Issue 50

F. Jafari et alii, Frattura ed Integrità Strutturale, 50 (2019) 209-230; DOI: 10.3221/IGF-ESIS.50.18 210 shotcrete was important as one of the important parts of sandwich panel. Besides, applied shotcrete with additives like micro-silica in the sys-tem that was comprised of small stone material improved the panel behavior. For this purpose, significant experimental and numerical researches were made for improving shotcrete behavior. Applying shotcrete in different structures for improving their performance has an old history. For the first time in 1914, shotcrete was used in an experimental mine in the USA [1]. After that, this system was used for covering stone surfaces and protecting them against weathering and in some cases as a temporary protection system. Owing to the fact that shotcrete was obtained as sheets of the bedrock, it was not considered as a fundamental protection system. After three decades, considerable progress has been made in mixing methods, additive material, machinery, and spray methods that led to the improvement of FRS performance in the economic sector and its mechanical properties and that is why has been used extensively all over the world [2] . Hence, using additive clearly improved the shotcrete performance and determining the range of Young's modulus of shotcrete with additive and studying the panel behavior in different Young's modulus is a new subject and necessary for sandwich panel. In addition, finding a way for enhancing the sandwich panel behavior and its mechanical properties of sandwich panel parts such as wire–insulting layer and concrete have been recently the subject of numerous experimental and numerical FEM studies. For example, Metelli et al in 2011 investigated the numerical behavior of concrete sandwich panels with glass fiber-composite connectors with the aim of focusing on the stresses and deformations caused by dead load, thermal actions and shrinkage [3]. In 2014, Palermo et al. investigated sandwich panels behavior in a three-story concrete frame with thin sandwich panel walls. It should be noted that the panel behavior has been studied separately in most previous cases without focusing on the performance in the building [4]. In 2016, Yokozeki et al. studied the vacuum-assisted resin transfer molding as a way for analyzing the effects of core machining configuration on the interfacial deboning toughness of foam core sandwich panels fabricated [5]. Hashemi et al. investigated the behavior using a concrete sandwich panel as an infill wall on the in-plane behavior of steel frames. The result of study showed that the interaction between the concrete sandwich panel and the steel frame in specimen IFM increased the initial stiffness, lateral strength, energy dissipation, and the equivalent viscous damping ratio of the system compared with the bare frame [6]. Heywood et al. reviewed the results of 12 single-spans. In this study,11 double-span bending tests conducted on profiled composite panels with polyisocyanurate cores sandwiched between light-gauge steel faces [7]. Applying FEM method for analyzing sandwich panel under the earthquake load has been observed in some earlier researches, for example Rezayifar et al. in 2007 studied one story sandwich panel building using ANSIS, and also laboratory test. According to this study result, ANSIS can correctly predict panel displacement and acceleration [8]. Palermo et al. studied the thin RC sandwich walls behavior under the seismic load in laboratory. This research introduces concrete damage mode, which has been validated throughout experimental tests. Force–displacement response and the damage progression were attained during experimental test was accomplished in different models [9]. Employing sandwich panel under the earthquake load and elaborating on the behavior of the panel in the steel building with Finite Element Method (FEM) and ANFIS is an important goal, which has been never achieved in the past. To this aim, ANFIS has been used as a neural network in this research. Neural network as practical method has been used to predict the earthquake load and performance of the structure in some research for analyzing laboratory and numerical research. Lee predicted damage localization for bridges using the probabilistic neural network, modal parameters, and ambient vibration data as the feature vectors for probabilistic neural networks [10]. Arslan used regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods to predict curvature and displacement ductility in reinforced concrete buildings and some parameters which influence the curvature and displacement ductility values [11]. Bagheri et al. investigated the new method for the estimation of strong ground motion with the colonial competitive algorithm; the result of the study led to predicting horizontal peak ground acceleration and spectral acceleration. The colonial competitive algorithm can be considered as a powerful and reliable tool for solving complex optimization problems [12]. Pang et al. in 2014 studied the ANN in order to estimate the fragility analysis of highway bridges. This research result indicated that ANN method could predict fragility accurate curve with optimizes time in cooperation. Furthermore, the sensitivity analyses demonstrated that the materials and geometric uncertainty play significant role in fragility curves increasing [13]. Kalman Šipoš et al. used ANN and BA methods in order to predict the multi-story frames seismic response. These methods were accomplished on laboratory data of one-story of (RC) frames with masonry in-fills. This research indicates that these two methods are considered as acceptable methods for base shear prediction, and also story drift of in-filled frame behavior prediction under seismic load [14]. Gholizadeh et al. studied the optimal design method in order to predict the structures performance in the earth quake load. The numerical results display the employing ANN efficiency to estimate the optimal weight of two structures under the earthquake [15]. Badarloo et al studied the effect of position and number of openings on the performance of composite steel shear walls,