A hybrid Modified Artificial Bee Colony and extended Kalman filter algorithm for structural system identification

The continuous monitoring of structural condition is extremely important for sustaining and preserving the service life of civil structures. It provides resolute and staunch information on the health, serviceability, integrity and safety of structures. System Identification (SI) is a process of extracting a “vibration signature” and comparing the present signature to that of the reference (i.e., undamaged) state. Any notable difference in the vibration signature of a system can be attributed to a certain type of degradation. However, developing efficient SI techniques for civil structures is a critical challenge that needs to be addressed. Hence, an effort is made to formulate SI as a non-convex optimization problem. The Modified Artificial Bee Colony (MABC) is assimilated with extended Kalman filter (EKF) for damage identification using modal data. An objective function obtained by the fractional changes of damaged and undamaged structure is controlled by the metaheuristic optimization algorithm embedded in the proposed damage-detection framework. The residual error that arises in modeling is minimized, creating a more accurate prediction for the damage severity and location. The effectiveness of the proposed SI method is demonstrated via numerical simulation and real-time experimental study. The results of the study identify the damage (both location and severity in a single step of optimization) with lesser computational time and faster convergence when compared with Artificial Bee colony (ABC) and Particle Swarm Optimization (PSO) algorithm. This article emphasizes the importance of continuous structural condition monitoring of civil engineering structures. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.