Acceleration‐based damage indicators for building structures using neural network emulators
In this paper we propose the use of artificial neural network (ANN) emulators for an acceleration‐based approach to evaluating building structures under earthquake excitation. The input layer of the ANN is a forced vibration, described as ground acceleration and the acceleration data of several floors. The approach is improved by using the acceleration at later time steps as the output of the neural network (NN). This time delay is optimized as a tunable band to provide the most sensitive signals.
Determining Displacement in Concrete Reinforcement Building with using Evolutionary Artificial Neural Networks
After the occurrence of an earthquake, the issues such as making decision promptly on building safety, making possibility to keep on utilizing from a building, locating the ruined place and the rate of the destruction is crucial. Today a new technique is the application of evolutionary artificial neural network models based on artificial intelligence, which is widely used in various scientific fields, especially in structure and earthquake engineering. In this article, a wall concrete frame with 4-stories and 4-bays, is studied in nonlinear dynamic analysis by software IDARC2D (ver.6.0) for 30 records of 0.1g to 1.5 g acceleration and the rate of total damage of the frame in each records and each accelerations are calculated, then the rate of damage are determined by using evolutionary artificial neural network models. To determine the number of effective lag times and input data of earthquake in the artificial neural network model, it is used Cross-Correlation of time series. Using genetic algorithm, the structure of the artificial neural network model were optimized in terms of cases such as number of layers and number of nodes through the hidden layer, type of transfer function and the learning algorithm of the network. All stages of training, validity and testing models were conducted by using NeuroSolutions Software. The results indicated that the number of effective lag times and input data were determined by applying Cross- Correlation method. Additionally, comparing outputs of MLP (MultiLayer perceptron) model as the best nonlinear regression model with the values of damage indicator for Park-Ang, it can be concluded that MLP model has more suitable abilities, accuracy and flexibility, than others.
Rational Design, Synthesis and Evaluation of γ-CD-Containing Cross-Linked Polyvinyl Alcohol Hydrogel as a Prednisone Delivery Platform
This study describes the in-silico rational design, synthesis and evaluation of cross-linked polyvinyl alcohol hydrogels containing γ-cyclodextrin (γ-CDHSAs) as platforms for the sustained release of prednisone (PDN). Through in-silico studies using semi-empirical quantum mechanical calculations, the effectiveness of 20 dicarboxylic acids to generate a specific cross-linked hydrogel capable of supporting different amounts of γ-cyclodextrin (γ-CD) was evaluated. According to the interaction energies calculated with the in-silico studies, the hydrogel made from PVA cross-linked with succinic acids (SA) was shown to be the best candidate for containing γ-CD. Later, molecular dynamics simulation studies were performed in order to evaluate the intermolecular interactions between PDN and three cross-linked hydrogel formulations with different proportions of γ-CD (2.44%, 4.76% and 9.1%). These three cross-linked hydrogels were synthesized and characterized. The loading and the subsequent release of PDN from the hydrogels were investigated. The in-silico and experimental results showed that the interaction between PDN and γ-CDHSA was mainly produced with the γ-CDs linked to the hydrogels. Thus, the unique structures and properties of γ-CDHSA demonstrated an interesting multiphasic profile that could be utilized as a promising drug carrier for controlled, sustained and localized release of PDN.