Comparative Evaluation of Semiactive Control Strategies for the Milad Telecommunication Tower Using MR Dampers Under Spectrally Matched Near‐ and Far‐Field Earthquakes
Bibliographic record
Abstract
ABSTRACT Tall telecommunication towers are highly vulnerable to seismic and wind‐induced vibrations due to their slender geometry, low damping, and long fundamental periods. Existing control systems often struggle to balance adaptability, energy efficiency, and real‐time feasibility. This study introduces a fully data‐driven semiactive control framework for the Milad Tower, integrating magnetorheological (MR) dampers modeled via artificial neural networks (ANNs) with four control strategies: linear quadratic regulator (LQR), fuzzy logic controller (FLC), model predictive controller (MPC), and adaptive neuro‐fuzzy inference system (ANFIS). The tower was subjected to 21 spectrally matched ground motions, categorized into near‐field with pulse, near‐field without pulse, and far‐field earthquakes, tailored to its long‐period dynamics using wavelet‐based spectral matching. Among all strategies, MPC achieved the greatest peak and RMS response reductions, lowering RMS displacement and acceleration by 73.3% and 40.3%, respectively, but required the highest control effort (60.9%). ANFIS matched or slightly exceeded MPC's performance while consuming less energy (54.6%) and demonstrated superior adaptability, especially under high‐pulse near‐field events by preventing control saturation. FLC consistently outperformed LQR but lagged behind ANFIS in adaptability. These results underscore the effectiveness of combining ANN‐based damper modeling with intelligent, data‐driven controllers, offering a high‐performance and energy‐efficient solution for real‐time seismic mitigation in tall telecommunication structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".