## 1 Introduction
Based on the incorporated input and output data, identification methods can be classified into two categories: the methods that incorporates input-output measurements to identify system parameters; so-called input-output methods, and the approaches that just use unknown output measurements, termed as output-only methods [13; 35]. Since output-only methods take all excitation forces as an unknown output, the obtained results are not controllable and repeatable. Moreover, the accuracy of results is greatly affected by variation in noise level [36; 37; 38]. Despite the mentioned challenges, output-only methods are preferred over input-output methods due to the technical difficulties associated with artificial exciting of large civil engineering structures that is the main requirement of input-output methods [39; 40]. [PERSON] et al. [41; 42] conducted a comparison between input-output and output-only subspace system identification methods using a model of a support-excited multi-story frame structure. Modal parameters were extracted from an input-output state-space model and the obtained results were compared to the ones obtained from output-only response data. Higher accuracy was achieved using the input-output method. The input-output algorithm is still a tempting choice for earthquake induced excitation. [PERSON] et al. [43] developed a new scheme for modal identification using output-only and input-output methods. The quality of identified system parameters was evaluated using Monte Carlo analysis in terms of accuracy of estimations and noise robustness. It was inferred that using input information provides more reliable results for modal identification. [PERSON] et al. [44] evaluated the performance of data-driven stochastic subspace identification (SSI-DATA) using test data from offshore jacket-type platform. The efficiency and efficacy of three different excitation signals of impact, step relaxation and ground motion were investigated using both input-output and output-only algorithms. All procedures had excellent agreement with estimated modal frequencies of stronger modes. However, less accurate results were reported for damping ratios. Stochastic subspace identification has been successfully applied for the modal analysis of several civil engineering structures [45; 46]. Different authors have used the identical term of \"SSI\" to denote two different phenomena of \"stochastic subspace identification\" and \"subspace system identification\" [47; 48; 49]. In order to avoid confusion with the term \"SSI\", from now on, SSI is only given to refer \"stochastic subspace identification\" category and no abbreviation is going to be used for subspace system identification throughout this paper. Recently a large number of subspace-based methods have been applied for VDD of civil structures. However, the previously conducted surveys have not kept pace with the changing environment and diversity in this field. Therefore, there is a need for a systematic review and meta-analysis focusing on the most important recent studies conducted in the considered area. The presented review systematically addresses the available studies that employed subspace-based techniques in the VDD of civil structures and describes some contributions towards the development and application of a subspace system identification algorithm in recent years. Some new perspectives are considered in the current study including classification of the selected papers. The outline of this review paper is as follows: Section 2 reviews literature about subspace-based dynamic identification and damage detection. The research framework including the PRISMA methodology is outlined in Section 3. Section 4 describes the results and the relation between key parameters in the selected papers. Finally, Section 5 ends with the concluding remarks and recommendations for future studies.