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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)

  • Dieu Tien Bui
  • Nhat-Duc Hoang
  • Viet-Ha Nhu
Original Article
  • 98 Downloads

Abstract

Determining the shear strength of soil is an important task in the design phase of construction project. This study puts forward an artificial intelligence (AI) solution to estimate this parameter of soil. The proposed approach is a hybrid AI model that integrates the least squares support vector machine (LSSVM) and the cuckoo search optimization (CSO). A dataset of 332 soil samples collected from the Trung Luong National Expressway Project in Viet Nam have been used for constructing and validating the AI model. The sample depth, sand percentage, loam percentage, clay percentage, moisture content, wet density of soil, specific gravity, liquid limit, plastic limit, plastic index, and liquid index are used as input variables to predict the output variable of shear strength. In the hybrid AI framework, LSSVM is employed to generalize the functional mapping that estimates the shear strength from the information provided by the aforementioned input variables. Since the model establishment of LSSVM requires a proper setting of the regularization and the kernel function parameters, the CSO algorithm is utilized to automatically determine these parameters. Experimental results show that the prediction accuracy of the hybrid method of LSSVM and CSO (RMSE = 0.082, MAPE = 14.841, and R2 = 0.885) is better than those of the benchmark approaches including the standard LSSVM, the artificial neural network, and the regression tree. Therefore, the proposed method is a promising alternative for assisting construction engineers in the task of soil shear strength estimation.

Keywords

Soil Shear strength Expressway Hybrid artificial intelligence Optimization Data-driven method 

Notes

Acknowledgements

This research was supported by the Geographic Information Science research group, Ton Duc Thang University, Ho Chi Minh city, Vietnam. We would like to thank the Transport Engineering Design Inc.—TEDI, Hanoi, Vietnam, for providing the data for this analysis.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

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

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