Dynamic prediction of jet grouted column diameter in soft soil using Bi-LSTM deep learning

Abstract

The bidirectional long short-term memory (Bi-LSTM) network is an innovative computation paradigm that learns bidirectional long-term dependencies between time steps and sequence data to predict future occurrences. This study proposes a framework to incorporate Bi-LSTM and data sequencing to predict diameter of jet grouted columns in soft soil in real time. The models are tested using a case study of jet grouting treatment of soft soil. The results show that the proposed strategies can efficiently predict the variation in column diameter with the depth. A comparative performance analysis among the Bi-LSTM, original long short-term memory (LSTM) and support vector regression (SVR) approaches is also conducted. The Bi-LSTM performs better than both the LSTM and SVR in root-mean-square error. This result substantiates the efficacy of modeling sequential step-by-step jet grouting process using the Bi-LSTM. Based on the analyzed results, some recommendations for improving the current design of jet grout columns are proposed.