Optimal sensor configuration for structural response prediction by a modified Nelder–Mead simplex method

2021 ◽  
Vol 28 (5) ◽  
Author(s):  
Tao Yin
Keyword(s):  
Simplex Method ◽  
Structural Response ◽  
Response Prediction ◽  
Sensor Configuration

Estimating Extreme Response of Drag Dominated Offshore Structures From Simulated Time Series of Structural Response

2007 ◽  
Author(s):  
A. Naess ◽  
O. Gaidai ◽  
S. Haver
Keyword(s):  
Response Time ◽  
Structural Response ◽  
Weather Conditions ◽  
Response Prediction ◽  
Offshore Structures ◽  
Rate Function ◽  
Extreme Response ◽  
Time Histories ◽  
Short Time

The paper presents a study of extreme response statistics of drag dominated offshore structures, showing a pronounced dynamic behaviour when subjected to harsh weather conditions. The key quantity for extreme response prediction is the mean up-crossing rate function, which can be simply extracted from simulated stationary response time histories. Present practise for obtaining adequate extremes for design purposes requires a number — say 20 or more — of 3-hour time domain analyses for several extreme sea states. For early phase considerations, it would be convenient if extremes of a reasonable accuracy could be obtained based on shorter and fewer simulations. It is therefore of interest to develop specific methods which make it possible to extract the necessary information from relatively short time histories. The method proposed in this paper opens up the possibility to predict simply and efficiently long-term extreme response statistics, which is an important issue for the design of offshore structures. A short description of this is given, but in the present paper the emphasis is on short-term analyses. The results presented are based on extensive simulation results for the Kvitebjo̸rn jacket structure, in operation on the Norwegian Continental Shelf. Specifically, deck response time histories for different sea states simulated from a MDOF model were used as the basis for our analyses.

scholarly journals Response prediction of multi-story building using backpropagation neural networks method

2019 ◽  
Vol 276 ◽  
pp. 01011
Author(s):  
Reni Suryanita ◽  
Harnedi Maizir ◽  
Yohannes Firzal ◽  
Hendra Jingga ◽  
Enno Yuniarto
Keyword(s):  
Structural Analysis ◽  
Structural Response ◽  
Building Design ◽  
Response Prediction ◽  
Soil Condition ◽  
Displacement Velocity ◽  
Floor Plan ◽  
Backpropagation Neural Network ◽  
Seismic Location ◽  
Story Building

The active ground motion in Indonesia might cause a catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to design the structural response of building against seismic hazard correctly. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be difficult and time-consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-story building with the fixed floor plan using Backpropagation Neural Network (BPNN) method. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 datasets were obtained and fed into the BPNN model for the learning process. The trained BPNN is capable of predicting the displacement, velocity, and acceleration responses with up to 96% of the expected rate.

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