# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## 1 Introduction  
In the recent years, there has been a considerable interest in \"Smart City\" concept and the monitoring, controlling, and preservation of the health state of critical infrastructures, like roads, buildings, bridges, and tunnels. Structural health is required to be diagnosed at every moment during a structure life cycle in order to provide high quality services. Civil engineering structures are designed for the lifetime of the occupants or facilities and their failure might lead to catastrophic consequences in terms of human life and economic assets [1]. Structural health monitoring (SHM) is one of the mainstakeholders of the said Smart City concept [2]. SHM is an effective solution contributing to the need for safety, lowering the maintenance costs and reliable condition assessment of structures [3,4]. SHM is an interdisciplinary subject that incorporates knowledge and experiences from synergetic technologies in civil, mechanical, control, and computer engineering to deal with the health assessment of structures. The health monitoring of structures has been the subject of many studies for the past three decades [5,6]. The development of a reliable SHM method for civil structures is a challenging task due to ambient-induced uncertainty and the associated complexity measures. Four analysis levels of damage detection are applied in the context of SHM that include: (1) detection, (2) localization, (3) quantification, and (4) prediction of the remaining life, whereas the first three levels are more explicitly reported in literature [7,8]. Based on the acting load, SHM methods can be divided into two classes of static and dynamic-based methods [9]. The methods that use vibration characteristics of structures to assess the health state of structures, so-called vibration-based damage detection (VDD) [10,11]. The key premise of VDD is to estimate the modal parameters of a structure while using the analytical model that was constructed by system identification methods [12,13]. Static-based damage detection (SDD) methods rely on measuring the change in static response of structure, such as load bearing capacity, strain, deflection, and stiffness. [PERSON] et al. and [PERSON] et al. [14] used strain data for damage detection of structure. [PERSON] et al. [15] proposed a method to take advantages of stay cable force measurements and structural temperatures for damage detection. [PERSON] et al. [16] used deflection data for damage identification in structures. [PERSON] et al. [17] introduced a temperature-driven method while using strain information of structures for anomaly detection. Weigh in motion (WIM) is a widely used vehicle classification method for the health monitoring of structures. Bridge weigh in motion (BWIM) is a type of WIM technology that can identify traffic data, including speed, number of axles, axles spacing, and gross and axle weight of the passing vehicles using a series of conventional strain gauges. BWIM is particularly suitable for short-term measurements of traffic data, as it can be easily installed and detached from the bridge. [PERSON] and [PERSON] [18] applied BWIM through using strain gauges to gain information on the quantity and weights of the trucks crossing highway bridges. [PERSON] et al. [19] proposed a BWIM-based damage identification method through introducing the concept of 'Virtual Axle' for deriving a damage indicator. [PERSON] and [PERSON] [20] proposed a model-free damage detection method using deck accelerations response and BWIM. [PERSON] and [PERSON] [21] developed a three dimensional finite element model to estimate multi-vehicles-bridge interaction in a BWIM. The environmental factors are a weak point of vibration- and static-based damage detection [22]. However, in some cases, the temperature based methods shown higher sensitivity when compared to vibration based methods. Estimations of the modal parameters in SHM are generally performed by system identification methods [23].