A novel search scheme based on the social behavior of crow flock for feed-forward learning improvement in predicting the soil compression coefficient

Abstract

Recent improvements achieved using nature-inspired optimizers encouraged the authors to employ a novel type of metaheuristic algorithms, namely crow search algorithm (CSA) in this study. The CSA is employed for optimizing a feed-forward artificial neural network (ANN) in predicting the soil compression coefficient (SCC). The SCC is one of the most crucial geotechnical parameters that the early prediction of it can increase the safety and cost-effectiveness of a project. For more reliability, the used data are collected from a real-world project. After developing the CSA–ANN hybrid, the most proper values for the algorithm parameters, including flock size, flight length, and awareness probability are found by sensitivity analysis (to be 400, 2, and 0.1, respectively). A comparison between the results of the typical ANN and the CSA-trained version revealed that the proposed algorithm can effectively reduce the mean absolute error (MAE) in both learning and predicting the SCC pattern (by 8.25 and 7.29%, respectively). Moreover, the increase of the coefficient of determination (R2) from 70.64 to 74.83% in the training phase, and from 73.74 to 76.19% in the testing phase proves the efficiency of the CSA in enhancing the ANN. The suggested CSA–ANN, therefore, can be an efficient model for the early prediction of the SCC in civil/geotechnical engineering projects.