Recent Developments in Damage Identification of Structures Using Data Mining

Abstract

Civil structures are usually prone to damage during their service life and it leads them to loss their serviceability and safety. Thus, damage assessment can guarantee the integrity of structures. As a result, a structural damage detection approach including two main components, a set of accelerometers to record the response data and a data mining (DM) procedure, is widely used to extract the information on the structural health condition. In the last decades, DM has provided numerous solutions to structural health monitoring (SHM) problems as an all-inclusive technique due to its powerful computational ability. This paper presents the first attempt to illustrate the data mining techniques (DMTs) applications in SHM through an intensive review of those articles dealing with the use of DMTs aimed for classification-, prediction-and optimization-based data mining methods. According to this categorization, applications of DMTs with respect to SHM research area are classified and it is concluded that, applications of DMTs in the SHM domain have increasingly been implemented, in the last decade and the most popular techniques in the area were artificial neural network (ANN), principal component analysis (PCA) and genetic algorithm (GA), respectively.

Finite Element Analysis of A Strengthened Beam Deliberating Elastically Isotropic And Orthotropic Cfrp Material

“…The procedure of the current study is drawn and presented in Figure 3. One of the main important responses of structural members in analysis and design is damage severity [20][21][22][23][24]. In order to explain the complicated mechanical response of the concrete materials under loading, many constitutive approaches have been proposed including damage model, anisotropic damage and isotropic damage model.…”

Predicting the Mechanical Properties of Concrete Using Intelligent Techniques to Reduce CO₂ Emissions

“…The main objective of optimisation methods is to achieve values from a set of parameters, which maximise and minimise objective functions subject to constraints. Some studies have suggested using the particle swarm optimisation (PSO) algorithm to improve the techniques (14,15). Optimisation methods using PSO apply the behaviour of flocking birds.…”

“…The mathematical equations, which corresponded in terms of coded factors with materials units in kg/m 3 were as follows. Splittingtensile strength = 1.80464+ 6.22*10 −3 * C−9.51*10 −5 *FA-3.54*10 −4 * CA−4.54*10 −3 *W+ 0.073336*SP [15] where C is cement amount, FA is fine aggregate amount, CA is coarse aggregate amount, W is water amount and SP are the chemical admixtures amount. Here, C, FA, CA, W, and SP must all be greater than zero.…”

Structural Damage Localization and Quantification Based on a CEEMDAN Hilbert Transform Neural Network Approach: A Model Steel Truss Bridge Case Study

“…In addition, Artificial Neural Network (ANN) has been widely used in SHM to automate the damage identification approach with the purpose of effectively reducing the human intervention and speeding up the process of structure diagnosis [35]. Most of the previous research works adopted the structural response parameters to train the ANN for the identification of the damages in civil engineering structures [36][37][38][39]. An ANN-based structural damage detection approach was presented by Masri et al [40] to monitor the linear and nonlinear systems through structural parameters even in the presence of noisy environments.…”