LAUSR

Accumulation of damages during the service life of a structure can reduce its safety. Every structure that is constructed has a particular age, but these structures can deteriorate before their service life due to various factors such as harsh environmental conditions, fatigue due to service loading, etc. To access the information regarding the health index of structure, the need for various unconventional damage assessment practices and dependable structural health monitoring systems is presently high. Structures to perform damage assessment efficiently and appropriate retrofitting are required. Structural health monitoring (SHM) has been verified to be an economical technique for damage assessment in structures over the past several decades. In reinforced concrete beams, flexural cracks distribute non-linearly and propagate along in all directions. The crack continues to propagate until the structure or structural component fractures. Due to this complex behavior of cracks, simplified damage simulation techniques such as reductions in the modulus of elasticity or section depth or stiffness of rotational spring elements cannot be applied to simulate flexural cracks in reinforced concrete components. Besides these simplified techniques, dynamic properties have been used extensively in the past. Dynamic properties such as frequency, mode shapes vary a lot with environmental changes, so they are not very reliable. This research will address the above gap in knowledge by developing a model that can represent the complex behavior of cracks and then utilize artificial neural networks to assess damage in RC flexural members.