Application of ANN in Construction: Comprehensive Study on Identifying Optimal Modifier and Dosage for Stabilizing Marine Clay of Qingdao Coastal Region of China

Abstract

Nowadays, the use of new compound chemical stabilizers to treat marine clay has gained significant attention. However, the complex non-linear relationship between the influencing factors and the unconfined compressive strength of chemically treated marine clay is not clear. In order to study the influence of various factors (dosage, type of stabilizer, curing age) on the unconfined compressive strength of solidified soil during chemical treatment, experiments were performed to determine the unconfined compressive strength of soft marine clay modified with various types of stabilizers. Further, an artificial neural network (ANN) model was used to establish a prediction model based on the unconfined compressive strength test data and to verify the performance. Sensitivity and optimization analyses were further conducted to explore the relative significance of parameters as well as the optimal dosage amount. Research has found that when the content of aluminate cement is 89.5% and the content of curing agent is 30%, the unconfined compressive strength significantly increases after 28 days of solidification, and the change in quicklime content has the greatest effect on the improvement in the unconfined compressive strength. The influence of modifiers on the unconfined compressive strength is in the order: potassium hydroxide > kingsilica > quick lime > bassanite. The values of each factor were obtained when the unconfined compressive strength was the maximum, which provided support for the optimization of the treatment scheme. The analysis of chemical treatment is no longer limited to the linear relationship according to the test results, which proves the feasibility of non-linear relationship analysis based on the artificial neural network.