A Method for the Damage Detection of Pile Foundation in High-Pile Wharf Based on A Curvature Mode Deletion Model

As the top of the pile foundation in high-pile wharf is connected to the superstructure and most of the pile bodies are located below the water surface, traditional damage detection methods are greatly limited in their application to pile foundation in service. In the present study, a new method for pile foundation damage detection is developed based on the curve shape of the curvature mode difference(CMD) before and after damage. In the method, the influence at each node on the overall CMD curve shape is analyzed through a data deletion model, statistical characteristic indexes are established to reflect the difference between damaged and undamaged units, and structural damage is accurately detected. The effectiveness and robustness of the method are verified by a finite element model(FEM) of high-pile wharf under different damage conditions and different intensities of Gaussian white noise. The applicability of the method is then experimentally validated by a physical model of high-pile wharf. Both the FEM and the experimental results show that the method is capable of detecting pile foundation damage in noisy curvature mode and has strong application potential.

Damage assessment and diagnosis of hydraulic concrete structures using optimization-based machine learning technology

Concrete is widely used in various large construction projects owing to its high durability,compressive strength,and plasticity.However,the tensile strength of concrete is low,and concrete cracks easily.Changes in the concrete structure will result in changes in parameters such as the frequency mode and curvature mode,which allows one to effectively locate and evaluate structural damages.In this study,the characteristics of the curvature modes in concrete structures are analyzed and a method to obtain the curvature modes based on the strain and displacement modes is proposed.Subsequently,various indices for the damage diagnosis of concrete structures based on the curvature mode are introduced.A damage assessment method for concrete structures is established using an artificial bee colony backpropagation neural network algorithm.The proposed damage assessment method for dam concrete structures comprises various modal parameters,such as curvature and frequency.The feasibility and accuracy of the model are evaluated based on a case study of a concrete gravity dam.The results show that the damage assessment model can accurately evaluate the damage degree of concrete structures with a maximum error of less than 2%,which is within the required accuracy range of damage identification and assessment for most concrete structures.