Quality monitoring in vibro ground improvement – A hybrid machine learning approach

Abstract

AbstractThis article presents a new approach of quality control to vibro ground improvement techniques based on hybrid machine learning (ML), i.e., a combination of classical analysis and ML techniques. The process is monitored with an instrumented rig equipped with multiple sensors. Key performance indicators (KPIs) are used to identify anomalous foundation columns. As the foundation columns are sub‐surface, there is no direct access to ground truth; consequently, unsupervised ML is applied to the recorded time‐series data. The risk of not detecting defective elements is reduced by the combination of two independent methods for anomaly detection, KPI‐ and ML‐based classification. The ML is used to gain a deeper process understanding and to detect anomalies which were not considered in the design phase of the KPI. New pre‐processing techniques were derived from the insights gained from the ML classifier; this led to a more robust classifier. It is shown how unsupervised ML, using a multi‐channel variational autoencoder (VAE) with long short‐term memory (LSTM) layers, can be utilized in a knowledge discovery process (KDP).