Prediction of suction caissons behavior in cohesive soils using computational intelligence methods

Document Type : Research Paper


Department of Earth Sciences Engineering, Arak University of Technology, Arak, Iran


Compared to drag anchors, suction caissons (Q) in clays often provide a cost-effective alternative for jacket structures, catenary, tension leg moorings, and taut leg. In this research, two computational approaches are proposed for predicting the uplift capacity of Q in clays. The proposed approaches are based on the combinations of adaptive network-based fuzzy inference system (ANFIS) models (ANFIS-subtractive clustering (ANFIS-SC) and ANFIS-fuzzy c-means (ANFIS-FC)) with metaheuristic techniques (ant colony optimization (ACO) or particle swarm optimization (PSO)). In these approaches, the PSO and ACO algorithms are employed to enhance the accuracy of ANFIS models. In order to develop hybrid models, a comprehensive database from open-source literature is used to train and test the proposed models. In these models, d (diameter of caisson), L (embedded length), D (depth), Su (undrained shear strength of soil), θ (inclined angle), and Tk (load rate parameter) were used as the input parameters. The performance of all models was evaluated by comparing performance indexes, i.e., means squared error and squared correlation coefficient. As a result, PSO and ACO can be used as reliable algorithms to enhance the accuracy of ANFIS models. Moreover, it was found that the ANFIS– subtractive clustering-ACO model provides better results in comparison with other developed hybrid models.


