Buffet-Induced Structural Response Prediction of Aircraft Horizontal Stabilizers Based on Immersogeometric Analysis and an Isogeometric Blended Shell Approach

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.

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Surrogate modeling for structural response prediction of a building class

Structural Safety ◽

10.1016/j.strusafe.2020.102041 ◽

2021 ◽

Vol 89 ◽

pp. 102041

Author(s):

Vamshi Krishna Gudipati ◽

Eun Jeong Cha

Keyword(s):

Structural Response ◽

Surrogate Modeling ◽

Response Prediction

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Uncertainty in Structural Response Prediction of Composite Structures Subjected to Blast Loading: Modeling, Quantification, and Reduction

Blast Mitigation Strategies in Marine Composite and Sandwich Structures - Springer Transactions in Civil and Environmental Engineering ◽

10.1007/978-981-10-7170-6_7 ◽

2017 ◽

pp. 131-156

Author(s):

Zhen Hu ◽

Sankaran Mahadevan

Keyword(s):

Composite Structures ◽

Structural Response ◽

Blast Loading ◽

Response Prediction

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

MATEC Web of Conferences ◽

10.1051/matecconf/201927601011 ◽

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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