Sector · Heavy industry and construction
Physical AI for heavy industry and construction
Mills, factories and building sites produce counts, alarms and progress data in many systems, and each one sees only part of the operation. These are the questions engineers ask first, answered from 3 published CodeNinja Atoms reference architectures.
- What does a physical AI system for heavy industry and construction look like?
- Which AI models can a heavy industry and construction operator run on its own hardware?
- How much compute and hardware does AI in heavy industry and construction need?
- Is it cheaper to own AI hardware or rent cloud GPUs in heavy industry and construction?
- What ontology or object model does a heavy industry and construction AI system need?
- Who approves the decisions an AI system makes in heavy industry and construction?
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.
| Design | Country | What it does |
|---|---|---|
| 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.
| Design | Models |
|---|---|
| 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 |
| Design | Choice | What was picked | Why |
|---|---|---|---|
| Structure Phase Watch | Detection and | RF-DETR fine tuned on site footage | Apache-2.0 from package to checkpoints; |
| Structure Phase Watch | segmentation | the JV owns and hardens weights a | packaged safety product would rent |
| Structure Phase Watch | Tracking | Roboflow trackers, ByteTrack class | Apache-2.0 motion only identity without the AGPL exposure of the BoxMOT collection |
| Structure Phase Watch | Forecasting | Chronos-2 universal forecasting | Apache-2.0 with no field of use restriction; weights may be held, fine tuned and redistributed on site |
| Structure Phase Watch | Embedding | BGE-M3 hybrid multilingual embedder | MIT with no field of use restriction; multilingual retrieval across a workforce tagged by language |
| Structure Phase Watch | Frontier | One node of eight 141 GB HBM GPUs (H200 | 1,128 GB holds the 904 GB of FP8 weights, |
| Structure Phase Watch | hardware | class) | KV cache and activations with headroom for sessions |
| Structure Phase Watch | Edge compute powered enclosures | Jetson Orin industrial edge class in solar coverage at gates and laydown areas | Detection survives patchy private LTE |
| Structure Phase Watch | Site inference | Eight L40S class PCIe GPUs, 48 GB class | Forecasting, embedding and breach |
| Structure Phase Watch | server | aggregation on JV hardware in the site data | room |
| Structure Phase Watch | Camera class Profile S/T; thermal at 9 Hz or less | Fixed visible cameras, Frigate ready ONVIF a reuse survey holds; the 9 Hz ceiling keeps | The existing 38 fixed cameras serve where US origin thermal units outside the 6A003.b.4.b export license to Saudi Arabia |
| Structure Phase Watch | Sizing rules gain, with a cleaning interval as a design parameter | Enclosure thermal budget including solar accuracy problems unless the thermal budget is engineered first | Dust, glare and 50 C summer ambient fail as |
| Structure Phase Watch | Sensing haulage GPS for 40 tracked trucks plus proposed trackers for 20 mixers, weather station, biometric turnstiles, batch plant SCADA | Crane anti-collision logs, crawler telematics, crews actually did, joined through the adapters | Ground truth of what cranes, trucks and |
| Structure Phase Watch | Pattern recommendation with a named approver PA R T I I I · C H A P T E R 9 Shadow Mode Comes Before Any Flag Is Trusted Three phases with counted items and hard gates prove the slip forecast and the breach detection against the record before anyone acts on a flag. Chapter 8 fixed the models, their licenses and the hardware classes they run on. This chapter sets out how the design earns the right to be trusted: three phases with counted items, workstreams and hard exit gates, so the slip forecast and the breach detection are proven against the record before anyone acts on a flag, and the work can be stopped cheaply if a gate does not hold. | Adapter tier, one object model, never directly, and every re-sequencing | Every source enters through an adapter, output stays a recommendation a person approves |
| Steel Count Ledger | Detection model | RF-DETR, Nano to Large checkpoints, fine-tuned per product type and per mill | Apache-2.0 from package to checkpoints; the operator owns and hardens the weights that count its product |
