Recurrent neural network long short term memory model to detect the pile toe using raw data of pile integrity test.

This article proposes a novel approach to automatically generate velocity reflectogram of Pile Integrity Testing using a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) model. Conventional Low-Strain Integrity Testing (LSIT) accuracy relies significantly on expert interpretation of reflected wave signals and entails subjectivity as well as efficiency limitations. The purpose of this study is to develop an artificial intelligence system capable of learning wave propagation behavior from acceleration inputs and generating reflectogram that capture pile toe locations correctly, thereby reducing dependence on human experience. The proposed technique eliminates human error and increases both the reliability and efficiency of the model. The strategy involved the collection of LSIT data from several of Egypt's driven piles projects, followed by systematic preprocessing which converted raw acceleration signals into digitized velocity-time series. Several RNN-LSTM networks with various hidden layers and neurons were trained and optimized against performance including measures the coefficient of determination (R