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首页> 外文期刊>International Journal of Naval Architecture and Ocean Engineering >Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models
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Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

机译：基于ANN，SVM和ANFIS模型的非改型河堤防波堤的破坏程度预测

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ABSTRACT The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correlation coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.
机译：摘要沿海结构的损伤分析非常重要，因为它涉及许多设计参数，以便更好，更安全地进行结构设计。在本研究中，未重塑的护堤防波堤的实验数据是从印度苏拉什卡尔市NITK的应用力学与水力学系海洋结构实验室收集的。使用实验数据集构建了诸如人工神经网络（ANN），支持向量机（SVM）和自适应神经模糊推理系统（ANFIS）模型之类的软计算技术，以预测未整形的护岸防波堤的破坏程度。实验数据用于训练ANN，SVM和ANFIS模型，并根据统计量度（如均方误差，均方根误差，相关系数和散布指数）确定结果。结果表明，软计算技术（即ANN，SVM和ANFIS）可以有效地预测未整形的护岸防波堤的破坏程度。

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