# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 1 Introduction
studied whether the strength of the repaired offshore pipelines was sufficient to withstand the burst pressure load, constructed a strength calculation model, and verified the correctness of the calculation model by combining it with the finite-element method [16]. Although finite-element analysis often yields satisfactory results, the actual operating conditions of the pipeline need to be considered. To achieve high accuracy, many meshes are required, resulting in high computational costs and lengthy time consumption. In addition, if the defect parameters change, the model needs to be rebuilt, lacking replicability and portability, making this method inconvenient and inefficient. With the development of artificial intelligence (AI) technology, many researchers have introduced the machine learning (ML) approach to predict pipeline residual strength. For example, [PERSON] et al. utilized different machine learning algorithms to predict the failure pressure of defective offshore pipelines. The results showed that the Gaussian Process Regression (GPR) and Multi-Layer Perceptron (MLP) models performed the best among all models [17]. [PERSON] et al. applied the Artificial Neural Network (ANN) to the prediction of the remaining strength of defective pipelines, addressing the problem of model over-fitting caused by insufficient experimental data in the past. The results indicated that compared with the feed-forward neural network, the MLP method had higher prediction accuracy when the sample data were insufficient [18]. [PERSON] et al. proposed a data-driven method for predicting the remaining strength of corrosion defects in offshore pipelines. Compared with the prediction results of existing algorithms, the established model had advantages in terms of error and regression performance [19]. [PERSON] et al. combined the ANN with the finite element method to predict the failure pressure of defective pipelines. The study found that the combination of the two methods by the artificial neural network overcame the shortcomings of the finite element method, and by using the ANN as part of the evaluation framework, the failure pressure of corroded pipelines could be predicted in a short time without affecting the accuracy of the results [20]. [PERSON] et al. aimed to address the problem of significant errors in predicting the remaining strength of pipelines with defects and established a prediction model for the remaining strength of pipelines with defects based on SVR. The results showed that the minimum relative error of the SVR model's prediction results was 0.55%, the maximum relative error was 10.35%, and the average relative error was 2.63%, verifying the accuracy and robustness of the SVR model. The research results can provide decision-making support for pipeline operation scheduling, inspection, and maintenance [21]. [PERSON] et al. facing the difficulty of predicting the failure strength of internal corrosion defects in pipelines under external pressure, established a parametric analysis model for pipelines with corrosion defects, and analyzed the influence of corrosion defects and hole parameters on the collapse resistance strength of pipelines. The results showed that the established data-driven prediction model has good prediction  
Figure 1: Different prediction methods for pipeline residual strength. performance. This research can provide a reference for evaluating the strength of pipeline corrosion defects and establishing a safety early-warning mechanism [22]. [PERSON] et al., aiming to solve the problem of predicting the failure pressure of defective pipelines, proposed a method based on deep learning. The prediction results showed that the adopted deep-learning model had high prediction accuracy. In addition, under the same computational conditions, the calculation speed of the deep learning model was at least two orders of magnitude faster than that of finite element simulation. This research is expected to support the rapid prediction of defective oil and gas pipeline failure pressure [23].