Output-Based Structural Damage Detection by Using Correlation Analysis Together with Transmissibility

Abstract

Output-based structural damage detection is becoming increasingly appealing due to its potential in real engineering applications without any restriction regarding excitation measurements. A new transmissibility-based damage detection approach is presented in this study by combining transmissibility with correlation analysis in order to strengthen its performance in discriminating damaged from undamaged scenarios. From this perspective, damage detection strategies are hereafter established by constructing damage-sensitive indicators from a derived transmissibility. A cantilever beam is numerically analyzed to verify the feasibility of the proposed damage detection procedure, and an ASCE (American Society of Civil Engineers) benchmark is henceforth used in the validation for its application in engineering structures. The results of both studies reveal a good performance of the proposed methodology in identifying damaged states from intact states. The comparison between the proposed indicator and the existing indicator also affirms its applicability in damage detection, which might be adopted in further structural health monitoring systems as a discrimination criterion. This study contributed an alternative criterion for transmissibility-based damage detection in addition to the conventional ones.

Real-Time Parametric Evaluation of Weld in Metallic Wires: A 3-D Simulation and Experimental Validation

“…Moreover, an attempt was made to establish the corelation index between the simulated results and actual results obtained from plant trial through statistical regression analysis [37] and plotted in Fig. 9.…”

“…In order to quantify the amount of similarity between simulation and actual data, a statistical approach has been adopted, popularly known as Pearson's co-relation coefficient. The technique was developed by Karl Pearson and later independently developed by Hotelling [37] wherein by considering X and Y are pair of measurements, one can say that the two measurements are statistically related, if knowing the value of one-member X of Fig. 9 Correlation of simulated results as a function of various stages any pair increases the precision of estimation of the secondmember Y.…”

Introductory Chapter: Some Insights into Bridge Structural Condition Monitoring

“…Review can refer to [14]. Even a lot of investigations about transmissibility can be accessed to [15][16][17][18][19], for instance, transmissibility coherence (TC) is raised [14,17]; cosine-based indicator is constructed from the modal assurance criterion (MAC) and incorporated with transmissibility for damage detection and quantification relatively [15]; Mahalanobis distance is also applied to transmissibility for damage detection [16]; transmissibility is extended to apply in the responses analysis of ultrasonic testing [19]; the transmissibility still encounters difficulty in both theory development and engineering application. This study tries to extend the transmissibility theory for estimating and reconstructing mode shape from structural responses solely.…”

A Data-Driven Damage Identification Framework Based on Transmissibility Function Datasets and One-Dimensional Convolutional Neural Networks: Verification on a Structural Health Monitoring Benchmark Structure

“…Feng et al [43] verified the feasibility and sensitivity of TFs in damage detection in subway tunnels using numerical simulation. Zhou et al [44] combined TFs with correlation analysis and conducted damage detection in cantilever beam and ASCE benchmark structures.…”