# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 1 Introduction  
Recent earthquakes around the world have shown how earthquake disasters can break in society. For example, an earthquake in Taiwan in 1999 resulted in over 10,000 casualties and economic losses of over USD 10 billion. The magnitude 7 earthquake in Haiti in 2010 caused tens of thousands of casualties, and the collapse of social infrastructure resulted in huge economic losses. The earthquake and tsunami that occurred in Japan in 2011 not only killed more than 10,000 people, they also caused a major radioactive leak owing to the explosion of the Daiichi nuclear power plant. The earthquake in Mexico in 2017 caused more than 1000 casualties, and a number of skyscrapers in Mexico City, the capital city of Mexico, were severely damaged [1]. Structures can be damaged through seismic loadings, and the conditions of such structures need to be determined to ensure their safety. The current practice for inspecting damaged structures requires engineers to directly evaluate the damage by viewing the structure by sight. However, this assessment can be subjective and might expose the engineers to risk. In recent years, vibration-based Structural Health Monitoring (SHM), a method for monitoring the health of such structures by measuring the response of the structures, has recently entered the spotlight. SHM aims to ensure the safety of civil infrastructure by measuring its dynamic response. The traditional SHM system measures the structural response by installing wired sensors such as accelerometers and displacement transducers, e.g., the Alamosa Canyon Bridge, I-10 Bridge, Hakucho Bridge in Japan, Bill Emerson Memorial Bridge in Missouri, and the Tsing Ma Bridge in Hong Kong [2; 3; 4; 5]. However, the wired SHM system cannot be easily deployed to existing structures, and is costly. To overcome these limitations, wireless sensor technology has been introduced. [PERSON] and [PERSON] presented asensor that can evaluate the condition of the structure, and [PERSON] and [PERSON] discussed a sensor and wireless communication technology that remotely monitors the health of large civil structures. [PERSON] et al. developed a micro-sensor that can retrieve data from an embedded strain gauge, and [PERSON] et al. proposed a monitoring system using a mobile phone [7]. Although enhancements in wireless sensor technology could reduce the cost of SHM systems, these methods still require manual installation of the sensors. Computer vision technology has recently been applied to SHM, overcoming the limitations of wired and wireless systems. [PERSON] et al. [8] concluded that close-range photogrammetry technology is a powerful measurement method that can provide unique solutions for a variety of bridge engineering applications. [PERSON] et al. [9] compared and evaluated the usefulness of various measurement techniques (terrestrial laser scanning (TLS), tachymetry, photogrammetry) applied to establish the behavior of suspension bridges under various loads. [PERSON] et al. [10] measures the vertical displacement of railroad bridges using terrestrial laser scanning (TLS) technology, and confirmed that this technology can provide reasonable results for quick assessment of railway infrastructure. [PERSON] et al. [11] confirmed that it is possible to obtain a 3D model of a structure with satisfactory accuracy through TLS and photogrammetry. [PERSON] et al. [12] presented the most efficient method for generating 3D models of underground structures using TLS techniques. [PERSON] and [PERSON] [13] conducted a study to extract the displacement of the structure and characterize the vibration mode of the cantilever beam using digital image correlation technology. [PERSON] et al. [14] and [PERSON] et al. [15] confirmed the reliability of a vision-based system for system identification by comparing vision-based and wired sensor systems. Furthermore, a method used to measure the structural displacement from an unmanned aerial vehicle (UAV) was proposed by [PERSON] et al. [16].