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Research data for: "Damage-aware structural control based on reinforcement learning"

Version 1.0

Jedlińska, Aleksandra; Pisarski, Dominik; Mikułowski, Grzegorz; Błachowski, Bartłomiej; Jankowski, Łukasz, 2024, "Research data for: "Damage-aware structural control based on reinforcement learning"", https://doi.org/10.18150/N6AQWV, RepOD, V1

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Description

This dataset contains the research data used for preparation of the publication:

Jedlińska A., Pisarski D., Mikułowski G., Błachowski B., Jankowski Ł., Damage-aware structural control based on reinforcement learning, EWSHM 2024, 11th Euopean Workshop on Structural Health Monitoring, 2024-06-10/06-13, Potsdam (DE), 8 pages, 2024. e-Journal of Nondestructive Testing (eJNDT), issue 2024-07. https://doi.org/10.58286/29606

This research has been supported by the National Science Centre, Poland, under grant agreement 2020/39/B/ST8/02615. The title of the project is "Reinforcement learning for semi-active structural control and decentralized mitigation of vibrations: development of new algorithms and assessment of of their efficiency". The aim of the project is the development and application of the machine learning techniques of reinforcement learning (RL) in tasks of semi-active structural control. The ultimate goal is the design of a framework that learns quasi-optimal control by itself in a repeated trial-and-error interactions with simulated structures.

The data files are in the txt/CSV format, and they have been exported using the Wolfram Mathematica environment. They define the investigated structure (an 11-DOF shear-type building with a semi-active TMD) and contain the control results defined as the mean total energy of the structure subjected to a seismic-type excitation. An additional problem in this manuscript was the damage-awarness, that is, the design of a specific RL control agent that estimates the current structural damage and uses this knowledge to improve its contorl decisions. One of the files contains thus the indices used to asses the effetiveness of damage identification.

Subject
Computer and Information Science; Engineering
Keyword

semi-active control, tuned mass damper (TMD), reinforcement learning, damage identification

Related Publication

Jedlińska A., Pisarski D., Mikułowski G., Błachowski B., Jankowski Ł., Damage-aware structural control based on reinforcement learning, EWSHM 2024, 11th Euopean Workshop on Structural Health Monitoring, 2024-06-10/06-13, Potsdam (DE), 8 pages, 2024. e-Journal of Nondestructive Testing (eJNDT), issue 2024-07. https://doi.org/10.58286/29606 https://doi.org/10.58286/29606 doi: 10.58286/29606

CC0 Creative Commons Zero 1.0 CC0 Creative Commons Zero 1.0

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readme.txt
Plain Text - 4.1 KB - Sep 18, 2024 - 0 Downloads
MD5: 2200bf8790cb63acaa7c941654e54c03
License: CC0 Creative Commons Zero 1.0
Readme file.
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matrix_M_mass.tab
Tabular Data - Original format: Comma Separated Values - 491 B - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
12 Variables, 12 Observations - UNF:6:BydFUgD4EtqiK4jeO4AWHA==
Structural mass matrix M.
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matrix_K_stiffness.tab
Tabular Data - Original format: Comma Separated Values - 590 B - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
12 Variables, 12 Observations - UNF:6:iL/84niaPm9fkbORxgjprQ==
Structural stiffness matrix K.
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matrix_C_low-TMD-damping.tab
Tabular Data - Original format: Comma Separated Values - 1.0 KB - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
12 Variables, 12 Observations - UNF:6:WG+YEEBg4pPgiRx+Mns50g==
Structural damping matrix C used by the control agent: low TMD damping.
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matrix_C_high-TMD-damping.tab
Tabular Data - Original format: Comma Separated Values - 1.0 KB - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
12 Variables, 12 Observations - UNF:6:bONs0Ueq/GAxuXRF4gPFyA==
Structural damping matrix C used by the control agent: high TMD damping.
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matrix_C_damping_optimal-TMD-passive.tab
Tabular Data - Original format: Comma Separated Values - 1.0 KB - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
12 Variables, 12 Observations - UNF:6:qRl7AHKrqtKYSX6JGuQLHQ==
Structural damping matrix C with the TMD damping level optimally tuned for the classical passive TMD.
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vector_r_load_allocation.tab
Tabular Data - Original format: Comma Separated Values - 33 B - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
1 Variables, 11 Observations - UNF:6:fupUTrCUENA/XBdyr9vHTg==
Load alloction vector r.
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mean_total_energy_RL-TMD_1.tab
Tabular Data - Original format: Comma Separated Values - 306.0 KB - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
1 Variables, 7999 Observations - UNF:6:zjZBwII6F821ybLSG4uUgQ==
Mean total energy of the structure (RL control: left column; passive TMD: right column). RL agent trained on undamaged structure and applied to undamaged structure. Used to compute Table 1 and columns 1-2 of Table 2 in the paper.
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mean_total_energy_RL-TMD_2.tab
Tabular Data - Original format: Comma Separated Values - 305.9 KB - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
1 Variables, 7999 Observations - UNF:6:KgrdV/JsPrz9KsVEMoC9iw==
Mean total energy of the structure (RL control: left column; passive TMD: right column). RL agent trained on undamaged structure and applied to damaged structure. Used to compute columns 3-4 of Table 2 in the paper.
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mean_total_energy_RL-TMD_3.tab
Tabular Data - Original format: Comma Separated Values - 153.1 KB - Sep 18, 2024 - 0 Downloads
License: CC0 Creative Commons Zero 1.0
1 Variables, 3999 Observations - UNF:6:9l/NZQ7PjfZqGrtsudi7zg==
Mean total energy of the structure (RL control: left column; passive TMD: right column). RL agent trained on damaged structure with damage features and applied to damaged structure. Used to compute Table 3 in the paper.
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