This study validates an adaptive control algorithm capable of compensating for online sensor failure. Online failure is a relevant problem when considering actively damped, multi-story smart buildings experiencing a disturbance event. In recent years, Artificial Neural Networks (ANNs) have proven very efficient in pattern classification and control applications. In this study, the unique application of ANNs involving Radial Basis Functions (RBFs) combined with H∞ optimal control has demonstrated three significant characteristic advantages: (1) real time adaptability, (2) optimal convergence and computation time, and (3) most importantly, no offline training. The novelty of the proposed controller is elucidated by performing disturbance rejection tests involving a scaled two degree of freedom shear frame subjected to a combined H∞ and ANN control. A bench scale structural model is instrumented with piezoelectric sensors and actuators. After the onset of a first mode disturbance, the structural frame is subjected to a complete sensor failure. The proposed controller is shown to enhance the performance of a baseline H∞ controller in the presence of sensor failure.