# Image-Based Structural Health Monitoring: A Systematic Review
## 3 Results  
One thousand four hundred and eightty (1480) literatures were identified as potential information sources. These are bounded from the year 2001 up until 2022 that perform SHM system on buildings, bridges, and roads. The acquired review papers are from Western, European, and Asian countries that apply CV techniques, the digital presentation of data, or deep learning with regards to structures to assess the status and provide assessment for if it requires immediate restoration or repair. After acquiring all possible review papers for the topic, fifty-five (55) published articles are tagged as duplicates. The writers of this study each made the decision to adhere to the protocol outlined by our methodology. From there, we applied our inclusion and exclusion criteria and combined our selected literatures. Upon adding them using their respective DOIs, 55 were tagged as duplicates by Zotero. These duplicated items were then merged. These were removed during screening with the other one thousand two hundred and forty seven (1247) research papers that do not complement the goal of the researchers' current study, answer the RQs, and violate the inclusion and exclusion criteria. After the initial evaluation of papers, one hundred and seventy eight (178) potential literature reviews remained. In the screening phase, researchers have seen sixteen (16) papers having the same contents in their study. In addition, twenty one (21) research papers were focused on the application of vibration-based SHM to structures to assess strength and durability and not in terms of the image-based monitoring technique. There were also thirty two (32) review papers that are irrelevant to the research study since most of them are mainly related to human science courses. During the screening and inclusion process, the authors encountered issues regarding the full text access on some literatures. Full access to some literatures is unavailable due to limited access to the selected databases. To address this, the authors requested a copy of the manuscript directly from the lead authors, while the remaining articles were accessed using a proxy web server. Hence, the researchers have obtained one hundred and nine (109) literatures relevant to the topic.  
As an overview of the succeeding discussions reported in the next sections, it was evident that SHM is a method being researched by previous researchers in the field of construction and engineering to find an objective interpretation of the actual status of infrastructures after calamities, or due to a long period of use. This is constantly changing, especially in the fourth industrial revolution, the digitalization era, with the use of computer vision (CV) to assess structural integrity via tables and graphs. Image-based SHM may focus on surface defect recognition using feature extractors [17] and optimal fiber ultrasonic sensing [32], damage detection [33], target-tracking digital image correlation [34], guided wave-based SHM [35], wavelet [36; 37; 38], and GPS technology [39]. In other cases, sensors [40; 41; 42; 43; 44] and IoT [45; 46] applications and interventions are practiced. There are also existing studies that use convolutional neural networks, which is an advanced artificial intelligence to assess graphical data for feasible interpretation. Other research also used programming languages such as MATLAB and C++ Programming, with the application of Arduino to produce a product that will provide a sensor for a specific structural element; any member designed to resist any and a combination of forces and moments, are examples of structural elements. Furthermore, previous research storage systems require innovation in areas where big data is not supported, resulting in information becoming time limited.