Research data for "Damage detection in a semi-active structural control system based on reinforcement learning"

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

Jedlińska A., Pisarski D., Mikułowski G., Błachowski B., Jankowski Ł., Damage detection in a semi-active structural control system based on reinforcement learning, ISMA 2024, International Conference on Noise and Vibration Engineering (ISMA 2024), 2024-09-09/09-11, Leuven (BE), 9 pages, 2024.

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 Python programming language. 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. Two control modes are considered (RL and optimum passive TMD) and three training/application modes, as described in the readme.txt file and in the publication.