Combining Dynamic Relaxation Method with Artificial Neural Networks to Enhance Simulation of Tensegrity Structures
نویسندگان
چکیده
منابع مشابه
DYNAMIC RELAXATION OF TENSEGRITY STRUCTURES 553 DYNAMIC RELAXATION OF TENSEGRITY STRUCTURES A Computational Approach to Form Finding of Double-layer Tensegrity Grids
The structural hierarchy inherent to tensegrities enables a building skin that performs on multiple levels simultaneously. While having one function in the global building mechanics, its individual components can work as self-contained systems balancing tensile and compressive forces locally within them. The behavior of elements under load is linear and thus describable analytically. When these...
متن کاملDynamic Artificial Neural Networks with Affective Systems
Artificial neural networks (ANNs) are processors that are trained to perform particular tasks. We couple a computational ANN with a simulated affective system in order to explore the interaction between the two. In particular, we design a simple affective system that adjusts the threshold values in the neurons of our ANN. The aim of this paper is to demonstrate that this simple affective system...
متن کاملESTIMATION OF INVERSE DYNAMIC BEHAVIOR OF MR DAMPERS USING ARTIFICIAL AND FUZZY-BASED NEURAL NETWORKS
In this paper the performance of Artificial Neural Networks (ANNs) and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) in simulating the inverse dynamic behavior of Magneto- Rheological (MR) dampers is investigated. MR dampers are one of the most applicable methods in semi active control of seismic response of structures. Various mathematical models are introduced to simulate the dynamic behavi...
متن کاملESTIMATING THE VULNERABILITY OF THE CONCRETE MOMENT RESISTING FRAME STRUCTURES USING ARTIFICIAL NEURAL NETWORKS
Heavy economic losses and human casualties caused by destructive earthquakes around the world clearly show the need for a systematic approach for large scale damage detection of various types of existing structures. That could provide the proper means for the decision makers for any rehabilitation plans. The aim of this study is to present an innovative method for investigating the seismic vuln...
متن کاملCombining Unsupervised and Supervised Artificial Neural Networks to PredictAquatic Toxicity
Most quantitative structure-activity relationship (QSAR) models are linear relationships and significant for only a limited domain of compounds. Here we propose a data-driven approach with a flexible combination of unsupervised and supervised neural networks able to predict the toxicity of a large set of different chemicals while still respecting the QSAR postulates. Since QSAR is applicable on...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Structural Engineering
سال: 2003
ISSN: 0733-9445,1943-541X
DOI: 10.1061/(asce)0733-9445(2003)129:5(672)