Research Areas and Integration Value Chain
PiM Lab is organized around three major research programs supported by original optimization and engineering AI.
I. Engineering AI and Civil–Hydraulic Informatics
Intelligent Infrastructure
- Bridge deterioration, UAV inspection, and repair cost analysis
- Structural health monitoring, retrofit, and deformation recognition
- Underground pipeline leakage and acoustic signal analytics
- Lifecycle maintenance and digital twins
Materials, Geotechnics, and Water
- Performance prediction for concrete, steel, soils, and composites
- Earthquake, liquefaction, slope, and hazard assessment
- Water quality, drinking-water safety, wastewater, and environmental governance
- Failure investigation and reliability analysis
Engineering Knowledge and Agents
- Multimodal large language models and RAG
- Construction procurement, contracts, and legal consultation
- Agentic AI and engineering knowledge management
- Computer vision, AIoT, and edge intelligence
II. Sustainable Built Environment and Building Energy
Building Energy and HVAC
- Heating/cooling loads, energy use, and comfort forecasting
- HVAC degradation, remaining useful life, and service life
- Energy management for commercial, residential, and campus buildings
- Anomaly detection and real-time early warning
Microgrids and Energy Resilience
- Photovoltaics, battery storage, and energy management systems
- SoC/SoH, capacity planning, and multi-objective control
- Campus grid import and contract-capacity risk
- Net-zero buildings, electric vehicles, and smart dispatch
Circular Economy and Facilities
- Semiconductor facilities and construction-material circularity
- Automated facility modeling and optimized design
- Plant microbial fuel cells and low-carbon technologies
- Integration of energy, cost, carbon, and supply chains
III. Decision Intelligence, Risk, and Project Governance
Project Analytics and Performance
- Cost, schedule, resources, and project-performance analytics
- Public-infrastructure performance and stage-gate mechanisms
- Construction technology assessment and project behavior
- Public–private partnerships and infrastructure governance
Decision and Financial Intelligence
- Construction costs, price escalation, and financial risk
- Portfolio optimization, hedging, and market analytics
- MCDM, fuzzy inference, and scenario planning
- Procurement and contract decisions
Risk, Failure, and Disaster Resilience
- Construction safety, risk culture, and behavior
- Engineering loss, wind-turbine collapse, and failure investigation
- Urban disaster response, resource allocation, and resilience
- Monte Carlo simulation and reliability assessment
Cross-Cutting Methods
Research Integration Value Chain
1. Problem Framing
Define consequential questions from engineering sites, industrial needs, policy, and societal risk.
2. Data and Methods
Build data governance, feature engineering, original algorithms, and AI models.
3. Validation and Deployment
Iterate through benchmarks, case studies, experiments, and field data.
4. Decisions and Diffusion
Deliver software, dashboards, maintenance strategies, policy insights, and trained people.
Source priority: formal records and official pages > the 2026 dossier > preserved legacy-site records. Dynamic citation and ranking metrics are dated.