Prediction of strength of reinforced lightweight soil using an artificial neural network

Abstract

Purpose

Reinforced lightweight soil (RLS) consisting of dredged soil, cement, air‐foam, and waste fishing net is considered to be an eco‐friendly backfilling material because it provides a means to recycle both dredged soil and waste fishing net. It may be difficult to find an optimum mixing ratio of RLS considering the design criteria and the construction's situation using the limited test results because the unconfined compressive strength is complicatedly influenced by various mixing ratios of admixtures. As a result, in order to expedite the field application of RLS, an appropriate prediction method is needed. The paper aims to address these issues.

Design/methodology/approach

In this study, an artificial neural network (ANN) model that was based on experimental test results performed on various mixing ratios, was developed to predict the unconfined compressive strength of RLS.

Findings

It was found that the unconfined compressive strength of RLS at a given mixing ratio could be reasonably estimated using the developed neural network model. In addition, sensitivity analysis was also conducted to evaluate the effect of mixing conditions on the compressive strength of RLS.

Practical implications

RLS is considered to be environmentally friendly because it provides a means to recycle both dredged soil and waste fishing net. The contractors could use the proposed ANN model as an alternative method to predict the strength of RLS with a specific mixing ratio.

Originality/value

This paper reveals that the developed ANN model can be served as a simple and reliable predictive tool for the strength of RLS without excessive laboratory tests for various admixture contents. An optimum admixture ratio of composed materials to get a designed strength could be easily found by using the proposed ANN model.

Citation

Park, H.I. and Kim, Y.T. (2011), "Prediction of strength of reinforced lightweight soil using an artificial neural network", Engineering Computations, Vol. 28 No. 5, pp. 600-615. https://doi.org/10.1108/02644401111141037