## 1 Introduction  
Disasters such as earthquakes and typhoons degrade the strength of structures. To protect property and lives, the structural health of buildings must be inspected after such natural disasters. In civil engineering, structural health monitoring (SHM) is applied to detect damage to structures. Novel SHM methods based on signal processing techniques have recently been proposed to analyze measured structural responses. In 2001, [PERSON] et al. [1] applied a statistical process control technique and a pattern recognition technique to diagnose the vibration-based damage. [PERSON] et al. [2] utilized the model updating approach to set up a damage criterion in 2004. Structural dynamic responses, such as velocity and acceleration, can be collected to determine the structural health condition of a building. SHM results can serve as the basis to implement suitable structural retrofit. In recent years, entropy has been used in different fields. Multiscale entropy (MSE) approaches have been applied to extract information from biological signals affected by noise. For example, [PERSON] et al. [3, 4] have proposed an MSE method and used it to separate healthy individuals from patients with congestive heart failure and patients with erratic cardiac arrhythmia. According to this study, analysis based on a single time scale cannot comprehensively extract heart signal information. This has thus led to the emergence of the concept of multiscale analysis, in which a coarse-grained process is utilized to divide a time signal into several time scales; this thus increases the feasibility and accuracy of entropy measures. In 2013, [PERSON] et al. [5] applied a composite Multiscale entropy (CMSE) method to identify gear damage; white noise and pink noise were used to prove that the CMSE method could reduce the standard deviation (SD) of entropy values. In addition, CMSE was reported to provide more accurate entropy values than MSE. Nevertheless, CMSE application resulted in undefined entropy values. In 2014, [PERSON] et al. [6] proposed a refined composite Multiscale entropy (RCMSE) approach. The RCMSE approach was demonstrated to increase the accuracy of entropy estimation and reduces the probability of inducing undefined entropy values for analyses of short time series. Because entropy is a statistical concept, artificial intelligence (AI) technology is used to differentiate entropy values. AI technology, including artificial neural networks (ANNs) and machine learning, has been applied in several fields. In 1992, [PERSON] et al. [7] applied a backpropagation neural network (BPN) to identify damage on a three-story steel structure. In 1993, [PERSON] et al. [8] applied a BPN to identify changes in the vibrational signatures of a structure and consequently detect damage. Moreover, in 1994, [PERSON] et al. [9] demonstrated the versatility of neural networks as a problem-solving tool and determined that such networks can be applied to solve different problems in civil engineering. In 2012, [PERSON] et al. [10] used permutation entropy (PE) to extract the features of bearing faults; in addition, they incorporated a support vector machine to classify damage cases. In 2013, [PERSON] et al. [11] combined multiscale PE and adaptive neuro-fuzzy systems to develop a bearing fault diagnosis system. The aim of the present study was to quantify entropy values. The flowchart of the study is presented in Figure 1. An ANN-based technique was applied to distinguish between signals obtained from damaged and healthy structures. Finite element model was applied to generate a training database for the ANN. The proposed system was validated by numerical simulation of a seven-story structure under the ambient vibration response and a series of steel structure experiment. The flowchart of the study procedures is shown in Figure 1. RCMSE is adopted to improve the stability and reliability when evaluating the entropy value of a system. Numerical model is first used to generate the damage database, and the entropy statics are collected.