# Physical AI for heavy industry and construction: questions answered Canonical: https://codeatoms.ai/sectors/heavy-industry-and-construction/ License: CC BY 4.0 Publisher: CodeNinja Atoms (https://codeatoms.ai) Mills, factories and building sites produce counts, alarms and progress data in many systems, and each one sees only part of the operation. ## What does a physical AI system for heavy industry and construction look like? A complete physical AI design for heavy industry and construction names what to sense, which existing systems to join, the object model that joins them, the models and hardware, the three-year cost and the person who approves every action. CodeNinja Atoms has published 3 such reference architectures for heavy industry and construction, each free to reuse under CC BY 4.0. Structure Phase Watch (Saudi Arabia): one live model of a construction site's structure phase that forecasts schedule slips three days out and shows safety breaches as they happen, on the contractor's own hardware inside the Kingdom. Steel Count Ledger (Pakistan): cameras and a GPU industrial PC at every casting strand and cooling bed count billets, ingots, rebars and girders as they are made, publish the counts through a one-way link into one record the authority owns, and let revenue officers reconcile counted against declared production. Factory Fire Watch (Saudi Arabia): live, read-only visibility of fire alarm panels, fire pumps, fire water tanks and energy meters across an operator's highest-risk factories, read through contacts, PLC inputs and LoRaWAN into an IoT platform hosted in Saudi Arabia, with never a write path into certified life-safety equipment. ## Which AI models can a heavy industry and construction operator run on its own hardware? Each published heavy industry and construction design names its models and why, and every model is open-weight or no model is used at all, so the operator can run it on hardware it owns. Structure Phase Watch: 5 self-hosted open models: GLM 5.3 (reasoning), Chronos-2 (forecasting), BGE-M3 (multilingual retrieval), RF-DETR (vision), Roboflow trackers. Steel Count Ledger: 3 self-hosted open models: RF-DETR (detection, fine-tuned per product type and mill), Roboflow trackers (identity across camera fields), GLM 5.3 (compliance work surface). Factory Fire Watch: One: IBM Granite Tiny Time Mixers (TTM-R2), about 0.85 million parameters, Apache-2.0, on CPU inside the platform; threshold evaluation on the safety path is rule logic. ## How much compute and hardware does AI in heavy industry and construction need? The compute follows from the models: the published heavy industry and construction designs size it as follows, from no new hardware to a full GPU node. Structure Phase Watch, frontier compute: One node of eight 141 GB HBM-class GPUs holds GLM 5.3 at FP8 (753 GB of weights, 904 GB with headroom). Structure Phase Watch, edge: Five Jetson Orin class nodes in solar powered enclosures at the gate, laydown, crane slew zone, batch plant and haul road. Steel Count Ledger, frontier compute: One node of eight 141 GB HBM-class GPUs holds GLM 5.3 at FP8 (753 GB of weights, 904 GB with headroom), in a dedicated data center behind an export licence checkpoint. Steel Count Ledger, edge: One sealed GPU industrial PC and IP66 HDR cameras per installation point; counting survives a slow wide-area link. Steel Count Ledger, the hard dependency: 141 GB-class accelerators need a US export licence for Pakistan (Country Group D:4); the node is ordered only once a licence naming the end user holds. Factory Fire Watch, compute: None bought: the requirement asks for no server or GPU, and the field gateways carry no model runtime. Factory Fire Watch, equipment, per factory: About 2,600 to 3,200 US dollars in list prices: a LoRaWAN gateway, two tank transmitters, two CT meters, four contact nodes, a switch, an enclosure and a UPS. ## Is it cheaper to own AI hardware or rent cloud GPUs in heavy industry and construction? Each heavy industry and construction design prices three years of ownership in its Appendix A, with every price cited, against renting the same capacity from a cloud region at its deepest three-year commitment where hardware is bought. Structure Phase Watch: three-year cost, owned, About 642,000 US dollars with support and power at the Saudi industrial tariff; three-year cost, rented, 1.41 million to 2.81 million US dollars for the same GPUs around the clock; ownership is about one half the cheapest three-year commitment; closed model break-even, The cheapest closed model matches the owned stack at about 31 users; above that, ownership is cheaper and the gap grows with every user. Steel Count Ledger: three-year cost, owned, About 4,445,000 US dollars for 300 installation points, of which the counting kits are 2,832,000 and cannot be rented; owning the frontier node costs about 686,000 against 751,000 on AWS's deepest three-year plan; closed model break-even, The cheapest closed model matches the owned node at about 33 users; above that, ownership is cheaper. Factory Fire Watch: three-year cost, 100 factories, About 328,000 to 439,000 US dollars with support and power; the eight-factory pilot is about 21,000 to 26,000 of equipment. ## What ontology or object model does a heavy industry and construction AI system need? The object model is the part that makes the system an ontology rather than a pipeline: typed objects for the things in the heavy industry and construction operation, with properties, status values and typed links. Every published design ships its object model as hyper-ontology/1 JSON that loads into Hyper Ontology. Structure Phase Watch: Tower crane (TC-01 to TC-14), Crawler crane (6 units), Casting bed, Precast unit, Flatbed or mixer truck, Batch ticket, P6 activity (owner baseline or fragnet), Permit to work, Incident or observation form, Gate movement, Delivery window, Lift schedule entry, Pour, Worker, Work zone (Zone A to D). Steel Count Ledger: Steel melting and re-rolling unit, Installation point, Industrial PC, Production count event, Product type (Billets, Ingots, Rebars, Girders), Declared production, Discrepancy case, Revenue field officer, Audit team, Steel mill staff, Tamper or offline alert, Daily uptime record, Product calibration record, Weighbridge record. Factory Fire Watch: High-risk factory, Fire alarm control panel, Fire pump, Fire water tank, Tank level transmitter, Energy meter (CT set), LoRaWAN gateway, Monitored point, Safety-critical alert, Audit log record, Periodic monitoring round, Preventive maintenance visit, Technical personnel, Monitoring officer. ## Who approves the decisions an AI system makes in heavy industry and construction? In every published heavy industry and construction design a named person makes the decision that changes the physical world; the system prepares it. Structure Phase Watch: Every re-sequencing is a recommendation a named planner approves; every breach alert is confirmed, dismissed or escalated by the HSE officer. Steel Count Ledger: Every discrepancy case is judged by a named revenue field officer; the system counts and reconciles, it never assesses, and the mill never edits a count. Factory Fire Watch: A monitoring officer acknowledges or escalates every safety-critical alert; the design monitors and never controls a panel, pump or valve. ## Sources - [Structure Phase Watch: Live Production, Crane and Delivery Evidence for Every Pour on a Construction Site](https://codeatoms.ai/structure-phase-construction-saudi-arabia/) (Saudi Arabia), DOI https://doi.org/10.5281/zenodo.23126448 - [Steel Count Ledger: Independently Counted Production for Every Steel Mill in Pakistan](https://codeatoms.ai/steel-production-count-pakistan/) (Pakistan), DOI https://doi.org/10.5281/zenodo.23126563 - [Factory Fire Watch: Read-Only Smart Fire Protection Monitoring for Every High-Risk Factory](https://codeatoms.ai/factory-fire-monitoring-saudi-arabia/) (Saudi Arabia), DOI https://doi.org/10.5281/zenodo.23126565