Structural Failure Prediction in Multi Storied Reinforced Concrete Buildings Using Cuckoo Search Optimization Combined with Neural Network

摘要

Structural failure classification for the reinforced concrete (RC) buildings is one of the machine learning challenging tasks. Several successful studies were conducted to train the Neural Network (NN) with well-known optimization techniques. In the current work, a cuckoo search (CS) based classification model of structural failure of the RC buildings was proposed. The proposed NN-CS system was compared to well-known models, namely the Multilayer perceptron feed-forward network (MLP-FFN) trained with scaled conjugate gradient descent and the NN supported by the Particle swarm optimization algorithm (NN-PSO). The performance metrics, including the accuracy, precision, recall, and F-measure were calculated. The experimental results established the superiority of the proposed NN-CS with reasonable improvement (93.33% accuracy) compared to the other models.