Toward a Big Data-Based Approach: A Review on Degradation Models for Prognosis of Critical Infrastructure

Guru Prakash, Xian Xun Yuan, Budhaditya Hazra, Daijiro Mizutani

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Safety and reliability of large critical infrastructure such as long-span bridges, high-rise buildings, nuclear power plants, high-voltage transmission towers, rotating machinery, and so on, are important for a modern society. Research on reliability and safety analysis started with a "small data"problem dealing with relative scarce lifetime or failure data. Later, degradation modeling that uses performance deterioration, or, condition data collected from in-service inspections or online health monitoring became an important tool for reliability prediction and maintenance planning of highly reliable engineering systems. Over the past decades, a large number of degradation models have been developed to characterize and quantify the underlying degradation mechanism using direct and indirect measurements. Recent advancements in artificial intelligence, remote sensing, big data analytics, and Internet of things are making far-reaching impacts on almost every aspect of our lives. The effect of these changes on the degradation modeling, prognosis, and safety management is interesting questions to explore. This paper presents a comprehensive, forward-looking review of the various degradation models and their practical applications to damage prognosis and management of critical infrastructure. The degradation models are classified into four categories: physics-based, knowledge-based, data-driven, and hybrid approaches.

Original languageEnglish
Article number021005
JournalJournal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems
Issue number2
Publication statusPublished - 2021 May 1


  • continuous and periodic condition assessment
  • diagnostic feature extraction
  • online diagnostic approaches


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