Application of gene expression programming for modeling bearing capacity of aggregate pier reinforced clay

Document Type : Research Paper

Authors

1 Department of Civil Engineering, Sirjan University of Technology, Iran.

2 Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

10.22059/ijmge.2024.345164.594982

Abstract

Utilizing the aggregate piers is one of the methods to improve and increase the bearing capacity of soft soils. The ultimate bearing capacity of these piers is affected by parameters such as the physical properties of the piers, structural conditions, the embedment depth and replacement ratio of piers, which complicates the estimation of bearing capacity. In this study, the Gene Expression Programming method was used for the prediction of the ultimate bearing capacity of clay soils reinforced with aggregate piers. For this purpose, two different models were developed, of which the first model (GEP2) utilized two input variables, the undrained shear strength of clay (Su) and replacement ratio (ar), while the second model (GEP4) used four input variables including the undrained shear strength of clay (Su), replacement ratio (ar), slenderness ratio (Sr), and embedment depth of piers (df). The coefficient of determination of the GEP2 model, and the GEP4 model is 0.921 and 0.942, respectively. Besides, comparing the GEP4 model of this research with the developed models of previous studies confirms the superior performance of the GEP4 model, considering both the accuracy and number of input parameters. The results of sensitivity analysis showed that the undrained shear strength of clay (Su), replacement ratio (ar), slenderness ratio (Sr), and embedment depth of piers (df) have the highest impact on the prediction of bearing capacity, respectively. Furthermore, the parametric analysis demonstrated that increasing the Su, ar, Sr, and df would improve the bearing capacity of the aggregate piers reinforced clay.

