Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep learning technology

The aim of this study is to propose a new detection method for determining the damage locations in pile foundations based on deep learning using acoustic emission data.First,the damage location is simulated using a back propagation neural network deep learning model with an acoustic emission data set acquired from pile hit experiments.In particular,the damage location is identified using two parameters:the pile location(PL)and the distance from the pile cap(DS).This study investigates the influences of various acoustic emission parameters,numbers of sensors,sensor installation locations,and the time difference on the prediction accuracy of PL and DS.In addition,correlations between the damage location and acoustic emission parameters are investigated.Second,the damage step condition is determined using a classification model with an acoustic emission data set acquired from uniaxial compressive strength experiments.Finally,a new damage detection and evaluation method for pile foundations is proposed.This new method is capable of continuously detecting and evaluating the damage of pile foundations in service.

Damage localization under ambient vibration using changes in flexibility

In recent years,Structural Health Monitoring (SHM) has emerged as a new research area in civil engineering.Most existing health monitoring methodologies require direct measurement of input excitation for implementation.However,in many cases,there is no easy way to measure these inputs-or alternatively to externally excite the structure.Therefore,SHM methods based on ambient vibration have become important in civil engineering.In this paper,an approach is proposed based on the Damage Location Vector (DLV) method to handle the ambient vibration case.Here,this flexibility-matrix-based damage localization method is combined with a modal expansion technique to eliminate the need to measure the input excitation.As a by-product of this approach,in addition to determining the location of the damage,an estimate of the damage extent also can be determined.Finally,a numerical example analyzing a truss structure with limited sensors and noisy measurement is provided to verify the efficacy of the proposed approach.

Location of micro-cracks in plates using time reversed nonlinear Lamb waves

A promising tool to detect micro-cracks in plate-like structures is used for generating higher harmonic Lamb waves.In this paper,a method combining nonlinear S0 mode Lamb waves with time reversal to locate micro-cracks is presented and verified by numerical simulations.Two different models,the contact acoustic nonlinearity(CAN)model and the Preisach-Mayergoyz(PM)model,are used to simulate a localized damage in a thin plate.Pulse inversion method is employed to extract the second and fourth harmonics from the received signal.Time reversal is performed to compensate the dispersion of S0 mode Lamb waves.Consequently,the higher harmonics generated from the damaged area can be refocused on their source.By investigating the spatial distribution of harmonic wave packets,the location of micro-cracks will be revealed.The numerical simulations indicate that this method gives accurate locations of the damaged area in a plate.Furthermore,the PM model is proved to be a suitable model to simulate the micro-cracks in plates for generation of higher harmonics.