A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project, Hai Phong city (Vietnam)

  • Viet-Ha Nhu
  • Nhat-Duc Hoang
  • Van-Binh Duong
  • Hong-Dang Vu
  • Dieu Tien BuiEmail author
Original Article


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.


Soil shear strength Housing project Hybrid computational intelligence Particle swarm optimization Support vector regression 



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.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Geological-Geotechnical EngineeringHanoi University of Mining and GeologyHanoiVietnam
  2. 2.Faculty of Civil Engineering, Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  3. 3.Department of Hydrogeology and Engineering GeologyVietnam Institute of Geosciences and Mineral ResourcesHanoiVietnam
  4. 4.Geographic Information Science Research GroupTon Duc Thang UniversityHo Chi Minh CityVietnam
  5. 5.Faculty of Environment and Labour SafetyTon Duc Thang UniversityHo Chi Minh CityVietnam

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