Andy Yang and Hannah Chen, San Jose WaterSan Jose Water, serving over 1 million people in the San Francisco Bay Area, is committed to maintaining its high level of service and reducing the number of water main breaks resulting in social, financial, and environmental impact. In the effort to develop and implement sound asset management strategies for pipelines, San Jose Water completed exhaustive probability of failure (PoF) and consequence of failure (CoF) analyses for all 2,400 miles of pipe in its system. San Jose Water tuned and trained an Artificial Neural Network (ANN), through MATLAB’s powerful machine-learning Deep Learning toolbox, to predict the PoF failure for each pipe segment in the distribution system given data concerning the pipeline’s characteristics, its environment, and its historic leaks. San Jose Water also developed a comprehensive CoF framework and used robust hydraulic modeling and spatial analysis tools to identify the triple bottom line (social, financial, and environmental) impact associated with each pipe. San Jose Water finally combined the PoF and CoF results to determine overall business risk exposure, replacement strategies, and O&M strategies. The strategies and processes used by San Jose Water are scalable for both small and large systems and are relevant to any industry interested in managing business risk exposure associated with pipelines.