| Steel Count Ledger | Tracking | Roboflow trackers, OC-SORT and ByteTrack class | Apache-2.0; holds identity across frames so one billet crossing two camera fields counts once |
| Steel Count Ledger | Work surface model | GLM 5.3, 753B parameter mixture of experts, FP8 | Frontier open weights for the operator's compliance agents; 904 GB against one node of 1,128 GB; bespoke license allows commercial use, fine-tuning and redistribution |
| Steel Count Ledger | Edge compute | GPU-accelerated industrial PC class, sealed, wide temperature, cooled enclosure | Sized from stream decode plus inference with headroom, hardened for mill heat, dust and electromagnetic interference |
| Steel Count Ledger | Frontier node | One node of eight 141 GB HBM GPUs, H200 class, in a dedicated data center | The class is export controlled for Pakistan, so the node runs where an export license naming the end user holds, checked at the phase one licensing checkpoint |
| Steel Count Ledger | Cameras | IP66/67 HDR industrial camera class, chosen from the pixel-density table per installation point; thermal, ECCN 6A003, only where hot billets blind visible cameras | Survives the mill environment while meeting country legality per model |
| Steel Count Ledger | Enclosures and power | Closed-loop cooled enclosures, vibration-damping mounts, surge protection, UPS with clean shutdown under NUT monitoring | A failing cooler is caught before the industrial PC cooks |
| Steel Count Ledger | Site networking | Hardened PoE industrial switches, VPN tunnel, IEC 62443 zoning | The camera zone stays segmented from mill control |
| Steel Count Ledger | Time synchronization | PTP grandmaster with GNSS and OCXO | Count events reconcile across systems only when every clock agrees |
| Factory Fire Watch | Model | IBM Granite Tiny Time Mixers TTM-R2, about 0.85M parameters, FP32, about 0.003 GB | CPU-only forecaster inside the operator's platform hosted in Saudi Arabia; Apache-2.0 with no field of use restriction leaves the artifact owned outright |
| Factory Fire Watch | Hardware class | Rugged enclosure, mounting, power and UPS with NUT-class clean shutdown; IP ratings per IEC 60529; NEMA enclosure types; SABER conformity | Outdoor exposure to dust, direct sun and hose washing, with certification lead time built into the bill of materials |
| Factory Fire Watch | Hardware class | Fanless hardened industrial switches; copper-versus-fibre run selection | Fibre between buildings and near the high-current systems being metered; copper for short powered drops |
| Factory Fire Watch | Sensing | FACP general alarm status contacts; fire pump controller PLC IOs and relays | Read-only interface into certified life-safety equipment, never a write path |
| Factory Fire Watch | Sensing | Submersible tank level transmitters; CT energy meters; LoRaWAN field sensors with store-and-forward gateways | The four monitored point families: panel status, pump status, water level and energy |
| Factory Fire Watch | Compute | None purchased; CPU inside the operator's existing IoT platform; field gateways carry no model runtime | The requirement asks for no server or GPU, and compute that is not needed at the extremity is not deployed there |
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.
| Design | Part | The design |
|---|---|---|
| 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.
| Design | Line | Three years |
|---|---|---|
| Structure Phase Watch | Three-year cost, owned | About 642,000 US dollars with support and power at the Saudi industrial tariff |
| Structure Phase Watch | 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 |
| Structure Phase Watch | 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 |
| Steel Count Ledger | 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.
| Design | Size | Objects | Download |
|---|---|---|---|
| Structure Phase Watch | 15 objects, 12 links | 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) | objects.json |
| Steel Count Ledger | 14 objects, 12 links | 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 | objects.json |
| Factory Fire Watch | 14 objects, 12 links | 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 | objects.json |
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.
| Design | Human control |
|---|---|
| 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 |
Every answer on this page is drawn from the papers linked in it: each paper's At a glance table, model register and cost appendix. Full text for agents: llms-full.txt. Designed on Praxis; object models load into Hyper Ontology.