MS1.7 - Advances in Computational Structural Dynamics

Submarine power cables are considered lifelines in wind farm engineering, playing a key role in transporting electric current produced by wind turbines or wave converter. Cables inevitably confront combined loadings in deep-sea areas, affecting the integrity and safety during their installation and application. Helical wires have been used in flexible structures like flexible pipes, umbilicals and submarine power cables for a long history. The cross section of a helical wire could differ from round to rectangular, which affects the mechanical behaviour of the wire itself, influences the behaviour of the helical layer, and finally causes a different overall response of a flexible structure. This paper represents a model based on periodic boundary conditions in order to investigate this phenomenon. This model makes as less assumptions as possible, capturing the contact and stick-slip behaviour among each component more efficiently than a full finite element model. It also considers the mechanical behaviour of the other layers under tension and bending, including their plasticity. The results from the model are verified with a few analytical models considering different wire cross section. Finally, a series of sensitivity studies are given to investigate the influence of winding angle, pith length and wire radius through the proposed model. The research results will benefit the cable/umbilical/flexible pipe designers.

This paper deals with data-driven-based regression models for the estimation of maximum inter-storey drift demands (MIDR) in multi-story cross-laminated timber (CLT) buildings. Buildings with a different number of storeys (6, 8, 12, 16, and 20) are defined to cover medium- to high-rise CLT buildings. Different levels of panel fragmentation are considered by dividing a wall into sub-panels. The buildings were also designed based on several behaviour factors (q) to provide different ductility levels. The predictors used in the models are informed by data-driven techniques based on a vast set of nonlinear response history analyses (NRHAs) of 69 CLT buildings subjected to strong earthquakes. We explore various prediction-based methods to develop the models, including Linear regression, Polynomial regressions, and Machine learning algorithms like Decision trees, K-nearest Neighbor, and Support Vector Regressors. The result indicates that Machine learning algorithms have superior prediction power compared with traditional regression models. The ML-based predictions agree more with the numerical results of NRHAs as reflected by the highest R2 and lowest RMSE for the MIDR estimates. Finally, the result obtained from this approach is compared and discussed with those coming from previous studies.