Performance of Machine Learning Algorithms for Predicting Jet Grout Column Diameter
Publication: Geo-Congress 2024
ABSTRACT
Machine learning methods have been increasingly adopted to predict the diameter of jet grout columns. However, a comparative study on the accuracy and reliability of various machine learning models has not been performed. This paper presents a comparison of the performance of the various machine learning models including K-nearest neighbors (KNN), artificial neural networks (ANN), support vector machine (SVM), and long short-term memory networks (LSTM) for the prediction of jet grout diameter. A database of published case studies consisting of soil type, properties of jet grout, and diameter was used to train the machine learning models for the prediction of the diameter of the jet grout column. The performance of the optimized machine learning algorithms was evaluated and compared. The ANN model was found to provide the best performance. K-fold validation (i.e., leave-one-out cross-validation) was performed for the ANN algorithm, and an ensemble model was developed for the prediction of diameter of the jet grout column.
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Information & Authors
Information
Published In
Geo-Congress 2024
Pages: 485 - 494
History
Published online: Feb 22, 2024
ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Case studies
- Comparative studies
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction methods
- Engineering fundamentals
- Geomechanics
- Geotechnical engineering
- Grouting
- Jet grouting
- Mathematics
- Methodology (by type)
- Model accuracy
- Models (by type)
- Neural networks
- Research methods (by type)
- Soil mechanics
- Soil properties
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