Predicting the Displacement of Single Battered Pile in Sandy Soil under Pullout Loading Using Artificial Neural Network
Article Sidebar
Main Article Content
Abstract
The displacement of battered piles is one of the most critical parameters in the design of a pile foundation. In this study, an Artificial Neural Network (ANN) algorithm was utilized to predict the displacement of piles in sandy soils subjected to pullout loading. A finite element analysis (FEA) in three dimensions, performed with the PLAXIS 3D program, was utilized to gather 2380 databases, including the length/ diameter of pile, pullout, batter angle, Poisson ratio, friction angle, dilatancy angle, relative density, and Young’s modulus as input variables, whereas the displacement of battered piles was considered an output variable. The dataset was divided into three parts: training (80%), validation (10%), and testing (10%). The performance of the Artificial Neural Network (ANN) algorithm was evaluated using the Mean Squared Error (MSE) and the Coefficient of Determination (R^2). This study applied a procedure known as a backpropagation neural network. According to the analysis of relative significance, the pullout load (Pu) and the pile length to its diameter (L/D) were the most effective characteristics among the other inputs. The R-values of the ANN model for the displacement of the battered piles dataset were 0.99 across all three phases of testing, validation, and training. The findings substantiated the viability of employing Artificial Neural Networks as a successful method for obtaining the displacement values of a single battered pile in sandy soil when subjected to pullout loading.
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
THIS IS AN OPEN ACCESS ARTICLE UNDER THE CC BY LICENSE http://creativecommons.org/licenses/by/4.0/
Plaudit
References
Shooshpasha I, Hasanzadeh A, Taghavi A. Prediction of the Axial Bearing Capacity of Piles by SPT-Based and Numerical Design Methods. Geomate Journal 2013; 4(8):560-564. DOI: https://doi.org/10.21660/2013.8.2118
Lopes F, Laprovitera H. On the Prediction of the Bearing Capacity of Bored Piles from Dynamic Penetration Tests. International Geotechnical Seminar on Deep Foundations on Bored and Auger Piles 1988; 1:537-540.
Decourt L. Prediction of Load Settlement Relationships for Foundations on the Basis of the SPT-T. CICLO de Conferencias Internacionle "Leonardo Zeevaert" 1995:85-104.
Architectural Institute of Japan. Recommendations for Design of Building Foundation. Tokyo: Architectural Institute of Japan; 2004.
Asteris PG, Ashrafian A, Rezaie BM. Prediction of the Compressive Strength of Self-Compacting Concrete Using Surrogate Models. Computers and Concrete 2019; 24(2):137-150.
Asteris PG, Moropoulou A, Skentou AD, Apostolopoulou M, Mohebkhah A, Cavaleri L, Rodrigues H, Varum H. Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects. Applied Sciences 2019; 9(2):243. DOI: https://doi.org/10.3390/app9020243
Hajihassani M, Abdullah SS, Asteris PG, Armaghani DJ. A Gene Expression Programming Model for Predicting Tunnel Convergence. Applied Sciences 2019; 9(21):4650. DOI: https://doi.org/10.3390/app9214650
Huang L, Asteris PG, Koopialipoor M, Armaghani DJ, Tahir M. Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength. Applied Sciences 2019; 9(24):5372. DOI: https://doi.org/10.3390/app9245372
Le LM, Ly HB, Pham BT, Le VM, Pham TA, Nguyen DH, Tran XT, Le TT. Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns under Axial Compression. Materials 2019; 12(10):1670. DOI: https://doi.org/10.3390/ma12101670
Najemalden AM, Ibrahim SW, Ahmed MD. Prediction of Collapse Potential for Gypseous Sandy Soil Using ANN Technique. Journal of Engineering Science and Technology 2020; 15(2):1236-1253.
