The main objective of this paper is to develop accurate novel frameworks for the estimation of the maximum pitting corrosion depth in oil and gas pipelines based on data-driven techniques. Thus, different advanced approaches using Artificial Intelligence (AI) models were applied, including Artificial Neural Network (ANN), M5 Tree (M5Tree), Multivariate Adaptive Regression Splines (MARS), Locally Weighted Polynomials (LWP), Kriging (KR), and Extreme Learning Machines (ELM).Additionally, a total of 259 measurement samples of maximum pitting corrosion depth for pipelines located in different environments were extracted from the literature and used for developing the AI-models in terms of training and testing.Furthermore, an investigation was carried out on the relationship between the maximum pitting depths and several combinations of probable factors that induce the pitting growth process such as the pipeline age, and the surrounding environmental properties. The results of the proposed AI-frameworks were compared using various criteria. Thus, statistical, uncertainty and external validation analyses were utilized to compare the efficiency and accuracy of the proposed AI-models and to investigate the main contributing factors for accurate predictions of the maximum pitting depth in the oil and gas pipeline.

本文的主要目的是基于数据驱动技术，开发准确的新颖框架，用于估算油气管道中的最大点蚀深度。因此，应用了使用人工智能（AI）模型的不同高级方法，包括人工神经网络（ANN），M5树（M5Tree），多元自适应回归样条（MARS），局部加权多项式（LWP），克里格（KR）和极限学习机（Extreme Learning Machines，ELM）。此外，还从文献中提取了259个不同环境下管道的最大点蚀深度的测量样本，并用于在训练和测试方面开发AI模型。对最大点蚀深度与诱发点蚀生长过程的可能因素的几种组合之间的关系进行了研究，例如管道寿命和周围环境特性。拟议的AI框架的结果使用各种标准进行了比较。因此，利用统计，不确定性和外部验证分析来比较所提出的AI模型的效率和准确性，并研究对石油和天然气管道中最大点蚀深度进行准确预测的主要因素。