Damage Detection on Steel Truss Bridges Using Modal Based Methods

Abstract The structural integrity of bridges is vital for ensuring the safety of road users, particularly under heavy traffic loads from large vehicles. Traditionally, bridge performance has been assessed through visual inspection techniques. However, these methods are highly dependent on the inspector’s experience and may overlook early signs of structural deterioration. Structural Health Monitoring Systems (SHMS) have emerged as important tools in assessing the dynamic behaviour of structures, particularly in complex systems such as steel truss bridges. Numerous studies highlight the challenges and advances in damage detection using techniques like mode shape curvature and frequency response functions. This study aims to compare four vibration-based damage detection methodologies through numerical analysis and experimental testing on a laboratory-scale steel truss bridge model. By examining mode shape changes due to artificial damage scenarios, the research seeks to establish a reliable and efficient damage detection approach for structural safety assessment. We propose Mode Shape Curvature (MSC) as reliable and effective method for identifying damage locations in steel truss bridge structures.