Pile foundations are typically employed when top-soil layers are unstable and incapable of bearing super-structural pressures. Accurately modeling pile behavior is crucial for ensuring optimal structural and serviceability performance. However, traditional methods such as pregnancy testing, while highly accurate, are expensive and time-consuming. Consequently, various approaches have been developed to predict load settlement behavior, including using artificial neural networks (ANNs). ANNs offer the advantage of accurately replicating substrate behavior's nonlinear and intricate relationship without requiring prior formulation.This research aims to employ artificial neural network (ANN) modeling techniques to simulate the load-settlement relationship of drilled piles. The primary aims of this study are threefold: firstly, to assess the effectiveness of the generated ANN model by comparing its results with experimental pile load test data; secondly, to establish a validation method for ANN models; and thirdly, to conduct a sensitivity analysis to identify the significant input factors that influence the model outputs. In addition, this study undertakes a comprehensive review of prior research on using artificial neural networks for predicting pile behavior. Evaluating efficiency measurement indicators demonstrates exceptional performance, particularly concerning the agreement between the predicted and measured pile settlement. The correlation coefficient (R) and coefficient of determination (R^2) indicate a strong correlation between the predicted and measured values, with values of 0.965 and 0.938, respectively. The root mean squared error (RMSE) is 0.051, indicating a small deviation between the predicted and actual values. The mean percentage error (MPE) is 11%, and the mean absolute percentage error (MAPE) is 21.83%.
Water treatment sludge (WTS) is a byproduct generated during the treatment of wastewater. In recent years, researchers have explored the potential of using WTS as a soil stabilizer to improve the geotechnical properties of soils. In this review, we will examine the current state of knowledge on the use of WTS for this purpose. The organic matter content of WTS is usually high and can range from 30% to 60%. The high organic matter content makes WTS a potential source of nutrients for plants, and it can also enhance soil structure and water retention. Another important consideration is the environmental impact of using WTS. The use of WTS can be an eco-friendly alternative to chemical stabilizers, which can have adverse effects on the environment. However, there are concerns about the potential for heavy metal contamination in WTS. To mitigate this risk, it is recommended to conduct thorough testing of WTS before using it as a soil stabilizer. Finally, the use of WTS as a soil stabilizer has the potential to improve the geotechnical properties of soils. However, it is essential to consider factors such as the type and dosage of WTS, the soil type, and the environmental impact before using it. Further research is also needed to explore the potential of using WTS in different soil types and environmental conditions.