Feature subset selection in structural health monitoring data using an advanced binary slime mould algorithm

Abstract

Feature Selection (FS) is an important step in data-driven structural health monitoring approaches. In this paper, an Advanced version of the Binary Slime Mould Algorithm (ABSMA) is introduced for feature subset selection to improve the performance of structural damage classification techniques. Two operators of mutation and crossover are embedded to the algorithm, to overcome the stagnation situation involved in the Binary Slime Mould Algorithm (BSMA). The proposed ABSMA is then embedded in a new data-driven SHM framework which consists of three main steps. In the first step, structural time domain responses are collected and pre-processed to extract the statistical features. In the second step, the order of the extracted features is reduced using an optimization algorithm to find a minimal subset of salient features by removing irrelevant, and redundant data. Finally, the optimized feature vectors are used as inputs to Neural Network (NN) based classification models. Benchmark datasets of a timber bridge model and a three-story frame structure are employed to validate the proposed algorithm. The results show that the proposed ABSMA provides a better performance and convergence rate compared to other commonly used binary optimization algorithms.