• Structural Engineering and Mechanics
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• v.37 no.6
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• pp.575-592
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• 2011

For seismic resistant design of critical structures, a dynamic analysis, either response spectrum or time history is frequently required. Owing to the lack of recorded data and the randomness of earthquake ground motion that may be experienced by structure in the future, usually it is difficult to obtain recorded data which fit the requirements (site type, epicenteral distance, etc.) well. Therefore, the artificial seismic records are widely used in seismic designs, verification of seismic capacity and seismic assessment of structures. The purpose of this paper is to develop a numerical method using Artificial Neural Network (ANN) and wavelet packet transform in best basis method which is presented for the decomposition of artificial earthquake records consistent with any arbitrarily specified target response spectra requirements. The ground motion has been modeled as a non-stationary process using wavelet packet. This study shows that the procedure using ANN-based models and wavelet packets in best-basis method are applicable to generate artificial earthquakes compatible with any response spectra. Several numerical examples are given to verify the developed model.

• Structural Engineering and Mechanics
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• v.87 no.1
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• pp.19-28
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• 2023

This paper describes the experimental investigation of steel pallet rack beam-to-column connec-tions. Total behavior of moment-rotation (M-φ) curve and the effect of particular characteristics on the behavior of connection were studied and the associated load strain relationship and corre-sponding failure modes are presented. In this respect, an estimation of SPRBCCs moment and rotation are highly recommended in early stages of design and construction. In this study, a new approach based on Support Vector Machines (SVMs) coupled with discrete wavelet transform (DWT) is designed and adapted to estimate SPRBCCs moment and rotation according to four input parameters (column thickness, depth of connector and load, beam depth,). Results of SVM-WAVELET model was compared with genetic programming (GP) and artificial neural networks (ANNs) models. Following the results, SVM-WAVELET algorithm is helpful in order to enhance the accuracy compared to GP and ANN. It was conclusively observed that application of SVM-WAVELET is especially promising as an alternative approach to estimate the SPRBCCs moment and rotation.

• Bulletin of the Korean Chemical Society
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• v.31 no.4
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• pp.808-814
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• 2010

Activities of nanostructure HZSM-5 and Co-ZSM-5 catalysts (with different Co-loading) for catalytic conversion of ethyl acetate and toluene were studied. The catalysts were prepared by wet impregnation method and were characterized by XRD, BET, SEM, TEM and ICP-AES techniques. Catalytic studies were carried out inside a U-shaped fixed bed reactor under atmospheric pressure and different temperatures. Toluene showed lower reactivity than ethyl acetate for conversion on Co-ZSM-5 catalysts. The effect of Co loading on conversion was prominent at temperatures below $400^{\circ}C$ and $450^{\circ}C$ for ethyl acetate and toluene respectively. In a binary mixture of organic compounds, toluene and ethyl acetate showed an inhibition and promotional behaviors respectively, in which the conversion of toluene was decreased at temperatures above $350^{\circ}C$. Inhibition effect of water vapor was negligible at temperatures above $400^{\circ}C$. An artificial neural networks model was developed to predict the conversion efficiency of ethyl acetate on Co-ZSM-5 catalysts based on experimental data. Predicted results showed a good agreement with experimental results. ANN modeling predicted the order of studied variable effects on ethyl acetate conversion, which was as follows: reaction temperature (50%) > ethyl acetate inlet concentration (25.085%) > content of Co loading (24.915%).

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Structural Engineering and Mechanics
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• v.89 no.6
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• pp.579-588
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• 2024

The present paper investigates the curvature ductility of confined reinforced concrete (RC) beams with normal (NSC) and high strength concrete (HSC). For the purpose of predicting the curvature ductility factor, an analytical model was developed based on the equilibrium of internal forces of confined concrete and reinforcement. In this context, the curvatures were calculated at first yielding of tension reinforcement and at ultimate when the confined concrete strain reaches the ultimate value. To best simulate the situation of confined RC beams in flexure, a modified version of an ancient confined concrete model was adopted for this study. In order to show the accuracy of the proposed model, an experimental database was collected from the literature. The statistical comparison between experimental and predicted results showed that the proposed model has a good performance. Then, the data generated from the validated theoretical model were used to train the artificial neural network (ANN) prediction model. The R² values for theoretical and experimental results are equal to 0.98 and 0.95, respectively which proves the high performance of the ANN model. Finally, a parametric study was implemented to analyze the effect of different parameters on the curvature ductility factor using theoretical and ANN models. The results are similar to those extracted from experiments, where the concrete strength, the compression reinforcement ratio, the yield strength, and the volumetric ratio of transverse reinforcement have a positive effect. In contrast, the ratio and the yield strength of tension reinforcement have a negative effect.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.05a
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• pp.51.2-51.2
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• 2011

