# On the Use of Neuro-Swarm System to Forecast the Pile Settlement
## 2 Methods and Material
### Case Study and Data Source  
The Klang Valley Mass Rapid Transit (KVMRT) project which was constructed and operated in Kuala Lumpur, Malaysia, was aimed to reduce the traffic crowding in this big city. During site investigation phase of this project, for supporting purposes of the KVMRT project, it was decided to construct many of large-diameter bored piles. Part of the above piles is implemented on different kinds of rocks such as granite and limestone. Our research focuses on outcomes of 96 rock-socketed piles (built in granite rock type). Granitic rock of Triassic era, dominating the undulant terrain, was noticed in the research zone. Subsurface data and materials were accomplished in the pile locations to examine the predominant geological situations. Based on our observations, underlying subsoil contours comprised remaining soils of various rocks (typically granite). In this project, bedrock depth was attained in a range of (0.7-14 m) below the ground level. The overload soil generally contains gravely silt or sandy or silty sand or gravely. Nonetheless, the phrases below summarize borelog-related data and field surveys; (a) a range of slightly weathered rock mass to highly weathered rock mass was observed during the investigations, (b) the minimum and maximum UCS values were obtained as 25 MPa and 68 MPa, respectively (the tests were carried out based on ISRM [62]), (c) till the depth of 16.5 m, a highly weathered residual soil was observed and the SPT N-values of the main soil (hard sandy silt) were obtained in a range of (4-167) blows/300 mm.  
The first step for conducting a predictive model is to prepare an appropriate database. Indeed, identification of parameters with the highest influence on model output is considered as a precondition for a predictive intelligence system. As noted above, the results of 96 pile driving analyzer (PDA) tests conducted on rock-socketed piles were taken into consideration for developing hybrid model. The tests were carried out with the help of pile driving analyzer equipment manufactured by Pile Dynamic, Inc. (Cleveland, OH, USA). According to previous investigations, piling geometry factors  
Figure 2: The learning procedure of neuro-swarm model.  
play a very important role on pile settlement. In this study, relevant parameters i.e., \(L_{s}\)/\(L_{r}\) (length of pile in the soil layer/length of pile in the rock layer) and \(L_{p}\)/\(D\) (total length of pile/pile diameter), were selected to be considered as inputs. Additionally, UCS parameter was also selected as a model input because of its importance in the part of pile socketed into the rock. Another input parameter taken into consideration was SPT N-value for representing the situation of soil layer. There is no need to point out that the pile load affects the pile settlement; hence, pile ultimate bearing capacity (\(Q_{u}\)) was fixed as another input to predict the pile settlement (\(S_{p}\)). Ranges of (3.9-31.6), (0.23-32.7), (3.7-166.8), (25-68 MPa), (12,300-43,000 kN) and (5-20 mm), were obtained for \(L_{p}\)/\(D\), \(L_{s}\)/\(L_{r}\), SPT N-value, UCS, \(Q_{u}\) and \(S_{p}\), respectively. To have a better understanding on the established database, input and output parameters for all 96 datasets are shown in Figure 3. In the next section, approaching the implement of neuro-swarm system in predicting pile settlement will be described.