Nowadays, several different structural damage detection techniques are being developed with the goalofmonitoringstructure stability with high accuracy and low cost. One of the well-known techniques isinverse analysis based on model updating methods.However, the main challenges in this technique is the development of algorithmsthat assist in the processing of the enormous amounts of data for the inverse process. To overcomethis, the Artificial Neural Network (ANN) has been used by many researchers to complement existing approaches.The integration of model updating methods andANNrequiresnot only a wealth of knowledge and experience in structural damage detection, but also appropriate numerical techniques, and proficiencyin scripting programming languages.In this paper, the objective is toconstructthe formulation of structural damage detection using inverse analysis incorporating Artificial Neural Network (ANN)for Kirchhoff plate theory and to establishthe source code.The output from the process is stiffness reduction ratio (SRF)while natural frequencies and mode shape as input data.Finite element method (FEM) wasusedin generating the formulation.The source code of the formulation has been written step-by-stepandkept as simple as possiblein Matrix Laboratory (Matlab) programming language.The performance of the formulationis verified against numerical work based on simulated damaged.The presented result shows that, this formulationexhibits excellent performance thus highly potentialfor damage detectionof the plate structure
