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cbm

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A deep reinforcement learning system for optimizing bridge maintenance decisions across municipal infrastructure fleets, implementing cross-subsidy budget sharing and cooperative multi-agent learning.

  • Updated Dec 5, 2025
  • Python

Deep Q-Network implementation for optimal bridge maintenance planning using Markov Decision Process formulation with vectorized parallel training. Based on Phase 3 (Vectorized DQN) from dql-maintenance-faster project.

  • Updated Dec 8, 2025
  • Python

This system analyzes bridge repair method recommendation reports generated by AI agents and visualizes the decision-making pathway from damage → deterioration factors → repair methods as a Decision Tree. It aims to "make the thought process visible."

  • Updated Dec 13, 2025
  • Python

EN : Water Pipe Leakage Risk Prediction System v0-3 | Industrial-grade GPR system achieving R²=0.9894 accuracy with 1,907x parallel processing efficiency | Complete scalability proven from N=44 to N=6000 datasets. JP : 水道管漏水リスク予測GPRシステム v0-3 | 産業レベル予測精度R²=0.9894、1,907倍並列処理効率化を実現したガウス過程回帰による高精度漏水リスク分析システム | N=44→N=6000完全スケーラビリティ実証済み

  • Updated Sep 21, 2025
  • Python

C51 Distributional DQN (v0.8) for bridge fleet maintenance optimization. Implements categorical return distributions (Bellemare et al., PMLR 2017) with 300x speedup via vectorized projection. Combines Noisy Networks, Dueling DQN, Double DQN, PER, and n-step learning. Validated on 200-bridge fleet: +3,173 reward in 83 min (25k episodes).

  • Updated Dec 8, 2025
  • Python

This tool applies self-improving (Agentic) clustering to bridge maintenance data in Open data at some Prefecture, Japan, to automatically identify bridge groups with high maintenance priority.

  • Updated Nov 29, 2025
  • Python

An AI-powered bridge health classification system that automatically categorizes bridge inspection reports into health levels using machine learning. The system leverages Explainable Boosting Machine (EBM) to achieve high accuracy while maintaining interpretability.

  • Updated Oct 3, 2025
  • Python

This project applies self-improving (Agentic) clustering with Bayesian Optimization to bridge maintenance data in some Prefecture, Japan, to automatically identify bridge groups with high maintenance priority.

  • Updated Nov 30, 2025
  • Python

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