Work

Designing AI-Powered Early Warning Systems to Detect and Prevent Critical Failures in Aging Infrastructure Across Urban Landscapes

Year: 2024

Type: conference-paper

Abstract: AI-powered early warning systems offer a proactive approach to detecting and preventing critical failures in aging urban infrastructure. This study explores the design and implementation of machine learning models for real-time monitoring and failure prediction. Results demonstrate significant improvements in predictive accuracy and response times, supporting the sustainability and resilience of urban landscapes.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14268706

Author: Al-Qarni, Areej

Institution: King Abdullah University of Science and Technology

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6893164805

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