Ghiasi, Ramin

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Ghiasi, Ramin

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  • Publication
    An advanced binary slime mould algorithm for feature subset selection in structural health monitoring data
    (CERAI, 2022-08-26)
    Ghiasi, Ramin 
    ;
    Malekjafarian, Abdollah 
    Feature selection (FS) is an important task for data analysis, pattern classification systems, and data mining applications. In this paper, an advanced version of binary slime mould algorithm (ABSMA) is introduced for feature subset selection to enhance the capability of the original slime mould algorithm (SMA) for processing of measured data collected from monitoring sensors installed on structures. In the first step, structural response signals under ambient vibration are pre-processed according to statistical characteristics for feature extraction. In the second step, extracted features of a structure are reduced using an optimization algorithm to find a minimal subset of salient features by removing noisy, irrelevant and redundant data. Finally, the optimized feature vectors are used as inputs to the surrogate models based on radial basis function neural network (RBFNN). A benchmark dataset of a wooden bridge model is considered as a test example. The results indicate that the proposed ABSMA shows better performance and convergence rate in comparison with four well-known metaheuristic optimizations. Furthermore, it can be concluded that the proposed feature subset selection method has the capability of more than 80% data reduction.
      29
  • Publication
    A data-driven approach for scour detection around monopile-supported offshore wind turbines using Naive Bayes classification
    (Elsevier, 2024-05-01)
    Jawalageri, Satish 
    ;
    Ghiasi, Ramin 
    ;
    Jalilvand, Soroosh 
    ;
    Prendergast, Luke J. 
    ;
    Malekjafarian, Abdollah 
    This paper proposes a novel data-driven framework for scour detection around offshore wind turbines (OWTs), where damage features are derived from wind and wave-induced acceleration signals collected along the tower. A numerical model of the NREL 5 MW wind turbine, which considers aerodynamic and hydrodynamic loading with soil-structure interaction (SSI) and servo-dynamics, is developed. The model is used to simulate the acceleration responses along the tower for a healthy structure, and a structure affected by progressive scour. A data segmentation process is initially performed on the collected data, which is followed by a feature selection scheme based on the analysis-of-variance (ANOVA) algorithm, to eliminate irrelevant characteristics from the time domain feature set of responses. The proposed framework consists of two main components: (a) offline training, and (b) real-time classification. The acceleration responses collected from the healthy structure and the structure subjected to three different damage scenarios (different scour depths) and under various load conditions, are used in the offline training mode. The selected feature vector from the feature extraction process is used as input to a Naive Bayes classifier (NBC) algorithm to train the model. In the real-time classification, a prediction of the scour depth affecting the structure is performed using a new dataset simulated from unseen load cases and scour conditions of the OWT. The results show that the model trained in the offline stage can predict the scour depth in the real-time monitoring stage with performance measures over approximately 94%.
      9Scopus© Citations 2