Damage Level Prediction of NonReshaped Berm Breakwater using Soft Computing Techniques

Abstract

Tranquility condition inside the port and harbor has to be maintained for loading cargo and passengers. In order to maintain calm condition inside the port and harbor, breakwater has to be constructed to dissipate wave energy that is coming inside. The alignment of the breakwater must be carefully considered after examining the predominant direction of approach of waves and winds, degree of protection required, magnitude and direction of littoral drift and the possible effect of these breakwaters on the shoreline. In general these studies are invariably conducted in a physical model test where various alternatives are studied and the final selection will be based on performance consistent with cost. Considering the coastal boundary and depth variation, field analysis of wave structure interaction, determination of stability and damage level of berm breakwater structure is difficult. Mathematical modeling of these complex interactions is difficult while physical modeling will be costly and time consuming. Hence one has to depend on physical model studies which are expensive and time consuming. Soft computing techniques, such as, Artificial Neural Network (ANN), Support Vector Machine (SVM),Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) have been efficiently proposed as a powerful tool for modeling and predictions in coastal/ocean engineering problems. For developing soft computing models in prediction of damage level of non-reshaped berm breakwater, data set are obtained from experimental damage level of non-reshaped berm breakwater using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. These data sets are divided into two groups, one for training and the other for testing. The input parameters that influence the damage level (S) of nonreshaped berm breakwater, such as, relative wave steepness (H/L0), surf similarity (ζ), slope angle (cotα) relative berm position by water depth (hB/d), relative armour stone weight (W50/W50max), relative berm width (B/ L0) and relative berm location (hB/L0) are considered in developing soft computing models for prediction damage level. The ANN model is developed for the prediction of damage level of non-reshaped berm breakwater. Two network models, ANN1 and ANN2 are constructed based on the parameters which influence the damage level of non-reshaped berm breakwater. The seven input parameters that are initially considered for ANN1 model are (H/L0), (ζ), (cotii α), (hB/d), (W50/W50max), (B/ L0) and (hB/L0). The ANN1 model is studied with different algorithm namely, Scaled Conjugate Gradient (SCG), Gradient Descent with Adaptive learning (GDA) and Levenberg-Marquardt Algorithm (LMA) with five numbers of hidden layer nodes and a constant 300 epochs. LMA showed good performance than the other algorithms. Also, influence of input parameters is evaluated using Principal Component Analysis (PCA). From PCA study, it is observed that cotα is the least influencing parameter on damage level. Based on the PCA study, least influencing parameter is discarded and ANN2 model is developed with remaining six input parameters. Training and testing of the ANN2 network models are carried out with LMA for different hidden layer nodes and epochs. The ANN2 with LMA 6-5-1 with 300 epochs gave good results. It is observed that the correlation of about 88% between predicted and observed damage level values by the ANN2 network models and measured values are in good agreement Furthermore, to improve the result of prediction of damage level of non-reshaped berm breakwater, SVM model was developed. This technique works on structural risk minimization principle that has greater generalization ability and is superior to the empirical risk minimization principle as adopted in conventional neural network models. This model was developed based on statistical learning theory. The basic idea of SVM is to map the original data x into a feature space with high dimensionality through a nonlinear mapping function and construct an optimal hyper-plane in new space. SVM models were constructed using different kernel functions. In order to study the performance of each kernel in predicting damage level of non-reshaped berm breakwater, SVM is trained by applying these kernel functions. Performance of SVM is based on the best setting of SVM and kernel parameters. Correlation Coefficient (CC) of SVM (polynomial) model (CC Train = 0.908 and CC Test = 0.888) is considerably better than other SVM models. To avoid over-fitting or under-fitting of the SVM model due to the improper selection of SVM and kernel parameters and also the performance of SVM, hybrid particle swarm optimization tuned support vector machine regression (PSO-SVM) model is developed to predict damage level of non-reshaped berm breakwater. The performance of the PSOSVM models in the prediction of damage level is compared with the measured values using statistical measures, such as, CC, Root mean Square Error (RMSE) and Scatteriii Index (SI). PSO-SVM model with polynomial kernel function gives realistic prediction when compared with the observed values (CC Train = 0.932, CC Test = 0.921). It is observed that the PSO-SVM models yield higher CCs as compared to that of SVM models. However, it is noticed that ANN model in isolation cannot capture all data patterns easily. Adaptive Neuro-Fuzzy Inference System (ANFIS) uses hybrid learning algorithm, which is more effective than the pure gradient decent approach used in ANN. ANFIS models were developed with different membership namely Triangular-shaped built-in membership function (TRIMF), Trapezoidal-shaped built-in membership function (TRAPMF), Generalized bell-shaped built-in membership function (GBELLMF), and Gaussian curve built-in membership function (GAUSSMF) to predict damage level of non-reshaped berm breakwater. The performance of the ANFIS models in the prediction of damage level is compared with the measured values using statistical measures, such as, CC, RMSE and SI. ANFIS model with GAUSSMF gave realistic prediction when compared with the observed values (CC Train = 0.997, CC Test = 0.938). It is observed that the ANFIS models yield higher CCs as compared to that of ANN models. The different soft computing models namely, ANN, SVM, PSO-SVM and ANFIS results are compared in terms of CC, RMSE, SI and computational time. The hybrid models in both (ANFIS and PSO-SVM) cases showed better results compared to individual models (ANN and SVM). When the hybrid models are compared, ANFIS model gives higher CC and lower RMSE. But considering computational time, ANFIS has taken more time than PSO-SVM model. Hence PSO-SVM is computationally efficient as compared to ANFIS. ANFIS and PSO-SVM models perform better and similar to observed values. Hence, ANFIS or PSO-SVM can replace the ANN, SVM for damage level prediction of nonreshaped berm breakwater. ANFIS or PSO-SVM can be utilized to provide a fast and reliable solution in prediction of the damage level prediction of non-reshaped berm breakwater, thereby making ANFIS or PSO-SVM as an alternate approach to map the wave structure interactions of berm breakwater.