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Modelling soil compaction parameters using a hybrid soft computing technique of LSSVM and symbiotic organisms search

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Abstract

The soil compaction parameters, i.e., optimum water content (OWC) and maximum dry density (MDD) are essential parameters used in civil engineering projects for monitoring the compaction of soils. The current practice of using laboratory testing to determine the OWC and MDD is time-consuming and costly. Thus, this research suggests a hybrid machine-learning solution to replace traditional soil testing for determining OWC and MDD. The novel method combines the least square support vector machine (LSSVM) and symbiotic organisms search (SOS) algorithm. These two computational intelligence algorithms work together to create an OWC and soil MDD prediction model, LSSVM–SOS. For this purpose, a large database of 13 different soils featuring 6 influencing factors was used. Overall, experimental results show that the proposed LSSVM–SOS has attained the most accurate prediction of the OWC of soils (RMSE = 0.0288, MAE = 0.0199, and R2 = 0.9656) and MDD (RMSE = 0.0305, MAE = 0.0206, and R2 = 0.9641). These results of the proposed model are significantly better than those obtained from other hybrid LSSVMs constructed with particle swarm optimization, grey wolf optimizer, and slime mould optimization algorithms. According to the findings, the newly created LSSVM–SOS can aid geotechnical engineers in the design phase of civil engineering projects.

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  1. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Lal Babu Tiwari, Avijit Burman & Pijush Samui

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  1. Lal Babu Tiwari
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  2. Avijit Burman
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Correspondence to Pijush Samui.

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Tiwari, L.B., Burman, A. & Samui, P. Modelling soil compaction parameters using a hybrid soft computing technique of LSSVM and symbiotic organisms search. Innov. Infrastruct. Solut. 8, 2 (2023). https://doi.org/10.1007/s41062-022-00966-x

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