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.

双向长期短期记忆（Bi-LSTM）网络是一种创新的计算范例，可学习时间步长和序列数据之间的双向长期依存关系，以预测未来的事件。这项研究提出了一个框架，该框架结合了Bi-LSTM和数据排序，可以实时预测软土中旋喷桩的直径。使用喷射注浆处理软土的案例研究对模型进行了测试。结果表明，所提出的策略可以有效地预测圆柱直径随深度的变化。还进行了Bi-LSTM，原始长期短期记忆（LSTM）和支持向量回归（SVR）方法之间的比较性能分析。Bi-LSTM的均方根误差比LSTM和SVR都好。该结果证实了使用Bi-LSTM进行连续逐步注浆过程建模的有效性。根据分析结果，提出了一些改进当前注浆塔设计的建议。