Ly HB, Pham BT, Dao DV, Le VM, Le LM, Le TT. Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete. Applied Sciences 2019; 9(18):3841. DOI: https://doi.org/10.3390/app9183841
Ly HB, Le LM, Duong HT, Nguyen TC, Pham TA, Le TT, Le VM, Nguyen NL, Pham BT. Hybrid Artificial Intelligence Approaches for Predicting Critical Buckling Load of Structural Members under Compression Considering the Influence of Initial Geometric Imperfections. Applied Sciences 2019; 9(11):2258. DOI: https://doi.org/10.3390/app9112258
Ly HB, Le TT, Le LM, Tran VQ, Le VM, Vu HL, Nguyen QH, Pham BT. Development of Hybrid Machine Learning Models for Predicting the Critical Buckling Load of I-Shaped Cellular Beams. Applied Sciences 2019; 9(24):5458. DOI: https://doi.org/10.3390/app9245458
Ly HB, Le LM, Phi LV, Phan VH, Tran VQ, Pham BT, Le TT, Derrible S. Development of an AI Model to Measure Traffic Air Pollution from Multisensor and Weather Data. Sensors 2019; 19(22):4941. DOI: https://doi.org/10.3390/s19224941
Xu H, Zhou J, Asteris PG, Jahed AD, Tahir MM. Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate. Applied Sciences 2019; 9(18):3715. DOI: https://doi.org/10.3390/app9183715
Dao DV, Trinh SH, Ly HB, Pham BT. Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches. Applied Sciences 2019; 9(6):1113. DOI: https://doi.org/10.3390/app9061113
Qi C, Ly HB, Chen Q, Le TT, Le VM, Pham BT. Flocculation-Dewatering Prediction of Fine Mineral Tailings Using a Hybrid Machine Learning Approach. Chemosphere 2020; 244:125450. DOI: https://doi.org/10.1016/j.chemosphere.2019.125450
Chen H, Asteris PG, Jahed AD, Gordan B, Pham BT. Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models. Applied Sciences 2019; 9(6):1042. DOI: https://doi.org/10.3390/app9061042
Nguyen HL, Le TH, Pham CT, Le TT, Ho LS, Le VM, Pham BT, Ly HB. Development of Hybrid Artificial Intelligence Approaches and a Support Vector Machine Algorithm for Predicting the Marshall Parameters of Stone Matrix Asphalt. Applied Sciences 2019; 9(15):3172. DOI: https://doi.org/10.3390/app9153172
Nguyen HL, Pham BT, Son LH, Thang NT, Ly HB, Le TT, Ho LS, Le TH, Tien BD. Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction. Applied Sciences 2019; 9(21):4715. DOI: https://doi.org/10.3390/app9214715
Ly HB, Monteiro E, Le TT, Le VM, Dal M, Regnier G, Pham BT. Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees. Materials 2019; 12(9):1544. DOI: https://doi.org/10.3390/ma12091544
Pham BT, Nguyen MD, Dao DV, Prakash I, Ly HB, Le TT, Ho LS, Nguyen KT, Ngo TQ, Hoang V. Development of Artificial Intelligence Models for the Prediction of Compression Coefficient of Soil: An Application of Monte Carlo Sensitivity Analysis. Science of The Total Environment 2019; 679:172-184. DOI: https://doi.org/10.1016/j.scitotenv.2019.05.061
Asteris PG, Nozhati S, Nikoo M, Cavaleri L, Nikoo M. Krill Herd Algorithm-Based Neural Network in Structural Seismic Reliability Evaluation. Mechanics of Advanced Materials and Structures 2019; 26(13):1146-1153. DOI: https://doi.org/10.1080/15376494.2018.1430874
Asteris PG, Armaghani DJ, Hatzigeorgiou GD, Karayannis CG, Pilakoutas K. Predicting the Shear Strength of Reinforced Concrete Beams Using Artificial Neural Networks. Computers and Concrete 2019; 24(5):469-488.
Asteris PG, Apostolopoulou M, Skentou AD, Moropoulou A. Application of Artificial Neural Networks for the Prediction of the Compressive Strength of Cement-Based Mortars. Computers and Concrete 2019; 24(4):329-345.
Asteris PG, Kolovos KG. Self-Compacting Concrete Strength Prediction Using Surrogate Models. Neural Computing and Applications 2019; 31(Suppl 1):409-424. DOI: https://doi.org/10.1007/s00521-017-3007-7
Asteris PG, Mokos VG. Concrete Compressive Strength Using Artificial Neural Networks. Neural Computing and Applications 2020; 32(15):11807-11826. DOI: https://doi.org/10.1007/s00521-019-04663-2
Dao DV, Ly HB, Trinh SH, Le TT, Pham BT. Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete. Materials 2019; 12(6):983. DOI: https://doi.org/10.3390/ma12060983
Kumar BM, Kumar K, Bharathi A. Improved Soil Data Prediction Model Base Bioinspired K-Nearest Neighbor Techniques for Spatial Data Analysis in Coimbatore Region. International Journal of Future Revolution in Computer Science & Communication Engineering 2017; 3(1):345-349.
