Optimization strategies of neural networks for impact damage classification of RC panels in a small dataset

Abstract Predicting damage modes of reinforced concrete (RC) panels subjected to impact loading is a difficult task and often involves considerable effort in doing experiments or simulations. The development of missiles in terms of strength and destructive power requires an accurate estimation of future damage levels. In addition, data collected from the experiment are often small. Therefore, this study aims to build an artificial neural network (ANN) model to classify the damage modes of RC panel under impact loading and enhance its performance by optimizing the model’s hyperparameters when learning a small dataset (254 observes for four classes in this study). To address this a novel optimization strategy was proposed and two metaheuristic optimization algorithms, i.e., genetic algorithm (GA) and particle swarm optimization (PSO) were presented for automatic selection of hyperparameters to increase the accuracy of the ANN model. The proposed optimization strategy was developed based on the incorporation of a stepwise gridsearch (SG) method into a nested cross-validation (NCV) process to find the optimal parameters for the ANN model, named SG-NCV-ANN. The efficiency of the proposed SG-NCV-ANN model and two hybrid models including ANN-GA, ANN-PSO are evaluated by comparing to each other and other machine-learning-based classification methods including ANN using a randomized cross-validation search (RCV-ANN), oblique random forest (oRF), support vector machine (SVM) and k-nearest neighbors (k-NN). Accuracy, micro f1 score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were employed to thoroughly assess the obtained results from the model. The experimental results indicated that the ANN-GA model achieves the highest AUC and f1 score compared to other state-of-the-art methods, following by the ANN-PSO model. While the proposed SG-NCV-ANN model obtained the best generalization performance on the present small dataset.