# On the Use of Neuro-Swarm System to Forecast the Pile Settlement
## 1 Introduction
[PERSON] et al. [3] successfully showed that their GEP model is able to perform better than statistical model. Some other techniques e.g., particle swarm optimization (PSO)-ANN, GA-ANN, imperialism competitive algorithm (ICA)-ANN, adoptive neuro-fuzzy inference system-GMDH-PSO and genetic programming were introduced for pile bearing capacity prediction, in the studies carried out by [PERSON] et al. [7], [PERSON] et al. [15], [PERSON] and [PERSON] [16], [PERSON] et al. [17] and [PERSON] et al. [18], respectively. As reported in the literature, piling loads, UCS of the rock, piling geometry parameters, pile length in different layers (i.e., soil and rock) and SPT-N value are model inputs of reasonable importance to predict the settlement of rock-socked piles. Furthermore, several studies [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31], have reported the successful application of artificial intelligent (AI) and machine learning (ML) techniques such as ANNs to approximate and consequently solve piling problems as well as civil engineering problems. Despite using ANNs in literatures in order to resolving various problems in solving science and engineering problems, some investigations (e.g., [32; 33; 34; 35]) confirmed that ANN has several inherent limitations such as lagging in learning rate and getting trapped in local minima. Therefore, using optimization algorithms (OAs) such as ICA and PSO show significant gain in order to dominate these restrictions and allow promotion of models predicted by adjusting bias and weight of ANNs. Recently, considerable capacity of these algorithms drew more attentions and researchers tried to combine them in order to optimize ANN models [26; 36; 37; 38]. In piling technology, several studies have been conducted to solve the problems by developing PSO-ANN models. For example, [PERSON] et al. [7] conducted a study of prediction of pile bearing capacity using a combination of PSO and ANN models. They predicted pile bearing capacity through the use of the most important parameters on the pile capacity and showed that the prediction performance of the PSO-ANN model is higher than the performance prediction of ANN model. A research on the load-deformation behavior of axially loaded piles was conducted by [PERSON] et al. [39]. They developed a PSO-ANN technique to predict the behavior of single piles embedded in soil and subjected to an axial load. Pile load, pile diameter, pile modulus, soil stiffness and pile length were used as inputs whereas pile load-deformation was considered as system output. In another study, the PSO was used to optimize ANN weights and biases in predicting lateral deflection of piles by [PERSON] et al. [40]. They successfully indicated that their developed hybrid model is able to provide a high level of accuracy. Apart from the examples of PSO-ANN implementation studies, PSO has been used for optimization purposes in many geotechnical applications (e.g., [41; 42; 43]). In this study, the development of hybrid ANN-based model namely PSO-ANN (or neuro-swarm) with detailed modeling process is presented herein for estimating the settlement of rock-socketed piles. For comparison purposes, a pre-developed ANN model is also developed to predict the settlement of piles. Then, the performance prediction of these techniques is evaluated and the best model among them is selected. The rest of this study is organized in the following way. In Section 2, the background of ANN, PSO and PSO-ANN models is described. Then, after giving some descriptions about database and study area, development process and the used models will be given in detail. Eventually, their prediction capacities are evaluated to introduce the best predictive model for estimation of the pile settlement.