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JRS Innovation (iFactory) — A cross-utility, segment-level water-main failure corpus assembled as a byproduct of deploying the prediction platform: for every pipe segment, feature rows (material, install vintage, diameter, joint type, manufacturer, soil conditions, pressure behaviour) joined to ground-truth outcome labels (broke / did not break, failure mode, emergency vs planned repair, replace vs repair) drawn from client break history and work orders. The commercially interesting object is the pooled view — one municipal utility's break history is too thin to train on, so only an integrato…
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A cross-utility, segment-level water-main failure corpus assembled as a byproduct of deploying the prediction platform: for every pipe segment, feature rows (material, install vintage, diameter, joint type, manufacturer, soil conditions, pressure behaviour) joined to ground-truth outcome labels (broke / did not break, failure mode, emergency vs planned repair, replace vs repair) drawn from client break history and work orders. The commercially interesting object is the pooled view — one municipal utility's break history is too thin to train on, so only an integrator deployed across many utilities holds enough labelled failures to make a robust model, and that pooled corpus is the scarce asset.
From 30 yearsCoverage Utilities · Information TechnologyAsset class Equities
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JRS Innovation (iFactory) — A cross-utility, segment-level water-main failure corpus assembled as a byproduct of deploying the prediction platform: for every pipe segment, feature rows (material, install vintage, diameter, joint type, manufacturer, soil conditions, pressure behaviour) joined to ground-truth outcome labels (broke / did not break, failure mode, emergency vs planned repair, replace vs repair) drawn from client break history and work orders. The commercially interesting object is the pooled view — one municipal utility's break history is too thin to train on, so only an integrato…
Jrsinnovation offers (Alternative, Geospatial, Supply Chain) — ~250K-2.5M labelled pipe-segment records pooled across client deployments (municipal distribution networks typically run 1,000-10,000 km of mains, i.e. roughly 50K-500K modelled segments per utility, each carrying asset attributes and a failure outcome per period), plus ingested SCADA pressure series and work-order text per client..
Pooled water-main failure prediction models and their training corpora; survival/hazard models for buried pipe by material and vintage; soil-corrosion susceptibility maps calibrated against realised failures; pressure-transient damage inference; capital-planning optimisation for pipe-replacement budgets; synthetic augmentation data for infrastructure AI; a benchmark corpus for main-break prediction research, which currently has no cross-utility labelled benchmark; insurer catastrophe and business-interruption models for water-network failure.
The data is with 30 years of history.
Coverage spans US, UK; Water Utilities, Application Software; alternative, geospatial, supply_chain; equities.
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