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Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep learning technology
Frontiers of Structural and Civil Engineering ( IF 3 ) Pub Date : 2021-04-28 , DOI: 10.1007/s11709-021-0715-y
Alipujiang Jierula , Tae-Min Oh , Shuhong Wang , Joon-Hyun Lee , Hyunwoo Kim , Jong-Won Lee

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.



中文翻译:

借助深度学习技术使用声发射检测桩基中的损坏位置和损坏步骤

本研究的目的是提出一种基于声发射数据的深度学习来确定桩基中损伤位置的新检测方法。首先,使用反向传播神经网络深度学习模型模拟损坏位置,该模型具有从打桩实验中获取的声发射数据集。特别地,使用两个参数来识别损坏位置:桩位置(P L)和距桩帽的距离(D S)。这项研究调查了各种声发射参数,传感器数量,传感器安装位置以及时间差对P LD S预测精度的影响。。另外,研究了损伤位置与声发射参数之间的相关性。其次,使用具有从单轴抗压强度实验获得的声发射数据集的分类模型来确定损伤阶跃条件。最后,提出了一种新的桩基损伤检测与评估方法。这种新方法能够连续检测和评估使用中的桩基础的损坏。

更新日期:2021-04-29
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