Damage Detection and Evaluation Model of Offshore Engineering Structures Based on Machine Learning

Offshore engineering structures, such as offshore drilling platforms and cross-sea bridges, are subject to the influence of harsh marine environment for a long time, which is prone to structural damage and poses a threat to national energy security and economic development. Traditional damage detection methods have some problems, such as low efficiency, high cost and limited detection range. In this paper, a model for damage detection and evaluation of offshore engineering structures based on machine learning is proposed. The model constructs a feature vector by extracting feature parameters from structural response data, and uses convolutional neural network (CNN) as the main algorithm to optimize CNN for the complexity of damage detection of offshore engineering structures. The model optimizes key parameters through cross-validation and grid search, and introduces Dropout and L2 regularization techniques to avoid over-fitting and improve generalization ability. Through the case analysis of the actual jacket platform, the effectiveness and accuracy of the model in practical application are verified. The model shows excellent identification ability and stability under different damage types and degrees, and the average accuracy, recall and F1 score are at a high level. This study provides strong technical support for the health monitoring and safety management of offshore engineering structures, and is of great significance to the intelligent development of offshore engineering.