On the Use of Neuro-Swarm System to Forecast the Pile Settlement

Danial Jahed Armaghani,Panagiotis G Asteris,Van Van Huynh,Seyed Alireza Fatemi,Reza Tarinejad,Ahmad Safuan A Rashid,Mahdi Hasanipanah

doi:10.3390/app10061904

Abstract

In civil engineering applications, piles (deep foundations) are pushed into the ground in order to perform as steady support of structures. As these type of foundations are able to carry a huge amount of load, they should be carefully designed in terms of their settlement. Therefore, the control and estimation of settlement is a significant issue in pilling design and construction. The objective of the present study is to introduce a modeling process of a hybrid intelligence system namely neural network optimized by particle swarm optimization (neuro-swarm) for estimation of pile settlement. To do that, properties results of several piles socketed into rock mass together with their settlements were considered as established databased to propose neuro-swarm model. Then, several sensitivity analyses were carried out to determine the most influential particle swarm optimization parameters for pile settlement prediction. Eventually, five neuro-swarm models were constructed to understand the behavior of this hybrid model on them in pile settlement prediction. As a result, according to results of five performance indices, dataset number 4 showed the highest prediction capacity among all five datasets. The coefficient of determination (R2) and system error values of (0.851 and 0.079) and (0.892 and 0.099) were obtained respectively for train and test stages of the best neuro-swarm model which reveal the capability level of this hybrid model in predicting pile settlement. The modeling process introduced in this study can be useful for the researchers who are interested to work on the same hybrid technique.

Highlights

Structural loads are carried by rock-socketed piles via their end bearing, or their shaft resistance, or a mixture of both
During artificial neural network (ANN) model development, it was found that a model with three hidden neurons provides the best performance prediction for estimation of pile settlement
Ls /Lr, Lp /D, uniaxial compressive strength (UCS), standard penetration test (SPT) N-value and Qu were set as inputs to forecast Sp

Summary

Introduction

Structural loads are carried by rock-socketed piles via their end bearing, or their shaft resistance, or a mixture of both. Ng et al [2] mentioned that there is a pressing need to provide rock-socketed 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. Aims to present a novel intelligent model for forecasting the settlement of rock-socketed piles featuring the uppermost importance in pile designing. A popular intelligence system namely the artificial neural network (ANN) was designed and introduced in study by Pooya

Objectives

Methods

Results

Conclusion