Pipeline infrastructure and its safety are critical for the recovering of U.S. economy and our standard of living. Accurate pipe material strength estimation is critical for the integrity and risk assessment of aging pipeline infrastructure systems. Existing techniques focus on the single modality deterministic estimation of pipe strength and ignores inhomogeneousity and uncertainties. In view of this, this project is a novel information fusion framework using multimodality diagnosis for pipe materials for accurate probabilistic strength and toughness estimation under uncertainties. The first task will be chemical composition, material microstructure, and basic surface mechanical properties are detected using various in situ and ex situ techniques. Advanced data analysis using Gaussian Processing model will be performed for surrogate modeling and uncertainty quantification. Following this, advanced sensing techniques using acoustic and electromagnetic sensing will be considered. Both simulation and prototype testing are proposed for model validation and demonstration. Finally, a generalized Bayesian network methodology is planned to fuse multiple sources of information from the multimodality diagnosis results. Probabilistic pipe strength and toughness estimation is inferred based on the posterior distribution after information fusion. If successful, this study can help to accurately and effectively assess the reliability of pipeline systems, and eventually help the decision making process to balance the pipeline safety and economical operations.