A Review on Application of Machine Learning Techniques in Seismic Analysis of Timber Structures
Abstract
In the last two decades, great progress in Machine Learning can be seen in various fields of structural engineering including seismic analysis. This paper focuses on the cross-filed of Machine Learning (ML) and seismic engineering and provides an overview on different ML techniques been used in seismic analysis studies, compare these techniques and their application to study the seismic response of timber structures. The comparison of common supervised ML techniques in this paper are Multi Linear Regression, Regression Tree, Regression Forest, K Nearest Neighbor, Support Vector Regression and Artificial Neural Networks. The recent increase in studies of ML is largely focused on Reinforced Concrete (RC) structures but its application for the behavior of timber structures is still to be explored. Timber structures are considered as the best performing material under strong ground motions. However, the problems associated with timber structures are lack of experimental data, standard numerical models and design codes. It has been observed that application of ML in this domain is new but considered as an increasingly dynamic area of high impact result where new horizons of research topics are waiting to be investigated. The review of different studies demonstrated the potential of improving the prediction of seismic performance and structural behavior by the use of ML. These methods allow more efficient and accurate modelling of complex problems than traditional methods.
Reference
Syed Ashraf Abbas. (2025). A Review on Application of Machine Learning Techniques in Seismic Analysis of Timber Structures. Construction Technologies and Architecture. https://doi.org/10.4028/p-5xmu1e
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