A novel electrospun nanofiber membrane was fabricated using combined poly (vinylphosphonic acid) (PVPA) and polyvinyl alcohol (PVA) intended for bone tissue engineering applications. PVPA is a proton-conducting polymer used as primer for bone implants and dental cements to prevent corrosion and brush abrasion. The phosphonate groups of PVPA have the ability to crosslink and attach itself to the hydroxyapatite surface facilitating faster integration of the biomaterial to the bone matrix. PVA was combined with PVPA to provide hydrophilicity, biocompatibility and improve its spinnability. To improve its mechanical strength, PVPA/PVA and neat PVA mixtures were combined to produce a multilayer scaffold. The physical and chemical properties of the of the fabricated matrix was investigated by SEM and TEM morphological analyses, tensile strength test, XRD, FT-IR spectra, swelling behavior and biodegradation rates, porosity and contact angle measurements. Biocompatibility was also examined in vitro by cytotoxicity and cell proliferation studies with MTT assay and cell adhesion behavior by SEM and confocal microscopy.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.05a
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• pp.52.1-52.1
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• 2011

In this work, the effect of the addition of bioactive glass in the biocompatibility and mechanical behavior of conventional TTCP/DCPA based bone cement were investigated. The cement was initially modified with chitosan and HPMC which cross-linked with citric acid to improved mechanical properties.The injectable bone substitutes were further modified by adding varying amounts of bioactive glass (0%, 10%, 20% and 30%) and its effects on the biocompatibility of the material were studied. Afterbio-glass powders were mixed with the optimized composition for HPMC and citric acid content,the IBS was incubated at $37^{\circ}C$ at different time intervals and showed progressive formation of HAp with increasing time. Mechanical properties like Vickers hardness and compressive strength were found to increase with the increasing amount of bioactive glass addition and that setting time was shortened. The fabricated IBS morphologies were further characterized using SEM. MTT assay was performed to check the cell cytotoxicity and cell proliferation for 1, 3 and 5 days. Cell morphology, adhesion and proliferation behavior of cell in the IBS by culturing MG-63 cells on the IBS for 20, 60 and 90 mins and 1, 3 and 5 days was also investigated. All the results showed increasing biocompatibility as the bioglass content increased. MTT results found the materials to be cytocompatible and SEM images showed that cells attached and proliferated successfully.

• Structural Engineering and Mechanics
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• v.18 no.3
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• pp.303-314
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• 2004

An artificial neural network (ANN) application is presented for flexural and axial vibration analysis of elastic beams with various support conditions. The first three natural frequencies of beams are obtained using multi layer neural network based back-propagation error learning algorithm. The natural frequencies of beams are calculated for six different boundary conditions via direct solution of governing differential equations of beams and Rayleigh's approximate method. The training of the network has been made using these data only flexural vibration case. The trained neural network, however, had been tested for cantilever beam (C-F), and both end free (F-F) in case the axial vibration, and clamped-clamped (C-C), and Guided-Pinned (G-P) support condition in case the flexural vibrations which were not included in the training set. The results found by using artificial neural network are sufficiently close to the theoretical results. It has been demonstrated that the artificial neural network approach applied in this study is highly successful for the purposes of free vibration analysis of elastic beams.

• Structural Engineering and Mechanics
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• v.16 no.1
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• pp.83-99
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• 2003

The aim of this paper is to develop the ISCOSTFUN (Intelligent System for Prediction of Concrete Strength by Functional Networks) in order to provide in-place strength information of the concrete to facilitate concrete from removal and scheduling for construction. For this purpose, the system is developed using Functional Network (FN) by learning functions instead of weights as in Artificial Neural Networks (ANN). In serial functional network, the functions are trained from enough input-output data and the input for one functional network is the output of the other functional network. Using ISCOSTFUN it is possible to predict early strength as well as 7-day and 28-day strength of concrete. Altogether seven functional networks are used for prediction of strength development. This study shows that ISCOSTFUN using functional network is very efficient for predicting the compressive strength development of concrete and it takes less computer time as compared to well known Back Propagation Neural Network (BPN).

• Journal of Biomedical Engineering Research
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• v.18 no.1
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• pp.1-8
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• 1997

This study was performed to evaluate the effect of resin and filler type on the compressive strength of light-activated composite resins. Experimental composite resins containing either amorphous spherical silica or crushed quartz in two matrix resins of BisGMA/TEGDMA and UTMA/TEGDMA were prepared and the specimens of 3 m in diameter and 6m in length were made. Compressive test was subjected to a crosshead speed of 0.5 mm/min, and the fracture surFaces were examined by SEM. The compressive strength of UTMA-based composite resin was higher than that of BisGMA-based composite resin. The loading rate of spherical silica was higher than that of crushed silica when the size dis- tribution of fillers was same. Strength decrease of Bis-GMA-based composite resin was severer than that of UTMA-based composite resin in a $37^{\circ}$c water environment. Fracture surface showed that the composite resin failure developed along the matrix resin and the filler/resin interface region.