## 1 Introduction
[PERSON] [18] proposed a computer vision method that detects structural damage using the CNN model. The obtained vibration data were transformed into time and frequency domain images via visualization method, and then these images were fed into CNN to classify structural damage. The accuracy of classification can refer to 93.5%. [PERSON] [19] and [PERSON] [20] proposed a one-dimensional CNN with data-processing techniques that utilized a small training sampling to identify structural damage location and mass changes effectively. However, the above methods have some limitations in handling contaminated or noisy data using the CNN methods because they may regard the contaminated information from acceleration data as fault information. More importantly, contaminated time-sequence data such as acceleration data cannot effectively reflect structural damaged conditions. Thus, many scholars have studied methods based on frequency information. To be specific, time signal is transformed into frequency information, which can reduce the influence from contaminated data and improve the accuracy of damage identification in noisy environments. For example, [PERSON] [21] converted vibration signal to frequency information and then utilized machine learning algorithms such as support vector machines to localize concrete cracks based on obtained frequency information. The experimental results showed that the proposed method had high accuracy. [PERSON] [22] utilized an artificial intelligence method based on fast Fourier transform (FFT) to recognize structural damage degree. The results showed that FFT was suitable for nonstationary vibration signals. [PERSON] [23] proposed a method by combining FFT analysis and artificial learning to evaluate damaged changes of structures through the discrete model. The result gained higher accuracy compared with using only other models or analyses. However, the accuracy of FFT combined with traditional methods such as support vector machines needs to be further improved. Although FFT has many advantages in handling time-sequence, the existing methods do not fully consider the advantages of combining FTT with deep learning, such as DCNN for structural damage detection. In this paper, high precision and robust structural damage detection method are proposed based on FFT-DCNN. In this method, FFT is utilized to analyze the frequency information, reducing the influence of contaminated data [24; 25]. DCNN automatically extracts features from structural frequency information [26; 27]. Finally, a three-story building structure [28; 29] and ASCE benchmark [30] are usually utilized to evaluate the ability of structural damage detection based on our proposed method. The following are the primary contributions of this paper: (1) A novel sensor data-driven structural damage detection method is proposed by combining FFT with DCNN, which can effectively handle the vibration signal to recognize the structural damage condition. (2) Compared with traditional damage detection such as FFT-SVM and SVM, KNN, random forest, and XGBoost, the experimental result shows that the proposed method achieves higher accuracy damage detection. (3) Since FFT-DCNN takes a short time on test datasets, it indicates that the proposed method can be utilized for the online detection of structural damage conditions in the field of SHM. The rest of the paper is organized as follows. Section 2 introduces the proposed FFT-DCNN architecture. Section 3 describes the structural damage detection method based on the proposed method. The experimental setup is introduced in Section 4. FFT-DCNN is carried out on a three-story building structure and ASCE benchmark in Sections 5 and 6, respectively. Finally, Section 7 summarizes some conclusions based on FFT-DCNN and potential topics for future research.