Layout Optimization of Multi-Type Sensors and Human Inspection Tools With Probabilistic Detection of Localized Damages for Pipelines

In this paper, a new layout optimization approach for health monitoring of locally damaged and non-piggable segments of pipelines is presented. In contrast to the existing literature, the proposed optimization-based approach considers (i) sensors network and human inspection, as detection methods, together, (ii) severity level of damages, such as damage size and risk of failure, (iii) three probabilistic detection metrics (to detect, infer, and size different damages) and a health monitoring cost metric simultaneously, (iv) several key attributes of detection methods, such as data acquisition cost and frequency, in an optimization context, and (v) probabilistic sampling methods and probabilistic damage data. An optimization model for determining decision variables such as the type and location of sensors and human inspection areas and tools along a pipeline is formulated. Due to the unavailability of publicly available real-world data and benchmarks, applications of the proposed approach are demonstrated using two notional examples with synthetically generated damage data. In the first example, a step-by-step demonstration of the proposed approach is given. Considering the severity level of different localized damages, it is shown that the proposed layout optimization approach can obtain a better and more robust solution, especially when used during a pipeline design, compared to those that only consider a single detection method or rely on deterministic damage data. In the second example, a longer pipeline segment with a greater density of localized damages is considered to show applicability of the proposed approach to larger sized problems. Finally, based on the results obtained it is demonstrated that the proposed approach provides a practical solution for consideration of probabilistic detection metrics and probabilistic damage data corresponding to stochastic degradation processes in optimal health monitoring of pipelines.