Keywords


[1]. Nicholson PG (2014) Soil improvement and ground modification methods. Butterworth-Heinemann
[2]. Han J (2015) Principles and practice of ground improvement. John Wiley & Sons
[3]. Shahu JT, Reddy YR (2011) Clayey soil reinforced with stone column group: model tests and analyses. Journal of Geotechnical and Geoenvironmental Engineering 137:1265–1274
[4]. Yoo C, Lee D (2012) Performance of geogrid-encased stone columns in soft ground: full-scale load tests. Geosynth Int 19:480–490
[5]. Hasan M, Samadhiya NK (2017) Performance of geosynthetic-reinforced granular piles in soft clays: model tests and numerical analysis. Computers and Geotechnic 87:178–187
[6]. Mehrannia N, Nazariafshar J, Kalantary F (2018) Experimental investigation on the bearing capacity of stone columns with granular blankets. Geotechnical and Geological Engineering 36:209–222
[7]. Bunawan AR, Momeni E, Armaghani DJ, Rashid ASA (2018) Experimental and intelligent techniques to estimate bearing capacity of cohesive soft soils reinforced with soil-cement columns. Measurement 124:529–538
[8]. Coldwell E, Khosravi M, Zaregarizi S, et al (2020) Stability analysis of an embankment supported by spatially variable soil-cement columns. In: Geo-Congress 2020: Foundations, Soil Improvement, and Erosion. American Society of Civil Engineers Reston, VA, pp 507–515
[9]. Dehghanbanadaki A, Motamedi S, Ahmad K (2020) FEM-based modelling of stabilized fibrous peat by end-bearing cement deep mixing columns. Geomechanics and Engineering 20:75–86
[10]. Phutthananon C, Jongpradist P, Jongpradist P, et al (2020) Parametric analysis and optimization of T-shaped and conventional deep cement mixing column-supported embankments. Computers and Geotechnic 122:103555. https://doi.org/10.1016/j.compgeo.2020.103555
[11]. Vibhoosha MP, Bhasi A, Nayak S (2020) Effect of Geosynthetic Stiffness on the Behaviour of Encased Stone Columns Installed in Lithomargic Clay. In: Prashant A, Sachan A, Desai CS (eds) Advances in Computer Methods and Geomechanics. Springer Singapore, Singapore, pp 197–207
[12]. Waichita S, Jongpradist P, Schweiger HF (2020) Numerical and experimental investigation of failure of a DCM-wall considering softening behaviour. Computer and Geotechnic 119:103380. https://doi.org/10.1016/j.compgeo.2019.103380
[13]. Stuedlein AW, Holtz RD (2013) Bearing capacity of spread footings on aggregate pier reinforced clay. Journal of geotechnical and geoenvironmental engineering 139:49–58
[14]. Ashour S, Ghataora G, Jefferson I (2022) Behaviour of Model Stone Column Subjected to Cyclic Loading. Transportation Geotechnics 35:100777. https://doi.org/10.1016/j.trgeo.2022.100777
[15]. Ghanti R, Kashliwal A (2008) Ground Improvement Techniques–with a focussed study on stone columns. Dura Build Care PVT LTD Retrieved on January 30:2013
[16]. Greenwood DA (1970) Mechanical improvement of soils below ground surface. In: Inst Civil Engineers Proc, London/UK/. pp 11–22
[17]. Vesić AS (1972) Expansion of cavities in infinite soil mass. Journal of the soil Mechanics and Foundations Division 98:265–290
[18]. Hughes JMO, Withers NJ, Greenwood DA (1975) A field trial of the reinforcing effect of a stone column in soil. Geotechnique 25:31–44
[19]. Brauns J (1978) Initial bearing capacity of stone columns and sand piles. In: Int. Symp. on Soil Reinforcing and Stabilizing Techniques in Engineering Practice. pp 497–512
[20]. Barksdale RD, Bachus RC (1983) Design and construction of stone columns. Turner-Fairbank Highway Research Center
[21]. Xin T, Minghua Z, Wei C (2018) Numerical Simulation of a Single Stone Column in Soft Clay Using the Discrete-Element Method. International Journal of Geomechanics 18:04018176. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001308
[22]. Xin T, Minghua Z, Zhengbo H, Longjian F (2020) Failure Process of a Single Stone Column in Soft Soil beneath Rigid Loading: Numerical Study. International Journal of Geomechanics 20:04020130. https://doi.org/10.1061/(ASCE)GM.1943-5622.0001776
[23]. Mitchell JK (1981) Soil improvement-state of the art report. In: Proc., 11th Int. Conference. on SMFE. pp 509–565
[24]. Bergado DT, Lam FL (1987) Full scale load test of granular piles with different densities and different proportions of gravel and sand on soft Bangkok clay. Soils and foundations 27:86–93
[25]. Alkhorshid NR, Araujo GLS, Palmeira EM, Zornberg JG (2019) Large-scale load capacity tests on a geosynthetic encased column. Geotextiles and Geomembranes 47:632–641. https://doi.org/10.1016/j.geotexmem.2019.103458
[26]. Bazzazian Bonab S, Lajevardi SH, Saba HR, et al (2020) Experimental studies on single reinforced stone columns with various positions of geotextile. Innovative Infrastructure Solutions 5:98. https://doi.org/10.1007/s41062-020-00349-0
[27]. Ghanizadeh AR, Ghanizadeh A, Asteris PG, et al (2023) Developing bearing capacity model for geogrid-reinforced stone columns improved soft clay utilizing MARS-EBS hybrid method. Transportation Geotechnics 38:100906. https://doi.org/10.1016/j.trgeo.2022.100906
[28]. Dadhich S, Sharma JK, Madhira M (2021) Prediction of ultimate bearing capacity of aggregate pier reinforced clay using machine learning. International Journal of Geosynthetics and Ground Engineering 7:1–16
[29]. Bong T, Kim S-R, Kim B-I (2020) Prediction of ultimate bearing capacity of aggregate pier reinforced clay using multiple regression analysis and deep learning. Applied Sciences 10:4580
