# On the Use of Neuro-Swarm System to Forecast the Pile Settlement
## 1 Introduction  
Structural loads are carried by rock-socketed piles via their end bearing, or their shaft resistance, or a mixture of both. As stated by [PERSON] [1], the application of drilled piles socketed into rock is among the best alternatives when bedrock is overlain by layers of loose soil at shallow depths. In suchcircumstances, shaft resistance in rock can ensure considerable bearing capacity, even with small pile dislocations. [PERSON] et al. [2] mentioned that there is a pressing need to provide rock-socked piles for enhancing the efficacy of pile designing and greater design loads. The design of such structures is performed through experiential or analytical approaches [3]. The latter has received substantial interest, and finite-element codes are currently accessible. The former is normally established upon the outcomes of full-scale analysis of piles. As stated by [PERSON] [1], the two procedures have equal suitability in the design of rock-socked piles. Even so, nearly all the accessible methods do not predict accurately due to the complicated action of piles. Our research, therefore, aims to present a novel intelligent model for forecasting the settlement of rock-socked piles featuring the uppermost importance in pile designing. [PERSON] [4] presented evidence in the field of predicting pile capacity and the findings were merely appropriate for input information. Thus, the succeeding section reviews the literature in order to identify the most appreciate model inputs for pile settlement estimation. Based on a research carried out by [PERSON] and [PERSON] [5], settlement of the pile is dependent upon pile geometry parameters (e.g., diameter, area and length), shear modulus of soil and piling load. Regarding rock-socked piles, numerous investigators [6; 7], have shown that the uniaxial compressive strength (UCS) of the rocks has a direct relationship with the pile bearing capacity and its settlement. A popular intelligence system namely the artificial neural network (ANN) was designed and introduced in study by [PERSON] et al. [8] to predict piling settlement using the standard penetration test data. They utilized nearly 1000 data, collected from previous studies, to develop the ANN models and concluded that the ANN is a powerful technique for solving problem related to pile settlement. In another intelligence study of foundation settlement, [PERSON] and [PERSON] [9] introduced an ANN model to estimate the settlement of spread foundations using soil properties, the foundation geometry parameters and reinforcement specifications. In a similar study, [PERSON] et al. [10] successfully demonstrated the applicability of ANN for estimation of the spread foundations settlement. According to their results, the most effective parameters on the spread foundations settlement were standard penetration test (SPT) blow count (N), foundation embedded ratio and foundation geometry (like width of foundation). [PERSON] et al. [11] employed an ANN model containing three layers in order to examine the driven piles bearing capacity with the help of the input parameters i.e., the elastic compression of soil and pile, pile setup and generating energy used in a pile. [PERSON] [12] attempted to propose a regression ANN capable of estimating a driven pile capacity within non-cohesive soil. He set four parameters as model inputs to his model, namely the soil friction angle, effective overburden pressure, duration of pile and the cross-sectional region of the pile. [PERSON] et al. [13] made use of the group method of data handling (GMDH) model optimized by genetic algorithm (GA). A gene expression programming (GEP) equation has been proposed in another relevant research by [PERSON] and [PERSON] [14] to estimate the axial capacity of driven piles. Their model utilized a number of independent variables such as the length and size of piles, weighted sleeve friction along the pile shaft, pile materials and pile elastic modulus. A GEP equation was developed by [PERSON] et al. [3] to approximate settlement of the rock-socked piles.