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eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Performance-Based Optimization of 2D Reinforced Concrete ...
Saba Faghirnejad · 2023-12-04 · via eess.SP updates on arXiv.org

Conducting nonlinear pushover analysis typically demands intricate and resource-intensive computational attempts, and involves a process that is highly iterative and necessary for satisfying design-defined and also requirements of codes in performance-based design. A computer-based technique is presented for reinforced concrete (RC) buildings in this study, incorporating optimization numerical approaches, techniques of optimality criteria and pushover analysis to seismic design automatically the pushover drift performance. The optimal design based on the performance of concrete beams, columns and shear walls in concrete moment frames is presented using the artificial bee colony optimization algorithm. The design is applied to three frames such as a 4-story, an 8-story and a 12-story. These structures are designed to minimize the overall weight while satisfying the levels of performance include Life Safety (L-S), Collapse Prevention (C-P), and Immediate Occupancy (I-O). To achieve this goal, three main steps are performed. In the first step, optimization codes are implemented in MATLAB software, and the OpenSees software is used for nonlinear static analysis of the structure. By solving the optimization problem, several top designs are obtained for each frame and shear wall. Pushover analysis is performed considering the constraints of relative displacement and plastic hinge rotation based on the nonlinear provisions of FEMA356 code to achieve each levels of performance. Following this, convergence, pushover, and drift history curves are plotted for each frame, and selecting the best design for each frame ultimately occurs. The results demonstrate the algorithm's performance is desirable for the structure to achieve selecting the best design and lower weight.