   Conventional lateral force-resisting systems can provide a stable, ductile response but also experience significant inelastic demands, rendering repairs impractical or uneconomical. Thus, there is a need for novel structural systems that protect structural and nonstructural components to reduce post-earthquake repairs and downtime. A U.S.-Japan research team – including three U.S. universities, two Japanese universities, and two major experimental research labs – is developing a structural solution to reduce peak drift and acceleration demands, thereby protecting buildings, their contents, and occupants during major earthquakes. The primary components of the system are: (1) steel base moment-resisting frames designed and detailed to behave in the inelastic range and dissipate energy, (2) stiff and strong elastic spines designed to remain essentially elastic to redistribute seismic demands more uniformly over the building height, and (3) force-limiting connections (FLC) that connect the frame to the spines to provide a yielding mechanism that limits acceleration demands. This economical earthquake-resilient system is intended to be used in essential facilities, such as hospitals, where damage to the buildings and contents and occupant injuries must be prevented and where continuity of operation is imperative. The system was recently tested at full scale at the E-Defense shake-table facility in Miki, Japan. This paper provides an overview of pre-test numerical simulations, shake-table test setup and instrumentation, and preliminary test results. 

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2. Modeling uncertainty of specimens employing spines and force‐limiting connections tested at E‐defense shake table

   https://doi.org/10.1002/eqe.3976  

   Astudillo, Bryam ; Rivera, David ; Duke, Jessica ; Simpson, Barbara ; Fahnestock, Larry A. ; Sause, Richard ; Ricles, James ; Kurata, Masahiro ; Okazaki, Taichiro ; Kawamata, Yohsuke ; et al ( July 2023 , Earthquake Engineering & Structural Dynamics)

   In light of the significant damage observed after earthquakes in Japan and New Zealand, enhanced performing seismic force‐resisting systems and energy dissipation devices are increasingly being utilized in buildings. Numerical models are needed to estimate the seismic response of these systems for seismic design or assessment. While there have been studies on modeling uncertainty, selecting the model features most important to response can remain ambiguous, especially if the structure employs less well‐established lateral force‐resisting systems and components. Herein, a global sensitivity analysis was used to address modeling uncertainty in specimens with elastic spines and force‐limiting connections (FLCs) physically tested at full‐scale at the E‐Defense shake table in Japan. Modeling uncertainty was addressed for both model class and model parameter uncertainty by varying primary models to develop several secondary models according to pre‐established uncertainty groups. Numerical estimates of peak story drift ratio and floor acceleration were compared to the results from the experimental testing program using confidence intervals and root‐mean‐square error. Metrics such as the coefficient of variation, variance, linear Pearson correlation coefficient, and Sobol index were used to gain intuition about each model feature's contribution to the dispersion in estimates of the engineering demands. Peak floor acceleration was found to be more sensitive to modeling uncertainty compared to story drift ratio. Assumptions for the spine‐to‐frame connection significantly impacted estimates of peak floor accelerations, which could influence future design methods for spines and FLC in enhanced lateral‐force resisting systems.

    

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3. PRELIMINARY NUMERICAL ANALYSIS OF A STRONGBACK COLUMN AS A RETROFIT OF A MOMENT-RESISTING FRAME

   D. Rivera Torres and B. Simpson ( January 2020 , 17th World Conference on Earthquake Engineering, 17WCEE)

   Steel moment-resisting frames (MRFs) are widely used in the United States to resist seismic forces. MRFs have many advantages, including high ductility, architectural versatility, and vetted member and connection detailing requirements. However, MRFs require large members to meet story drift criteria. Moreover, strong-column-weak-beam requirements can result in significant member sizes, and – even in the cases where strong-column-weak-beam requirements are satisfied – MRFs can still be vulnerable to story mechanisms in one or a few stories. Recently, the concept of a strongback has been utilized successfully to delay or prevent story mechanism behavior in braced frames. The strongback is represented by a steel truss or column that is designed to remain essentially elastic, thus allowing the system to transfer inelastic demands across stories. Although systems including strongbacks exhibit more uniform story drift demands with building height and reduced peak drift response, the elastic nature of the strongback can also result in near-elastic higher-mode force demands. This study compares the dynamic response of a baseline MRF to that of a retrofit using a strongback column. Several ground motions are considered to determine which cases produce the largest drift, acceleration, and force demands. 

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4. Proportioning energy-dissipating members in strongback braced frames

   Talley, Peter C. ; Denavit ; Mark D. ; Wierschem, Nicholas E. ( January 2023 , Proceedings of the Annual Stability Conference Structural Stability Research Council)

   Many structural systems are susceptible to soft-story instabilities during earthquakes that are lifethreatening and can lead to damage that is too costly to repair. One way to mitigate damage and reduce the potential for soft-story instability is through the addition of an elastic spine that distributes drifts across the height of a structure. One such system is the strongback braced frame, which replaces one side of a buckling-restrained braced frame with a strongback truss. With the strongback providing vertical continuity, an expanded design space is made available for the arrangement of buckling-restrained braces (BRBs) or other energy-dissipating members. An example of this expanded design space is that a designer could opt to not include BRBs at every story. Methods for proportioning the energy-dissipating resistance in strongback braced frames have been proposed. However, most methods don't allow exploitation of the full design space. The objective of this work is to propose and evaluate a potential method of proportioning energy-dissipating members for arbitrary vertical arrangements within strongback braced frames. For a prototypical building, the BRBs are designed in various configurations using existing methods and with the new method. Nonlinear time history analyses of the resulting designs coupled with a rigid strongback are performed and the results are compared. The impacts of overstrength and P-Δ effects are quantified. The findings support the proposed method of BRB design that enables exploration of the wide design space made available by the strongback. 

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5. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEG channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. 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. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 

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