Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network

Stabilized dredged sediments are used as a backfilling material to reduce construction costs and solution environmental protection. Therefore, the compressive strength is an important criterion determine stabilized application such road construction, building highway construction. Using traditional method empirical approach experimental methods, determination of difficult due complexity this composite material. In investigation, artificial neural network (ANN) model introduced forecast strength. To perform simulation, 51 datasets were collected from literature. The dataset consists 4 input variables (water content, cement air foam waste fishing net content) output variable (compressive strength). Evaluation models was made compared on training (70% data) testing (30% remaining by criteria Pearson’s correlation coefficient (R), Mean Absolute Error (MAE), Root Square (RMSE). results show that ANN can accurately predict with low water content. content most affecting unconfined be in following order: > net.