# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 2 ANFIS: Literature Review  
The solution of problems associated with engineering systems requires the use of several different disciplines implementing different methods of modeling and analysis. For a complex engineering system, often a physics-based mathematical model is used, which is extremely difficult to formulate. For such a system, several other approaches (neural networks, fuzzy inference systems, etc.) under the rubric of \"soft computing\" provide a useful alternative. Soft computing models are becoming popular and have been of increasing interest during the last three decades. This approach is based on human reasoning and learning and uses the human tolerance for uncertainty and imprecision and fuzziness in the decision-making processes [10]. Recently, artificial neural networks (ANNs) and ANFIS have been used extensively for various civil engineering applications in construction management, building materials, hydraulics, structural engineering, geotechnical and transportation engineering, etc. Here, a selected few recent works in the area related to our subject are presented. [PERSON] et al. [11] developed an ANN to predict the compressive strength of high-performance concrete mixes. [PERSON] and [PERSON] [12] developed a fuzzy inference system (FIS) model and applied it to modeling and controlling concepts. [PERSON] and [PERSON] [13] applied ANN and FL to predict rubberized mortar properties. [PERSON] [14] used ANFIS and NN models to determine the critical buckling load. [PERSON] and [PERSON] [15] developed an ANFIS model to estimate the concrete strength of a given mix proportion based on existing datasets. [PERSON] et al. [16] used ANFIS to predict the shear modulus and damping coefficient of sand and rubber mixtures. [PERSON] et al. [17] used an adaptive neuro-fuzzy system to simulate nonlinear mapping in the sulphate expansion of Portland cement (PC) mortar. Experimental data that had previously been collected for various parameters were treated in the analysis. [PERSON] et al. [18] developed a neuro-fuzzy model to classify and to predict the behavior of steel beams under concentrated loads. [PERSON] and [PERSON] [19] applied ANFIS to assess bridge risk based on multiple bridge maintenance projects. [PERSON] and [PERSON] [20] used ANFIS to investigate the characteristics of a scour hole that develops around a group of piles in a well-defined field situation and to determine the parameters that control the scour hole. [PERSON] [21] developed an ANFIS model to predict the shear strength of concrete beams reinforced with fiber-reinforced polymer (FRP) bars. [PERSON] and [PERSON] [22] applied ANFIS to predict the moment capacities of reinforced concrete (RC) slabs exposed to fire. [PERSON] et al. [23] investigated the ability of radial basis neural networks and ANFIS methods in the prediction of ultimate strength and strain of concrete cylinders confined with FRP sheets. [PERSON] and [PERSON] [24] used ANFIS to determine the shear strength of RC beams with shear reinforcement. [PERSON] et al. [25] used an ANFIS model to predict the uniaxial compressive strength of cemented backfill.