Monitoring structural damage is essential for preserving and sustaining civil and mechanical systems' structural service lifecycle. Successful monitoring provides valuable information on structural health, integrity, and safety. Maintaining continuous performance highly depends on monitoring damage's occurrence, formation, and propagation. Damage may accumulate on a structure due to surrounding conditions or human-induced factors. Although structural health monitoring (SHM) technology is becoming more mature and is being adopted across a wide range of civil engineering applications (CEAs), the difficulty of capturing subtle damage from structural vibration response (SVR) is still challenging. The SVR is almost nonstationary and complex. In addition, there is no generic robust, intelligent algorithm for extracting sensitive features from massive collected data that can estimate and predict different structural integrity conditions. Thus, this study introduces a technique to derive informative damage-sensitive features (DSFs) and develop a pattern, recognition-based statistical model. The extracted DSFs differ from the prior one in some significant respect, accurately represent various damage features, and then are integrated with an AI network for pattern recognition. The wavelet energy as a damage feature is used to classify structural damage states. Experimental data of a six-story frame are used to validate the computational model and demonstrate its efficiency and accuracy. The proposed algorithm can determine the structural integrity state of large complex systems with a noisy measurement under arbitrary dynamic excitation.