This document proposes a multi-sensor data fusion system to enhance the analysis of concrete deterioration due to alkali-aggregate reaction (AAR). The system uses different sensor types like acoustic, electro-mechanical, optical, and embedded sensors to collect internal and external damage data. Feature extraction and a decentralized Kalman filter are used to fuse the heterogeneous sensor data. An artificial neural network then characterizes and quantifies the damage levels. The study expects to improve accuracy over single sensor systems and establish correlations between surface damage, internal damage, and gel concentration levels causing structural deterioration.