EMACH SHM Platform – Real-Time Structural Defect Detection

Overview:
A high-performance AI system for detecting structural defects in bridges and infrastructure in real-time, combining visual inspections and sensor data.

Key Features:

• CNNs, U-Net, and attention-based models for precise defect detection
  
  

• Multi-GPU distributed training with FP16/BF16 mixed precision
  
  

• Low-latency inference (<150ms) for live inspections
  
  

• Multimodal data fusion: images + sensor signals
  
  

• Automated risk scoring and anomaly prioritisation
  
  

• Containerised deployment with Docker/Kubernetes
  
  

• Real-time dashboards for actionable decision support
  
  

Impact:

• Faster, safer, and more accurate structural inspections
  
  

• Optimised maintenance scheduling with data-driven insights
  
  

Visuals / Media Suggestions:

• Annotated defect images
  
  

• Dashboard screenshots
  
  

• AI pipeline diagram: Image + Sensor → Inference → Risk Score
  
  

Crishel – Multi-Structure Defect Inspection Platform

Overview:
Crishel is a scalable AI application for detecting and analysing defects across multiple structure types (concrete, steel, masonry, cast iron) using images and sensor metadata.

Key Features:

• YOLOv8-based defect detection with custom labels
  
  

• Metadata-driven search, filtering, and reporting
  
  

• AI overlays with risk scoring and severity prioritisation
  
  

• Multi-GPU training and inference pipelines
  
  

• Image comparison, map-based views, downloadable inspection reports
  
  

• Streamlit web app deployment with CI/CD
  
  

Impact:

• Efficient inspection of large infrastructure portfolios
  
  

• Accurate, consistent defect detection
  
  

• Supports predictive maintenance and proactive risk mitigation
  
  

Visuals / Media Suggestions:

• Annotated images of defects
  
  

• Interactive dashboards and map views
  
  

• Risk scoring tables
  
  

GaiaMind – Environmental Disaster Prediction

Overview:
A multi-modal AI system predicting environmental hazards using satellite imagery, IoT sensors, weather models, and news/social feeds.

Key Features:

• CNN/U-Net for satellite imagery, LSTM/GRU for sensor forecasting
  
  

• Transformer-based NLP for anomaly detection in news/social feeds
  
  

• Multi-GPU pipelines for large-scale datasets
  
  

• Real-time inference (<150ms) on cloud and edge devices
  
  

• Cloud-native deployment with dashboards and GPT-powered assistant
  
  

Impact:

• Proactive environmental disaster prevention
  
  

• Real-time alerts enabling measurable social and economic benefits
  
  

Visuals / Media Suggestions:

• Satellite imagery overlays
  
  

• Dashboard screenshots
  
  

• System architecture diagram: Sensors + Data → AI Models → Dashboards
  
  

Pilot & Proof-of-Concept Projects

• Bridge Defect Risk Prioritisation Tool: AI scoring by severity and urgency
  
  

• Environmental Sensor Forecasting Prototype: Predictive models for local risks
  
  

• Edge AI Deployment Demo: Low-latency, high-performance inference on field devices
  
  

Technologies Used:
Python, PyTorch Lightning, TensorFlow 2.x, Hugging Face Transformers, CNN/U-Net, LSTM/GRU, Vision-Language Models, LLMs, Diffusion Models, ONNX, TensorRT/TensorRT-LLM, vLLM, CUDA, Docker, Kubernetes, Multi-GPU DDP, CI/CD, Cloud-native deployment, GPT integration