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Predictive Asset Stewardship Using AI & Data Analytics

  • Unique Paper ID: 168380
  • Volume: 11
  • Issue: 5
  • PageNo: 837-842
  • Abstract:
  • Deterioration of infrastructure especially road erosion poses a significant risk to public safety. Inclement weather and reduced traffic congestion improve this process. This paper presents a comprehensive framework for predictive asset management through the integration of artificial intelligence and data analysis. The goal is to develop an advanced predictive maintenance system that can predict road erosion before it becomes a hazardous situation. Using a hybrid short-term memory (LSTM) network and gradient booster (GBM), the model captures sequential patterns and complex nonlinear interactions within maintenance data. Maintain traffic weather history. Real-time analytics play an important role in this system. It is a constant feed of traffic and environmental data that drives dynamic predictions. This model leverages advanced engineering datasets. including traffic volume Vehicle type Weather variables and maintenance history This provides accurate insights into the accumulation of road wear. This information is displayed through an easy-to-use dashboard. Provide actionable advice by helping urban planners and maintenance teams proactively address degradation issues A structured asset management approach thus significantly reduces the chances of road accidents through timely intervention. To ensure the continued safety and efficiency of operations on major roads in the city.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 5
  • PageNo: 837-842

Predictive Asset Stewardship Using AI & Data Analytics

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