Shahin MA, Jaksa M. Neural Network Prediction of Pullout Capacity of Marquee Ground Anchors. Computers and Geotechnics 2005; 32(3):153-163. DOI: https://doi.org/10.1016/j.compgeo.2005.02.003
Shahin MA. Load-Settlement Modeling of Axially Loaded Steel Driven Piles Using CPT-Based Recurrent Neural Networks. Soils and Foundations 2014; 54(3):515-522. DOI: https://doi.org/10.1016/j.sandf.2014.04.015
Shahin MA. State-of-the-Art Review of Some Artificial Intelligence Applications in Pile Foundations. Geoscience Frontiers 2016; 7(1):33-44. DOI: https://doi.org/10.1016/j.gsf.2014.10.002
Shahin MA. Intelligent Computing for Modeling Axial Capacity of Pile Foundations. Canadian Geotechnical Journal 2010; 47(2):230-243. DOI: https://doi.org/10.1139/T09-094
Goh AT. Back-Propagation Neural Networks for Modeling Complex Systems. Artificial Intelligence in Engineering 1995; 9(3):143-151. DOI: https://doi.org/10.1016/0954-1810(94)00011-S
Goh AT, Kulhawy FH, Chua C. Bayesian Neural Network Analysis of Undrained Side Resistance of Drilled Shafts. Journal of Geotechnical and Geoenvironmental Engineering 2005; 131(1):84-93. DOI: https://doi.org/10.1061/(ASCE)1090-0241(2005)131:1(84)
Nejad FP, Jaksa MB, Kakhi M, McCabe BA. Prediction of Pile Settlement Using Artificial Neural Networks Based on Standard Penetration Test Data. Computers and Geotechnics 2009; 36(7):1125-1133. DOI: https://doi.org/10.1016/j.compgeo.2009.04.003
Momeni E, Nazir R, Armaghani DJ, Maizir H. Application of Artificial Neural Network for Predicting Shaft and Tip Resistances of Concrete Piles. Earth Sciences Research Journal 2015; 19(1):85-93. DOI: https://doi.org/10.15446/esrj.v19n1.38712
Nawari N, Liang R, Nusairat J. Artificial Intelligence Techniques for the Design and Analysis of Deep Foundations. Electronic Journal of Geotechnical Engineering 1999; 4(2):1-21.
Teh C, Wong K, Goh A, Jaritngam S. Prediction of Pile Capacity Using Neural Networks. Journal of Computing in Civil Engineering 1997; 11(2):129-138. DOI: https://doi.org/10.1061/(ASCE)0887-3801(1997)11:2(129)
Bagińska M, Srokosz PE. The Optimal ANN Model for Predicting Bearing Capacity of Shallow Foundations Trained on Scarce Data. KSCE Journal of Civil Engineering 2019; 23(1):130-137. DOI: https://doi.org/10.1007/s12205-018-2636-4
Al-Tememy M, Al-Neami M, Asswad M. Finite Element Analysis on Behavior of Single Battered Pile in Sandy Soil under Pullout Loading. International Journal of Engineering 2022; 35(6):1127-1134. DOI: https://doi.org/10.5829/IJE.2022.35.06C.04
Le BV, Van NN, Le BK. Study on the Settlement of Raft Foundations by Different Methods. MATEC Web of Conferences 2018; 193:04054. DOI: https://doi.org/10.1051/matecconf/201825104054
Saliha NB, Abdallab TA, Ahmadc FA. Soil-Foundation Interaction and Its Influences on Some Geotechnical Properties of Sulaimaniyah, Northern Iraq Fine-Grained Soils Utilizing Plaxis 3D. Engineering and Technology Journal 2023; 41(5):642-651. DOI: https://doi.org/10.30684/etj.2022.136345.1310
Waterman D. Structural Elements and Excavations. Presentation in CG1 Chile; 2006.
Meyerhof GG. Bearing Capacity and Settlement of Pile Foundations. Journal of the Geotechnical Engineering Division 1976; 102(3):197-228. DOI: https://doi.org/10.1061/AJGEB6.0000243
Bazaraa A, Kurkur M. N-Values Used to Predict Settlements of Piles in Egypt. In Situ Tests in Geotechnical Engineering 1986:462-474.