[30]. Kim B-I, Lee S-H (2005) Comparison of bearing capacity characteristics of sand and gravel compaction pile treated ground. KSCE Journal of Civil Engineering 9:197–203
[31]. Ali K, Shahu JT, Sharma KG (2010) Behaviour of reinforced stone columns in soft soils: an experimental study. In: Indian geotechnical conference. pp 620–628
[32]. Black JA, Sivakumar V, Bell A (2011) The settlement performance of stone column foundations. Géotechnique 61:909–922
[33]. Fattah MY, Al-Neami MA, Al-Suhaily AS (2017) Estimation of bearing capacity of floating group of stone columns. Engineering science and technology, an international journal 20:1166–1172
[34]. Ambily AP, Gandhi SR (2007) Behavior of stone columns based on experimental and FEM analysis. Journal of geotechnical and geoenvironmental engineering 133:405–415
[35]. Hanna AM, Etezad M, Ayadat T (2013) Mode of failure of a group of stone columns in soft soil. International Journal of Geomechanics 13:87–96
[36]. Mohanty P, Samanta M (2015) Experimental and numerical studies on response of the stone column in layered soil. International Journal of Geosynthetics and Ground Engineering 1:1–14
[37]. Algin HM, Gumus V (2018) 3D fe analysis on settlement of footing supported with rammed aggregate pier group. International Journal of Geomechanics 18:1–18
[38.] Etezad M, Hanna AM, Ayadat T (2015) Bearing capacity of a group of stone columns in soft soil. International Journal of Geomechanics 15:1–15
[39]. Naseer S, Sarfraz Faiz M, Iqbal S, Jamil SM (2019) Laboratory and numerical based analysis of floating sand columns in clayey soil. International Journal of Geo-Engineering 10:10. https://doi.org/10.1186/s40703-019-0106-6
[40]. Mansourian A, Ghanizadeh AR, Golchin B (2018) Modeling of resilient modulus of asphalt concrete containing reclaimed asphalt pavement using feed-forward and generalized regression neural networks. Journal of Rehabilitation in Civil Engineering 6:132–147
[41]. Kellouche Y, Boukhatem B, Ghrici M, Tagnit-Hamou A (2019) Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network. Neural Computational Application 31:969–988. https://doi.org/10.1007/s00521-017-3052-2
[42]. Ma CK, Lee YH, Awang AZ, et al (2019) Artificial neural network models for FRP-repaired concrete subjected to pre-damaged effects. Neural Computational Application 31:711–717. https://doi.org/10.1007/s00521-017-3104-7
[43]. Nasrollahzadeh K, Afzali S (2019) Fuzzy logic model for pullout capacity of near-surface-mounted FRP reinforcement bonded to concrete. Neural Computational Application 31:7837–7865. https://doi.org/10.1007/s00521-018-3590-2
[44]. Su H, Fu Z, Wen Z (2019) SFPSO algorithm-based multi-scale progressive inversion identification for structural damage in concrete cut-off wall of embankment dam. Applied Soft Computing 84:105679. https://doi.org/10.1016/j.asoc.2019.105679
[45]. Xue W, Xu Z, Wang H, Ren Z (2019) Hazard assessment of landslide dams using the evidential reasoning algorithm with multi-scale hesitant fuzzy linguistic information. Applied Soft Computing 79:74–86. https://doi.org/10.1016/j.asoc.2019.03.032
[46]. Ghanizadeh AR, Heidarabadizadeh N, Jalali F (2020) Artificial neural network back-calculation of flexible pavements with sensitivity analysis using Garson’s and connection weights algorithms. Innovative Infrastructure Solutions 5:63. https://doi.org/10.1007/s41062-020-00312-z
[47]. Jebur AA, Atherton W, al Khaddar RM, Loffill E (2021) Artificial neural network (ANN) approach for modelling of pile settlement of open-ended steel piles subjected to compression load. European Journal of Environmental and Civil Engineering 25:429–451. https://doi.org/10.1080/19648189.2018.1531269
[48]. Ghanizadeh AR, Ziaee A, Khatami SMH, Fakharian P (2022) Predicting Resilient Modulus of Clayey Subgrade Soils by Means of Cone Penetration Test Results and Back-Propagation Artificial Neural Network. Journal of Rehabilitation in Civil Engineering 10:146–162
[49]. Goh ATC (1995) Empirical design in geotechnics using neural networks. Geotechnique 45:709–714
[50]. Chan WT, Chow YK, Liu LF (1995) Neural network: an alternative to pile driving formulas. Computers and Geotechnic 17:135–156
[51]. Das SK, Basudhar PK (2006) Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnic 33:454–459
[52]. Das M, Dey AK (2018) Prediction of Bearing Capacity of Stone Columns Placed in Soft Clay Using ANN Model. Geotechnical and Geological Engineering 36:1845–1861. https://doi.org/10.1007/s10706-017-0436-0
[53]. Dey AK, Debnath P (2020) Empirical approach for bearing capacity prediction of geogrid-reinforced sand over vertically encased stone columns floating in soft clay using support vector regression. Neural Computational Applied 32:6055–6074. https://doi.org/10.1007/s00521-019-04092-1
[54]. Ardakani A, Dinarvand R, Namaei A (2020) Ultimate Shear Resistance of Silty Sands Improved by Stone Columns Estimation Using Neural Network and Imperialist Competitive Algorithm. Geotechnical and Geological Engineering 38:1485–1496. https://doi.org/10.1007/s10706-019-01104-8
[55]. Dehghanbanadaki A (2021) Intelligent modelling and design of soft soil improved with floating column-like elements as a road subgrade. Transportation Geotechnics 26:100428. https://doi.org/https://doi.org/10.1016/j.trgeo.2020.100428
