This research proposes an alternative for estimating shear strength of soil based on a hybridization of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). SVR is used as a function approximation method for making prediction of the soil shear strength based on a set of twelve variables including sample depth, sand content, loam content clay content, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic index, and liquid index. The hybrid framework, named as PSO–SVR, relies on PSO, as a metaheuristic, to optimize the training phase of the employed function approximator. A data set consisting of 443 soil samples associated with the experimental results of shear strength has been collected from a housing project in Vietnam. This data set is then used to train and verify the performance of the PSO–SVR model specifically constructed for shear strength estimation. The hybrid model has achieved a good modeling outcome with Root Mean Square Error (RMSE) = 0.038, Mean Absolute Percentage Error (MAPE) = 9.701%, and Coefficient of Determination (R2) = 0.888. Hence, the PSO–SVR model can be a potential alternative to be participated in the design phase of high-rise housing projects.
This is a preview of subscription content, log in to check access.
This research was funded by the Geographic Information Science Research group, Ton Duc Thang University, Ho Chi Minh city, Vietnam. We would like to thank the Topgeo construction and Investigation Joint Stock Company (Hanoi) and Vingroup Joint Stock Company (Vietnam) for providing the data for this research.
Compliance with ethical standards
Conflict of interest
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.
Tien Bui D, Hoang N-D, Nhu V-H (2018) A swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: a case study at Trung Luong National Expressway Project (Vietnam). Eng Comput. https://doi.org/10.1007/s00366-018-0643-1Google Scholar
Das BM, Sobhan K (2013) Principles of geotechnical engineering. Cengage Learning, Boston (ISBN-10:1133108660)Google Scholar
Prayogo D, Susanto YTT (2018) Optimizing the prediction accuracy of friction capacity of driven piles in cohesive soil using a novel self-tuning least squares support vector machine. Adv Civ Eng 2018:9. https://doi.org/10.1155/2018/6490169Google Scholar
Hoang N-D, Bui DT (2018) Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bull Eng Geol Environ 77(1):191–204. https://doi.org/10.1007/s10064-016-0924-0Google Scholar
Abramento M, Carvalho CS (1989) Geotechnical parameters for the study of natural slopes instabilization at ‘Serra do Mar’ Brazil. In: Proceedings 12th international conference soil mechanics foundations engineering Rio de Janeiro, vol 3, pp 1599–1602Google Scholar
Katte V, Blight G (2012) The roles of solute suction and surface tension in the strength of unsaturated soil. In: Mancuso C, Jommi C, D’Onza F (eds) Unsaturated soils: research and applications. Springer, Berlin, pp 431–437Google Scholar
Leong EC, Nyunt TT, Rahardjo H (2013) Triaxial testing of unsaturated soils. In: Laloui L, Ferrari A (eds) Multiphysical testing of soils and shales. Springer series in geomechanics and geoengineering. Springer, Berlin, Heidelberg, pp 33–44Google Scholar
Bishop CM (2011) Pattern recognition and machine learning (information science and statistics). Springer, Berlin (ISBN-10: 0387310738)Google Scholar
Tien Bui D, Bui Q-T, Nguyen Q-P, Pradhan B, Nampak H, Trinh PT (2017) A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric For Meteorol 233:32–44. https://doi.org/10.1016/j.agrformet.2016.11.002Google Scholar
Sachdeva S, Bhatia T, Verma AK (2017) Flood susceptibility mapping using GIS-based support vector machine and particle swarm optimization: a case study in Uttarakhand (India). In: 2017 8th International conference on computing, communication and networking technologies (ICCCNT), 3–5 July 2017, pp 1–7. https://doi.org/10.1109/ICCCNT.2017.8204182
Hacibeyoglu M, Ibrahim MH (2018) A novel multimean particle swarm optimization algorithm for nonlinear continuous optimization: application to feed-forward neural network training. Sci Program 2018:9. https://doi.org/10.1155/2018/1435810Google Scholar
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, New York, NY, pp 39–43Google Scholar
Clayton CR (1995) The standard penetration test (SPT): methods and use. Construction Industry Research and Information Association, LondonGoogle Scholar
Schmertmann JH (1978) Guidelines for cone penetration test: performance and design. Federal Highway Administration, Washington, DCGoogle Scholar
(ASTM) ASfTaM (2005) ASTM D4648/D4648M-16, standard test methods for laboratory miniature vane shear test for saturated fine-grained clayey soil. Active Standard ASTM D4648, vol ASTM International, West Conshohocken, PA, 2016. http://www.astm.org