This paper presents a data-driven approach to predict the pipelines’ corrosion-induced Burst failure. In this approach, different aspects of pit growth progression and spatial distribution of pits are simulated. The proposed approach takes advantage of population characteristics to model these aspects of the degradation paths for each pipe section down to the size of single joints. The insights obtained from simulations are used to project the degradation of each pipe section. Understanding corrosion behavior and field data are used to model the corrosion-related parameters such as corrosion pit dimensions, probability and time of initiation, and location. The failure is modeled using the probabilistic simulation considering degradation rate, interactions among pits, and material properties as stochastic variables. The proposed approach and included models are tested using multiple real-life inline inspection datasets. Validation of predicted properties shows prediction errors ranging from 3%–10% depending on the three remaining strength calculation approaches. This work aimed to serve as an important tool for risk-based maintenance prioritization, inspection interval assessment, and the fitness of service assessment of pipelines.

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Chemical process industries are complex environments prone to accidents with potentially drastic consequences. This ever-growing sector is becoming increasingly complex to satisfy the needs of global energy markets and consumer supply chains. Parallel to this growth and increased complexity, the number of accidents where conventional safety practices have failed to prevent them rises. Many relate such shortcomings in the preventive approach to the unattainability of imagining all failure scenarios. Moreover, conventional safety's reliance on linear causal chains impairs its ability to comprehend the hazardous conditions arising from human, societal, and organizational factors' interaction with the technical system. A further complicating factor adding to the ambiguity of these interactions is the digitization of chemical process industries. Rapid technology integration, application of novel tools, and associated methods could increase the possibility of deviating from normal operating conditions and result in hazardous conditions. Many believe resilience-oriented concepts, if not a paradigm shift in safety, could improve the shortcomings of conventional safety practices. Resilience's approach to safety recognizes that all expected deviations and unknown hazards cannot be prevented; therefore, it increases system readiness and strengthens the capacity to absorb, adapt, and recover from adverse events to prevent catastrophic failures. This chapter aims to provide inclusive yet brief insights into resilience, why the chemical process industries should strive to become resilient, how resiliency is achieved, and how it may be measured by reviewing the state-of-the-art published literature concerned with resilience assessment.@en