Prediction method of surface settlement of rectangular pipe jacking tunnel based on improved PSO-BP neural network
Descripción general
Resumen del artículo
This study develops an improved PSO-BP neural network model to predict surface settlement during rectangular pipe jacking tunnel construction. The model, tested on a real-world case study, outperforms traditional methods by accounting for adaptive mutation and incorporating inertia weight and mutation particles for enhanced accuracy and stability. The findings suggest this model can be valuable for similar projects.
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Scientists found they developed a super smart computer program that can guess how much the ground might sink when they build a square tunnel by pushing pipes underground. This special program is better than old ways, helping them build safely.
Posibles conflictos de intereses
The authors acknowledge funding from several sources, including the National Natural Science Foundation of China, the Natural Science Foundation of Hunan Province, and the Science and Technology Innovation Project of Yiyang City. No direct conflicts related to specific companies or products were identified, but the funding sources could potentially influence research directions or interpretations.
Limitaciones identificadas
Explicación de la calificación
The study presents a novel approach to predicting surface settlement during rectangular pipe jacking tunnel construction, using an improved PSO-BP neural network model. The methodology is sound, and the results demonstrate improved accuracy compared to traditional methods. However, the limitations related to generalizability, oversimplification of factors, and limited comparison with other models prevent a higher rating. The funding sources raise a potential for indirect conflict of interest.
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