Suby Bhattacharya

Dynamic instability of pile-supported structures in liquefiable soils during earthquakes

Piles are long slender columns installed deep into the ground to support heavy structures such as oil platforms, bridges, and tall buildings where the ground is not strong enough to support the structure on its own. In seismic prone zones, in the areas of soft soils (loose to medium dense soil which liquefies like a quick sand) piles are routinely used to support structures (buildings/ bridges). The pile and the building vibrate, and often collapse, in liquefiable soils during major earthquakes. In this paper an experimental and analytical approach is taken to characterize this vibration. The emphasis has been given to the dynamic instability of piled foundations in liquefied soil. The first natural frequency of a piled-structure vibrating in liquefiable soil is obtained from centrifuge tests. The experimental system is modelled using a fixed-free Euler-Bernoulli beam resting against an elastic support with axial load and tip mass with rotary inertia. Natural frequencies obtained from the analytical method are compared with experimental results. It was observed that the effective natural frequency of the system can reduce significantly during an earthquake.

Obtaining Spectrum Matching Time Series Using a Reweighted Volterra Series Algorithm (RVSA)

In this paper, we introduce a novel algorithm for morphing any accelerogram into a spectrum matching one. First, the seed time series is re‐expressed as a discrete Volterra series. The first‐order Volterra kernel is estimated by a multilevel wavelet decomposition using the stationary wavelet transform. Second, the higher‐order Volterra kernels are estimated using a complete multinomial mixing of the first‐order kernel functions. Finally, the weighting of every term in this Volterra series is optimally adapted using a Levenberg–Marquardt algorithm such that the modified time series matches any target response spectrum. Comparisons are made using the SeismoMatch algorithm, and this reweighted Volterra series algorithm is demonstrated to be considerably more robust, matching the target spectrum more faithfully. This is achieved while qualitatively maintaining the original signal’s nonstationary statistics, such as general envelope, time location of large pulses, and variation of frequency content with time.