Settlement prediction for deep foundation piles using artificial neural networks Arshiya ABADKON1*, Muhammed Ernur AKINER1 1Bogazici University, Civil Engineering Department Bebek, Istanbul, Turkey

Presentation Outline I. Introduction II. Objective III. Background of theoretical approaches for prediction of pile settlement IV. Experimental verification of pile settlement IV. Artificial neural network approach for the prediction of pile settlement V. conclusions 2

Introduction Introduction Soil conditions are inherently not suitable to support footings due to large foundation loads in tall buildings Hence, pile foundations are necessary in case of an insufficient bearing capacity or excessive settlement 3

Bored piles

Driven piles

Concrete piles are special types of deep foundations used to transmit loads to deeper strata capable of supporting the applied loads It is natural to see settlements of deep foundations under large design loads. Hence: Accurate prediction of pile settlement is necessary to ensure appropriate structural performance

Typical Methods for Settlement Prediction of Concrete Piles 7 Theoretical Methods Empirical Methods

Objective To apply neural network (ANNs) methodology for this geotechnical problem To develop a non-linear ANNs model for predicting the settlement accurately in deep foundations such as concrete piles To overcome the limitations of the traditional methods 8

Presentation Outline I. Introduction II. Objective III. Background of theoretical approaches for prediction of pile settlement IV. Experimental verification of pile settlement IV. Artificial neural network approach for the prediction of pile settlement V. conclusions 9

The following empirical equation has been used for calculating the tip settlement of the pile: The following empirical equation has been used for calculating the tip settlement of the pile: 10 Settlement of the tip of the pile Bearing pressure at the tip of the pile Diameter of tip of the pile Shape factor Poison ratio Modulus of elasticity of the soil

Mathematical modelling of the pile and soil parameters provides various formulas and theories for ultimate bearing capacity and settlement calculations of a pile which are used in pile design. However these analyses rarely give reliable results because real site conditions can affect the vertical load capacity of the pile Therefore pile load tests play an important role in the measuring of the load bearing capacity and displacement behaviour of piles 11

Difficulties with Pile Load Testing 12 Costly Time Consuming

Presentation Outline I. Introduction II. Objective III. Background of theoretical approaches for prediction of pile settlement IV. Experimental verification of pile settlement IV. Artificial neural network approach for the prediction of pile settlement V. conclusions 13

Load Testing of Piles 14 The most common types of loading tests: Constant rate of penetration (CRP) test Maintained load test (MLT) Osterberg Test

CRP Test 15

CRP Test 16

Osterberg Test 17

Osterberg Cell Installation

Presentation Outline I. Introduction II. Objective III. Background of theoretical approaches for prediction of pile settlement IV. Experimental verification of pile settlement IV. Artificial neural network approach for the prediction of pile settlement V. conclusions 19

Artificial neural network approach for the prediction of pile settlement 20 ANN architecture of the settlement prediction model (Hidden and output layer activation functions are both sigmoid functions).

Settlement prediction using the empirical formula

Settlement prediction using the ANN model

Comparison of observed settlement and predicted settlement values of empirical and ANN approaches

Presentation Outline I. Introduction II. Objective III. Background of theoretical approaches for prediction of pile settlement IV. Experimental verification of pile settlement IV. Artificial neural network approach for the prediction of pile settlement V. conclusions 24

25 Two distinct approaches were used in this research in order to predict pile settlement as close as possible to their measured values. First approach was to use empirical equations which were given in the literature for estimation of pile settlement. Second approach was to use ANN model When the results are investigated, it is clear that proposed ANN models give more reliable results for settlement when compared with the results from empirical models

26 So Artificial neural network can be used in big construction projects to save a huge amount of budget which is spent for pile load tests. In addition we can save time by using ANN approach in our engineering projects.

Thank you! 27