[56]. Das M, Dey AK (2022) Prediction of Bearing Capacity of Stone Columns Using Type-2 Fuzzy Logic. In: Das BB, Hettiarachchi H, Sahu PK, Nanda S (eds) Recent Developments in Sustainable Infrastructure (ICRDSI-2020)—GEO-TRA-ENV-WRM: Conference Proceedings from ICRDSI-2020 Vol. 2. Springer Singapore, Singapore, pp 413–437
[57]. Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027
[58]. Ferreira C (2002) Gene expression programming in problem solving. In: Soft computing and industry. Springer, pp 635–653
[59]. Shahmansouri AA, Bengar HA, Ghanbari S (2020) Compressive strength prediction of eco-efficient GGBS-based geopolymer concrete using GEP method. Journal of Building Engineering 31:101326
[60]. Mahdinia S, Eskandari-Naddaf H, Shadnia R (2019) Effect of cement strength class on the prediction of compressive strength of cement mortar using GEP method. Construction and Building Materials 198:27–41
[61]. Javed MF, Amin MN, Shah MI, et al (2020) Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete. Crystals (Basel) 10:737. https://doi.org/10.3390/cryst10090737
[62]. Alinezhadi M, Mousavi SF, Hosseini Kh (2021) Comparison of Gene Expression Programming (GEP) and Parametric and Non-parametric Regression Methods in the Prediction of the Mean Daily Discharge of Karun River (A case Study: Mollasani Hydrometric Station). JSTNAR 25:43–62. https://doi.org/10.47176/jwss.25.1.1012
[63]. Ganguly S, Datta S, Chakraborti N (2009) Genetic algorithm-based search on the role of variables in the work hardening process of multiphase steels. Computational Material Science 45:158–166
[64]. Bhargava S, Dulikravich GS, Murty GS, et al (2011) Stress corrosion cracking resistant aluminum alloys: Optimizing concentrations of alloying elements and tempering. Materials and Manufacturing Processes 26:363–374
[65]. Shahmansouri AA, Bengar HA, Jahani E (2019) Predicting compressive strength and electrical resistivity of eco-friendly concrete containing natural zeolite via GEP algorithm. Construction and Building Material 229:1–18
[66]. Naderpour H, Nagai K, Fakharian P, Haji M (2019) Innovative models for prediction of compressive strength of FRP-confined circular reinforced concrete columns using soft computing methods. Composite Structure 215:69–84
[67]. Martin JP (2018) A Full-Scale Experimental Investigation of the Bearing Performance of Aggregate Pier-Supported Shallow Foundations
[68]. Bergado DT, Lam FL (1987) Full scale load test of granular piles with different densities and different proportions of gravel and sand on soft Bangkok clay. Soils and foundations 27:86–93
[69]. Baumann V, Bauer GEA (1974) The performance of foundations on various soils stabilized by the vibro-compaction method. Canadian Geotechnical Journal 11:509–530
[70]. Stuedlein AW, Holtz RD (2012) Analysis of footing load tests on aggregate pier reinforced clay. Journal of Geotechnical and Geoenvironmental Engineering 138:1091–1103
[71]. Greenwood DA (1975) Vibroflotation: rationale for design and practice. Methods of treatment of unstable ground 189–209
[72]. Hughes JMO, Withers NJ, Greenwood DA (1975) A field trial of the reinforcing effect of a stone column in soil. Geotechnique 25:31–44
[73]. Han J, Ye S (1991) Field tests of soft clay stabilized by stone columns in coastal areas of China. In: Proc., 4th Int. Deep Foundations Institute Conf. pp 243–248
[74]. Lillis C, Lutenegger AJ, Adams M (2004) Compression and uplift of rammed aggregate piers in clay. In: GeoSupport 2004: Drilled Shafts, Micropiling, Deep Mixing, Remedial Methods, and Specialty Foundation Systems. pp 497–507
[75]. White DJ, Pham HT v, Hoevelkamp KK (2007) Support mechanisms of rammed aggregate piers. I: Experimental results. Journal of Geotechnical and Geoenvironmental Engineering 133:1503–1511
[76]. Khan K, Jalal FE, Iqbal M, et al (2022) Predictive Modeling of Compression Strength of Waste PET/SCM Blended Cementitious Grout Using Gene Expression Programming. Materials 15:3077
[77]. Chen X-Y, Chau K-W (2019) Uncertainty analysis on hybrid double feedforward neural network model for sediment load estimation with LUBE method. Water Resources Management 33:3563–3577
[78]. Gandomi AH, Yun GJ, Alavi AH (2013) An evolutionary approach for modeling of shear strength of RC deep beams. Materials and Structure 46:2109–2119
[79]. Javed MF, Amin MN, Shah MI, et al (2020) Applications of gene expression programming and regression techniques for estimating compressive strength of bagasse ash based concrete. Crystals (Basel) 10:737
[80]. Shah MI, Javed MF, Abunama T (2021) Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. Environmental Science and Pollution Research 28:13202–13220
[81]. Azim I, Yang J, Javed MF, et al (2020) Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming. In: Structures. Elsevier, pp 212–228
[82]. Iqbal MF, Liu Q, Azim I, et al (2020) Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. J Hazardous Materials 384:121322.
[83]. Alavi AH, Gandomi AH, Nejad HC, et al (2013) Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems. Neural Computational Applied 23:1771–1786. https://doi.org/10.1007/s00521-012-1144-6
[84]. Emamgolizadeh S, Bateni SM, Shahsavani D, et al (2015) Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS). Journal of Hydrolology (Amst) 529:1590–1600. https://doi.org/10.1016/j.jhydrol.2015.08.025