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Structural Engineering and Mechanics
  Volume 63, Number 4, August25 2017, pages 429-438

Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm
Sankhadeep Chatterjee, Sarbartha Sarkar, Sirshendu Hore, Nilanjan Dey, Amira S. Ashour, Fuqian Shi and Dac-Nhuong Le

    Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multiobjective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLPFFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.
Key Words
    genetic algorithm; classification; neural network; reinforced concrete
Sankhadeep Chatterjee : Department of Computer Science & Engineering, University of Calcutta, Kolkata, India
Sarbartha Sarkar : Department of Mining Engineering, Indian School of Mines, Dhanbad, India
Sirshendu Hore : Department of Computer Science & Engineering, Hooghly Engineering and Technology College Chinsurah, India
Nilanjan Dey : Department of Information Technology, Techno India College of Technology, West Bengal, India
Amira S. Ashour : Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Egypt
Fuqian Shi : College of Information & Engineering, Wenzhou Medical University, Wenzhou, China
Dac-Nhuong Le : Duy Tan University, Danang, Vietnam /
Haiphong University, Haiphong, Vietnam

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