Pile driving is a widely used technique for the construction of buildings and infrastructure. A popular technique is to vibrate the pile into the sediment. However, since building sites are increasingly being located in metropolitan areas, there is a growing concern about the environmental impact that vibrations may cause during driving. Therefore, pile driving assessment and prediction have become important for several reasons, including reducing the risk of damage to nearby structures and limiting disturbance to adjacent properties. In particular, pile driveability (i.e., the penetration rate) assessment is immensely useful prior to installation as it increases the construction performance and subsequently reduces costs and environmental impact.

The important factors influencing the penetration rate include vibrator characteristics, pile properties, and soil conditions. However, due to assumptions and the lack of methods that accurately represent the complex phenomena at play during vibratory driving, a disparity is obtained between the predictions of modern pile behavior programs and the observed penetration rate.

Recently, the registration of pile driving data has increased significantly. This extended amount of measurement data can potentially be leveraged for an improvement of the prediction of the penetration rate in future projects. Literature review on the application of machine learning (ML) within pile driving, geotechnical engineering and drilling revealed that the artificial neural network (ANN) is a promising alternative method for the prediction of the driveability of vibratory driven piles (i.e., vibro-driveability).

In this work, machine learning methods and the traditional model were utilized to predict vibro-driveability. Promising ML techniques include the multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN). These neural networks were trained with the particle swarm optimization (PSO) algorithm. The backpropagation (BP) algorithm was also incorporated to train the MLPNN and RBFNN models as a conventional method. Based on results obtained with the aforementioned methods, we propose a new model, the Vibratory Driveability (VD) model, that combines the fruitful characteristics of the MLPNN and RBFNN.

The performance of the five different models was compared with the performance of contemporary vibro-driveability prediction software for three test sets. This was done using different performance indices including the mean squared error (MSE), mean absolute error (MAE) and the weighted average percentage error (WAPE). Additionally, the desired characteristics of the predictions based on the geo-engineer's input were examined and compared with the obtained predictions. It was demonstrated that the ANN-based methods achieved drastic improvements in prediction performance and consequently outperformed the traditional model, making ANN-based methods the preferred alternative for the prediction of vibro-driveability. Among the ANN models, the VD model produced the highest performance, as it reflected the desired prediction behavior for all three test cases and showed competitive prediction performance in terms of the performance metrics.

This work leads to the first-ever published research on the application of artificial neural networks for the prediction of vibro-driveability. As such, it could form the foundation for the development of new (vibratory) pile driving behavior assessment and prediction software. The development of these commercial applications could lead to a considerable reduction in costs and environmental impact.