Nazir A, Nasr A. Pullout Capacity of Batter Pile in Sand. Journal of Advanced Research 2013; 4(2):147-154. DOI: https://doi.org/10.1016/j.jare.2012.04.001
Adeli H. Neural Networks in Civil Engineering. Computer-Aided Civil and Infrastructure Engineering 2001; 16(2):126-142. DOI: https://doi.org/10.1111/0885-9507.00219
Tarawneh B. Pipe Pile Setup: Database and Prediction Model Using Artificial Neural Network. Soils and Foundations 2013; 53(4):607-615. DOI: https://doi.org/10.1016/j.sandf.2013.06.011
McCulloch W, Pitts W. A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 1943; 5(4):115-133. DOI: https://doi.org/10.1007/BF02478259
Momeni E, Nazir R, Armaghani DJ, Maizir H. Prediction of Pile Bearing Capacity Using a Hybrid Genetic Algorithm-Based ANN. Measurement 2014; 57:122-131. DOI: https://doi.org/10.1016/j.measurement.2014.08.007
Zaabab AH, Zhang QJ, Nakhla M. A Neural Network Modeling Approach to Circuit Optimization and Statistical Design. IEEE Transactions on Microwave Theory and Techniques 1995; 43(6):1349-1358. DOI: https://doi.org/10.1109/22.390193
Das SK, Basudhar PK. Undrained Lateral Load Capacity of Piles in Clay Using Artificial Neural Network. Computers and Geotechnics 2006; 33(8):454-459. DOI: https://doi.org/10.1016/j.compgeo.2006.08.006
Moayedi H, Jahed AD. Optimizing an ANN Model with ICA for Estimating Bearing Capacity of Driven Pile in Cohesionless Soil. Engineering with Computers 2018; 34(2):347-356. DOI: https://doi.org/10.1007/s00366-017-0545-7
Dao DV, Adeli H, Ly HB, Le LM, Le VM, Le TT, Pham BT. A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation. Sustainability 2020; 12(3):830. DOI: https://doi.org/10.3390/su12030830
Maizir H, Kassim KA. Neural Network Application in Prediction of Axial Bearing Capacity of Driven Piles. International MultiConference of Engineers and Computer Scientists 2013:13-15.
Benali A, Nechnech A. Prediction of the Pile Capacity in Purely Coherent Soils Using the Approach of the Artificial Neural Networks. International Seminar on Innovation & Valorisation in Civil Engineering & Construction Materials 2011:23-25.
Pal M, Deswal S. Modeling Pile Capacity Using Support Vector Machines and Generalized Regression Neural Network. Journal of Geotechnical and Geoenvironmental Engineering 2008; 134(7):1021-1024. DOI: https://doi.org/10.1061/(ASCE)1090-0241(2008)134:7(1021)
Nehdi M, Djebbar Y, Khan A. Neural Network Model for Preformed-Foam Cellular Concrete. ACI Materials Journal 2001; 98(5):402-409. DOI: https://doi.org/10.14359/10730
Pham TA, Ly HB, Tran VQ, Giap LV, Huong LT, Hong TD. Prediction of Pile Axial Bearing Capacity Using Artificial Neural Network and Random Forest. Applied Sciences 2020; 10(5):1871. DOI: https://doi.org/10.3390/app10051871
Beale MH, Hagan MT, Demuth HB. Neural Network Toolbox User's Guide. 3rd ed. Natick: MathWorks; 2010.
Yousif ST, Razzak AA. Artificial Neural Networks Modeling of Elasto-Plastic Plates. Saarbrücken: Scholars' Press; 2017.
Mohanty S, Jha MK, Kumar A, Sudheer K. Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India. Water Resources Management 2010; 24(9):1845-1865. DOI: https://doi.org/10.1007/s11269-009-9527-x
Singh T, Verma A, Sharma P. A Neuro-Genetic Approach for Prediction of Time Dependent Deformational Characteristic of Rock and Its Sensitivity Analysis. Geotechnical and Geological Engineering 2007; 25(4):395-407. DOI: https://doi.org/10.1007/s10706-006-9117-0
Garson DG. Interpreting Neural Network Connection Weights. AI Expert 1991; 6(7):47-51.
Gaaver KE. Uplift Capacity of Single Piles and Pile Groups Embedded in Cohesionless Soil. Alexandria Engineering Journal 2013; 52(3):365-372. DOI: https://doi.org/10.1016/j.aej.2013.01.003
Al-Neami MA, Rahil FH, Al-Bayati KS. Bearing Capacity of Batter Piles Embedded in Sandy Soil. International Journal of Geotechnical Engineering 2016; 10(5):529-532. DOI: https://doi.org/10.1080/19386362.2016.1203094