Engineering & computing · OECD FoS 2.1 · China MoE 0813 建筑学 / 0814 土木工程
Architecture & civil engineering
建筑与土木工程
L1Assistive → Steady Score 1.25 · Last reviewed 2026-10-22 · Next review 2027-01-19
BIGGEST BREAKTHROUGH 2017-05 AI · majorField · major
Cha et al.: deep-learning crack damage detection using CNNs (CACIE) ↗
Foundational deep-learning visual inspection paper in SHM
Why this level
Broad but shallow: RIBA's 2026 report says 74% of practices use AI on some projects, mostly general generative tools; structural engineers keep judgment central. No recognised AI-originated design breakthrough.
S = 0.30·method + 0.35·discovery + 0.25·adoption + 0.10·education (education scores 0 when there is no evidence; if empty, the other weights are renormalised)
Sources
- riba.org/media/fecm2pl5/riba-ai-report-2026-final_v3.pdf
- ncsea.com/news-post/structural-engineering-report-explores-the-future-of-ai-adoption-workforce-and-teams
Composite 0.75–1.49: AI is an aid in parts of research or practice, with cases or surveys, but no recognised AI-key result. All levels →
Research directions
Generative concept designBIM automationStructural optimisation
Papers
- Automated compliance checking across the building lifecycle: Systematic and semantic review integrating PRISMA and deep search
Compliance checking is AI’s main entry point
2026-05 · Automation in Construction
- Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Foundational deep-learning visual inspection paper in SHM
2017-05 · Computer-Aided Civil and Infrastructure Engineering
- AI-Generated Deepfake Financial Scams: A Missing Liability Regime for Consumer Protection Frameworks
Argues consumer-protection law lacks a liability regime for GenAI deepfake financial scams.
2026-10-19 · OpenAlex · Asian Journal of Comparative Law · A Lui, A Miglionico
- Indoor temperature prediction methods for urban overheating: A systematic review
Review of 46 studies on indoor temperature prediction methods (ML 17.4%, DL 8.7%) for city-scale overheating.
2026-10-19 · OpenAlex · Building Simulation · Sepideh Motamedpooya, Rodney A. Stewart, Shannon Rutherford, Aaron J. E. Bach
- A multi‑modal recognition method for building structural materials integrating structured text description and adaptive decision weighting
Multimodal recognition of building structural materials combining images and structured text.
2026-10-17 · OpenAlex · Discover Artificial Intelligence · Min Ma
- Generative adversarial networks with Monte Carlo dropout uncertainty quantification for structural health monitoring: Fibre Bragg grating data augmentation comparison in Hawk T1A aircraft
Conditional GANs with MC-dropout UQ augment fibre-Bragg-grating structural health monitoring data.
2026-10-15 · arXiv · Alvaro Peiro, Marios Impraimakis
- From arXiv, bioRxiv, medRxiv and OpenAlex (last ~30 days); summaries written by AIMap from the abstract.
Milestones 7
Big glowing nodes are leaps: AI leap cool · Field leap warm · double ring = both
-
Deployment
Spokane offers free Permittable AI intake-completeness pre-screening for wildfire-rebuild residential permitsAI: calibrated to the city’s submittal rules to flag missing documents and sheets. Field: another city adopts AI permit pre-checks, limited to completeness
City of Spokane -
Deployment
City of Toronto launches AI Building Permit Application Pre-Check pilotMajor city adds AI compliance pre-check
City of Toronto -
Industry
RIBA AI Report 2026: 74% of UK practices use AI on some projects; 40% for compliance checksAdoption rose from 59% to 74% in a year
RIBA -
Paper
Systematic review: automated compliance checking across the building lifecycle (Automation in Construction)Compliance checking is AI’s main entry point
Automation in ConstructionTo be verified -
Deployment
Burlington, Canada launches eCheck assistive AI to pre-check development applications against building code and zoningAI enters permit review; staff retain decisions
City of Burlington -
Deployment
Denver City Council approves a 5-year, $4.59M ComplyAI (CivCheck) AI-guided plan-review contract (10–1)Major US city buys AI code-compliance review; human review still required
Colorado Politics -
Paper
Cha et al.: deep-learning crack damage detection using CNNs (CACIE)Foundational deep-learning visual inspection paper in SHM
Computer-Aided Civil and Infrastructure EngineeringTo be verified
No milestones match this filter.
More papers & repos 2
| Type | Title | Date |
|---|---|---|
| Paper | Automated compliance checking across the building lifecycle: Systematic and semantic review integrating PRISMA and deep search To be verified | 2026-05 |
| Paper | Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks To be verified | 2017-05 |