Frontiers of Structural and Civil Engineering
>> 2021,
Volume 15,
Issue 2
doi:
10.1007/s11709-021-0715-y
RESEARCH ARTICLE
Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep learning technology
. School of Resource & Civil Engineering, Northeastern University, Shenyang 110819, China.. Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Korea.. School of Mechanical Engineering, Pusan National University, Busan 46241, Korea.. Korea Institute of Geoscience and Mineral Resource (KIGAM), Daejeon 34132, Korea
Received: 2021-03-29
Accepted: 2021-04-27
Available online: 2021-04-27
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Abstract
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 ( ) and the distance from the pile cap ( ). 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 and . 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.
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