Adaptive neuro fuzzy prediction of deflection and cracking behavior of NSM strengthened RC beams

• Near Surface Mounted (NSM) strengthening for underperforming structures.
• A soft computing technique is employed for the serviceability prediction.
• Reinforced Concrete (RC) beams strengthened with the NSM technique.

The Near Surface Mounted (NSM) technique is a promising strengthening approach, though unpopular due to reliable serviceability prediction options. In this paper, a soft computing technique is employed to predict the deflection and cracking behavior of NSM strengthened RC beams. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to simulate the serviceability behavior of one control and six NSM strengthened RC beams using variable bond lengths (1600, 1800 or 1900 mm) with different NSM strengthening materials (steel and CFRP bars). The proposed ANFIS model employed variable load and bond length as inputs while the output parameters were deflection and crack width of the steel and CFRP bars. The ANFIS results were compared with the experimental results using the root-mean-square error (RMSE), coefficient of determination and Pearson coefficient. The results found that the ANFIS approach showed an improvement in predictive accuracy and generalization capability in comparison to the fuzzy approach. The highest level of accuracy with ANFIS was achieved when predicting CFRP bar crack width, where RMSE = 8.05E−07. In contrast, the lowest level of accuracy was achieved when predicting CFRP bar deflection, with RMSE = 0.045185.