Queensland Rail Predictive Analytics

Project Overview

Client: Queensland Rail (QR)
Duration: 2022
Role: Lead Data Scientist
Impact: $60 Million in Cost Savings

The Challenge

Queensland Rail manages over 200 aging bridge assets across their network. They needed a scalable solution to: - Evaluate current asset performance - Predict future degradation patterns - Optimize their substantial maintenance budget - Improve operational safety without disrupting services

My Solution

1. Scalable Python Architecture

Developed automated Python solutions that could handle 200+ individual structural members efficiently:

# Example: Asset performance evaluation framework
class AssetPerformanceAnalyzer:
    def __init__(self, asset_data):
        self.data = asset_data
        self.models = {}

    def predict_degradation(self, time_horizon):
        # Probabilistic degradation modeling
        pass

2. Bayesian Networks for Uncertainty

Implemented Bayesian Networks to update material strength predictions using new measurement data: - Incorporated prior knowledge from historical data - Updated beliefs with new inspection results - Quantified uncertainty in predictions

3. Causal Inference for Decision Support

Built causal models to evaluate operational decisions: - Analyzed impact of restricting train passage during cyclones - Balanced safety improvements with operational continuity - Achieved 40% safety improvement with minimal disruption

Technical Stack

  • Languages: Python
  • Libraries: Pandas, NumPy, Scikit-learn, PyMC
  • Methods: Monte Carlo Simulation, Bayesian Inference, Causal Inference
  • Infrastructure: HPC Cluster with SLURM, Parallelized Computing

Key Outcomes

Financial Impact

  • $60 million in allocated maintenance cost savings
  • Optimized budget allocation across asset portfolio
  • Reduced unnecessary preventive maintenance

Safety Improvements

  • 40% increase in operational safety metrics
  • Better risk-informed decision making
  • Proactive intervention strategies

Technical Achievements

  • Processed millions of data points efficiently
  • Reduced computation time by 95% through parallelization
  • Created reusable framework for future projects

Lessons Learned

  1. Domain Knowledge is Critical: Understanding railway engineering constraints was essential for developing practical solutions

  2. Stakeholder Communication: Regular presentations to management ensured buy-in and adoption

  3. Scalability First: Building with scalability in mind saved significant time as the project expanded

Future Applications

The framework developed for this project has potential applications in: - Road infrastructure management - Pipeline integrity assessment - Building portfolio optimization - Any large-scale asset management scenario

This project demonstrates my ability to deliver high-impact data science solutions that translate directly to business value. The combination of advanced analytics with domain expertise enabled Queensland Rail to make data-driven decisions that improved both safety and financial outcomes.

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