NEURAL NETWORKING OF INFILLED RC LOW-RISE SERVICE BUILDINGS

Artificial neural networks (ANNs) are one of the most research areas that attracts the attention of experts of various scientific areas. Recent research activities regarding ANNs indicated that this method is a powerful tool to solve complicated problems in engineering fields.In this paper, ANNs were utilized to predict the lateral behavior of school buildings in Egypt. For this, reinforced concrete (RC) frames representing common school buildings with different characteristics were analyzed using nonlinear dynamic pushover analysis to obtain their capacity curves, failure loads and displacements. Parameters included number of stories, location and dimensions of the frames, distribution of masonry infill panels, and properties of concrete and reinforcement. Obtained data were used to train several ANN models with different topologies and learning algorithms. The most representative ANN was used to obtain more insight into the behavior of school building frames with different parameters

FE Modeling of CFRP-Retrofitted RC Frames with Masonry Infill Walls

A number of numerical and experimental studies have been reported in recent literature to investigate the effects of infill walls on the seismic response of RC infilled frames. Many experimental studies used CFRP sheets as an external bracing system for retrofitting the infilled RC frames. It has been found that the common mode of failure of such retrofitted frames is the debonding of the CFRP-concrete adhesive material. In the current study, the behaviour of CFRP retrofitted infilled RC frames was investigated with a finite element micro model. In that model, a four-node shell element was used for modeling the concrete, infill panel and CFRP sheets. The interaction between concrete frame and infill panel was modelled using contact surfaces to allow the occurrence of separation and prevent penetration. Nonlinearities of the concrete, infill panel, steel and CFRP sheets were considered. To allow the occurrence of debonding mode of failure, the adhesive layer was modelled using cohesive surface-to-surface interaction model, which assumes that the failure of cohesive bond is characterized by progressive degradation of the cohesive stiffness, which is driven by a damage process based on the fracture energy. The proposed model was verified using experimental results from the literature. Results indicated that the cohesive model could capture the debonding mode of failure which has been observed experimentally. The validated micro model was used to investigate the effects of the strip end area, the anchor location and partial bonding of the CFRP sheet to the infill panel surface on the behaviour of infilled frames. The results of parametric study showed that, to get the highest efficiency of the CFRP retrofitted infilled frame, bonding about 25% only of the diagonal length from each end is sufficient to get the same behaviour of the totally bonded sheet