[1] Alavi, A. H., Aminian, P., Gandomi, A. H., & Esmaeili, M. A. (2011). Genetic-based modeling of uplift capacity of suction caissons. Expert Systems with Applications, 38(10), 12608-12618.
[2] Anemangely, M., Ramezanzadeh, A., & Tokhmechi, B. (2017). Shear wave travel time estimation from petrophysical logs using ANFIS-PSO algorithm: A case study from Ab-Teymour Oilfield. Journal of Natural Gas Science and Engineering, 38, 373-387.
[3] Bezdek, J. C. (1973). Fuzzy mathematics in pattern classification. Ithaca: Cornell University.
[4] Cao, J., Audibert, J., Al-Khafaji, Z., Phillips, R., & Popescu, R. (2002). Penetration resistance of suction caissons in clay. Paper presented at the The Twelfth International Offshore and Polar Engineering Conference.
[5] Catalão, J. P. d. S., Pousinho, H. M. I., & Mendes, V. M. F. (2010). H. Fattahi & H. Nazari / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 109-116 115 Hybrid wavelet-PSO-ANFIS approach for short-term electricity prices forecasting. IEEE transactions on power systems, 26(1), 137-144.
[6] Chen, M.-Y. (2013). A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Information Sciences, 220, 180-195.
[7] Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of intelligent and Fuzzy systems, 2(3), 267- 278.
[8] Cho, Y., Lee, T., Park, J., Kwag, D., Chung, E., & Bang, S. (2002). Field tests on suction pile installation in sand. Paper presented at the ASME 2002 21st International Conference on Offshore Mechanics and Arctic Engineering.
[9] Clukey, E., Morrison, M., Gamier, J., & Corté, J. (1995). The response of suction caissons in normally consolidated TLP loading conditions. Paper presented at the Offshore Technology Conference.
[10] Clukey, E. C., & Morrison, M. J. (1993). A centrifuge and analytical study to evaluate suction caissons for TLP applications in the Gulf of Mexico. Paper presented at the Design and performance of deep foundations: Piles and piers in soil and soft rock.
[11] Datta, M., & Kumar, P. (1996). Suction beneath cylindrical anchors in soft clay. Paper presented at the The Sixth International Offshore and Polar Engineering Conference.
[12] Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. Computational Intelligence Magazine, IEEE, 1(4), 28-39.
[13] Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: A survey. Theoretical computer science, 344(2), 243-278.
[14] Dyvik, R., Andersen, K. H., Hansen, S. B., & Christophersen, H. P. (1993). Field tests of anchors in clay. I: Description. Journal of Geotechnical engineering, 119(10), 1515-1531.
[15] Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. Paper presented at the Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on.
[16] El-Gharbawy, S., & Olson, R. (2000). Modeling of suction caisson foundations. Paper presented at the The Tenth International Offshore and Polar Engineering Conference.
[17] Ghasemi, E., Kalhori, H., & Bagherpour, R. (2016). A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting. Engineering with Computers, 32(4), 607-614.
[18] Ghomsheh, V. S., Shoorehdeli, M. A., & Teshnehlab, M. (2007). Training ANFIS structure with modified PSO algorithm. Paper presented at the 2007 Mediterranean Conference on Control & Automation.
[19] Grima, M. A., Bruines, P., & Verhoef, P. (2000). Modeling tunnel boring machine performance by neuro-fuzzy methods. Tunnelling and Underground Space Technology, 15(3), 259-269.
[20] Hasanipanah, M., Amnieh, H. B., Arab, H., & Zamzam, M. S. (2018). Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications, 30(4), 1015-1024.
[21] Iphar, M., Yavuz, M., & Ak, H. (2008). Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environmental geology, 56(1), 97-107.
[22] Jang, J.-S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685.
[23] Jiang, H., Kwong, C., Ip, W., & Wong, T. (2012). Modeling customer satisfaction for new product development using a PSObased ANFIS approach. Applied Soft Computing, 12(2), 726-734.
[24] Kaveh, A., Hamze-Ziabari, S., & Bakhshpoori, T. (2018). Feasibility of PSO-ANFIS-PSO and GA-ANFIS-GA models in prediction of peak ground acceleration. International Journal of Optimization in Civil Engineering, 8(1), 1-14.
[25] Larsen, P. (1989). Suction anchors as an anchoring system for floating, offshore constructions. Paper presented at the Offshore Technology Conference.
[26] Lin, C.-J., & Hong, S.-J. (2007). The design of neuro-fuzzy networks using particle swarm optimization and recursive singular value decomposition. Neurocomputing, 71(1-3), 297-310.
[27] Meysam Mousavi, S., Tavakkoli-Moghaddam, R., Vahdani, B., Hashemi, H., & Sanjari, M. (2013). A new support vector modelbased imperialist competitive algorithm for time estimation in new product development projects. Robotics and ComputerIntegrated Manufacturing, 29(1), 157-168.
[28] Oliveira, M. V. d., & Schirru, R. (2009). Applying particle swarm optimization algorithm for tuning a neuro-fuzzy inference system for sensor monitoring. Progress in Nuclear Energy, 51(1), 177-183.
[29] Pousinho, H. M. I., Mendes, V. M. F., & Catalão, J. P. d. S. (2011). A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal. Energy Conversion and Management, 52(1), 397-402.
[30] Pousinho, H. M. I., Mendes, V. M. F., & Catalão, J. P. d. S. (2012). Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach. International Journal of Electrical Power & Energy Systems, 39(1), 29-35.
[31] Rahman, M., Wang, J., Deng, W., & Carter, J. (2001). A neural network model for the uplift capacity of suction caissons. Computers and Geotechnics, 28(4), 269-287.
[32] Rao, S. N., Ravi, R., & Ganapathy, C. (1997). Pullout behavior of model suction anchors in soft marine clays. Paper presented at the The Seventh International Offshore and Polar Engineering Conference.
[33] Sezer, E. A., Nefeslioglu, H. A., & Gokceoglu, C. (2014). An assessment on producing synthetic samples by fuzzy C-means for limited number of data in prediction models. Applied Soft Computing, 24, 126-134.
[34] Shahlaei, M., Madadkar-Sobhani, A., Saghaie, L., & Fassihi, A. (2012). Application of an expert system based on Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System (GA– ANFIS) in QSAR of cathepsin K inhibitors. Expert Systems with Applications, 39(6), 6182-6191.
[35] Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. Paper presented at the Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence.
[36] Shoorehdeli, M. A., Teshnehlab, M., & Sedigh, A. (2006). A novel training algorithm in ANFIS structure. Paper presented at the 2006 American Control Conference.
[37] Üstün, B., Melssen, W., Oudenhuijzen, M., & Buydens, L. (2005). Determination of optimal support vector regression parameters by genetic algorithms and simplex optimization. Analytica Chimica Acta, 544(1), 292-305.
[38] Ustun, S. V., & Demirtas, M. (2009). Modeling and control of V/f 116 H. Fattahi & H. Nazari / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 109-116 controlled induction motor using genetic-ANFIS algorithm. Energy Conversion and Management, 50(3), 786-791.
[39] Weiling, C., & Lee, J. (1995). Fuzzy Logic for the Applications to Complex Systems. Paper presented at the Proceedings of the International Joint Conference of CFSA/IFIS/SOFT on Fuzzy Theory and Applications. Singapore et al.: World Scientific.
[40] Whittle, A. J., & Kavvadas, M. J. (1994). Formulation of MIT-E3 constitutive model for overconsolidated clays. Journal of Geotechnical engineering, 120(1), 173-198.
[41] Yilmaz, I., & Yuksek, G. (2009). Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. International Journal of Rock Mechanics and Mining Sciences, 46(4), 803-810.
[42] Zdravković, L., Potts, D., & Jardine, R. (2001). A parametric study of the pull-out capacity of bucket foundations in soft clay. Geotechnique, 51(1), 55-67.
[43] Zeng, D. H., Liu, Y., Jiang, L. B., Li, L., & Xu, G. (2012). A New Approach to Cutting Temperature Prediction Using Support Vector Regression and Ant Colony Optimization. Paper presented at the Advanced Engineering Forum.