A conversational AI agent that turns kilometer-scale fiber-optic sensing (DAS) data into bridge-health answers anyone can act on — a verdict, the reasoning, and a report, in seconds.
Live demo — full dashboard + assistant on real data. Best on desktop. One-click Chinese/English switch, top-right.
Dashboard tour → plain-language question → verified anomaly analysis → standard figures → one-click Word report → publish back to the live screen. Every AI answer on screen is a live backend response — nothing is staged.
If the player shows an error, keep this HTML in the same folder as final.mp4 and final_zh.mp4 (subtitle sidecars final.srt / final_zh.srt included).
“We have the data — we just can’t read it.”
The live monitoring dashboard with the DASGPT assistant embedded (center), on real bridge data.
The LLM only routes questions to pre-validated engineering code — it never sees raw waveforms and never invents numbers. Every answer is traceable: which algorithm, what parameters, which data.
A live answer: the reference-channel PSD curve with the bridge’s modal peaks, computed on the client’s recordings and drawn directly in the chat.
Everything below is real and verbatim, translated from the original Chinese WeChat threads. Names are redacted; unredacted threads and a 756 KB written review document are available to the judges on request.
Raw recordings from an in-service railway bridge, handed over for us to build on — a higher-cost trust signal than any survey answer. Now the sole demo dataset.
A senior transport-industry executive tried the public demo the same week we sent it — then forwarded it to his chief engineer and two deputy chief engineers in charge of bridges.
A six-point written review anchored on our actual demo bridge, plus a 756 KB optimization document from a second reviewer — feedback about a system they expect to run.
Engineers of a major engineering company (Phase-2 cooperation with SUSTech) tested the live demo — and asked us to record the very demo film in this submission.
“I took a look at the intelligent bridge-health monitoring system this afternoon — the technology is very advanced, the functionality is powerful, and the value for money is good. 👍👍 I asked our two bridge deputy chief engineers to review it as well; I’m forwarding their comments verbatim.” — Senior executive, transport-engineering organization (translated)
“Tried it briefly — works great. I’ll test it more at work.” — Engineer, partner engineering company, after a hands-on trial (translated)
Exhibit A — the executive’s verdict (“technology very advanced … value for money good”); he asks two deputy chief engineers to review.
Exhibit B — from the deputy chief engineer’s six-point written review (excerpt; full thread available).
Exhibit C — partner engineer after a hands-on trial: “works great; I’ll test it more at work.”
Exhibit D — the partner group asks for a demo video — the film above answers that request.
What we do not claim: no paid contracts yet; structured operator interviews are the first post-competition step. The evidence here is early but real — a senior reviewer, written expert requirements, hands-on trials, all on the live product.
Pricing and packaging to be calibrated in the first pilot conversations.
1. Formal end-user interviews — bridge operators and government supervisors, run on the live demo. 2. From looped recordings to live streams — real-time ingestion and cloud storage for 24/7 monitoring. 3. First 1–2 pilot bridges, with alarm thresholds and report templates calibrated on site.
From replayed recordings to live data streams and cloud storage — 24/7 online monitoring on the same conversational entry point.
Same fiber, more scenarios: road-subgrade health, traffic flow and vehicle-speed recognition, dynamic vehicle weighing (weigh-in-motion), dam and embankment safety, slope stability, tunnels and underground pipe networks — toward one integrated road–bridge–tunnel–slope entry point. Grounded in real field work: on the Chicao G508 highway, DAS has already done subgrade imaging, traffic-event detection, and vehicle-weight estimation (most vehicles within about one tonne of error).
Standardized alarm thresholds and report templates, calibrated per structure against real operating conditions.
Multi-bridge, multi-project deployment with unified data management — from one bridge to a fleet, then tunnels, pipelines, and rail corridors.
Customer-driven backlog: the six-point expert review in the Validation section above is already scheduled into this roadmap — traceability against traffic records, a self-evolving knowledge base, cumulative fatigue-damage tracking, season-aware thresholds.
These are planned directions, stated as such — everything demonstrated elsewhere on this page ships today.
Thesis + first prototype: conversational analysis on sample data.
Our team’s production bridge-monitoring platform came together with DASGPT embedded inside — one integrated product, end to end.
Real client data end-to-end and made the sole dataset; public deployment; two-way dashboard write-back; one-click Word reports; 4:43 demo film in three cuts; full product manual.
A formal monitoring report, generated and downloaded from one sentence.
AI architect — agent harness, product, integration & deployment.
AI architect — agent tooling and evaluation.
DAS domain expert — bridge algorithms (PSD, modal analysis, anomaly detection).
DAS domain expert — algorithm development and validation.
Backed by real-world skin in the game: a client provided raw recordings from their in-service bridge, and industry engineers are already testing the live product hands-on.