A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining
Document Type
Article
Publication Date
3-1-2020
Abstract
Implementation of data mining (DM) techniques in different areas of civil engineering has recently given very good results. However, application of DM in structural health monitoring (SHM) is not used as much as expected, thus, many challenges are still ahead. Therefore, it seems a vital need is required to develop the applicability of DM in SHM. To this end, the current study attempts to present a DM-based damage detection methodology using modal parameter data, which trained by means of a hybrid artificial neural network-based imperial competitive algorithm (ANN-ICA). Likewise, the hybrid ANN is optimized by a new optimization-based evolutionary algorithm, called ICA, to predict the severity and location of multiple damage cases obtained from experimental modal analysis of intact and damaged slab-on-girder bridge structures. Furthermore, the applicability of DM approach was developed to detect the hidden patterns in vibration data using Cross Industry Standard Process for DM (CRISP-DM) tool. The performance of the model was carried out using comparison of a pre-developed ANN and ANN-ICA model. (C) 2019 Elsevier B.V. All rights reserved.
Keywords
Damage identification, Data mining, Imperial competitive algorithm, Artificial neural network, Cross Industry Standard Process for Data Mining, Evolutionary algorithms
Divisions
fac_eng
Funders
Universiti Malaya [IIRG007A],Ministry of Education (MOE), Malaysia [IIRG007A]
Publication Title
Applied Soft Computing
Volume
88
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS