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In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1,000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.

An independent field performance evaluation for a secondary stormwater filtration device, named the Ecosol Strom Pit (Class 2), was performed between May 2017 and July 2019 in an urban catchment in Queensland, Australia. During the testing period, a total of 37 rainfall events were recorded, of which between 15 and 21 events were evaluated as qualifying for the purposes of characterizing the removal efficiency performance of the device. A statistical analysis of the event mean concentrations (EMCs) of the flow streams through the device indicate a statistically significant difference between the influent and effluent streams. A variety of pollutant removal evaluation metrics, including concentration-based and total load-based metrics, were utilized in this study to characterise the efficacy of the device. Two new approaches are proposed for facilitation the analysis: a nonlinear regression approach to more effectively deal with nonlinear patterns in the influent and effluent data and the regression of concentration (ROC), which is an added concentration-based metrics. In summary, the removal efficiencies of the Ecosol Storm Pit (Class 2) were evaluated to be 72–74% for total suspended solids (TSS), 45–50% for total phosphorus (TP), 41–45% for total nitrogen (TN), 27–32% for total heavy metals (THM), 79–85% for total petroleum hydrocarbons (TPH), and 80–88% for total recoverable hydrocarbons (TRH).