# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Applications for Innovation
These approaches may include the finite element method (FEM) or discrete element method (DEM). Further, [96] implemented DEM by creating a linear and linear parallel bond model, which was used to describe the mechanical constitutive behavior of asphalt mixture through binary image processing, followed by particle formation, clump establishment, and the removal of voids. As the model achieves an accuracy of more than 85%, the difference between the modeled results and actual testing may be caused by the contact stiffness of mortar and aggregates. If structures are subject to any type of loading, they act accordingly. As a result, when evaluating the transformation of force and displacement between particles based on [PERSON]'s second law of motion, access to limitless simulations and analyses is achievable using DEM [97]. When compared to real test results, this technique may reveal viable models with little or no inaccuracy. Understanding different relationships and underlying links amongst structures and their elements when loaded or subject to extreme excitations can be just a click away using high computing devices. Varying but suitable dynamic loadings [98] to realize performance and application of data fusion [99] to investigate harmony and dissonance of different components, several parameters, multiple purposes of the application, and various mechanical and physical properties and behaviors can be the highlight of future works in civil infrastructures using image-based SHM. Big Data Analysis in Image-based SHM  
Big data may be classified according to its variety, volume, velocity, and complexity [100]. Structured data has a well-defined format and is simple to store and query. On the other hand, data mining is needed for the proper organization and analysis of semi-structured and unstructured data [101]. SHM has mostly concentrated on directly instrumenting a structure utilizing sensors that provide data useful for the evaluation. There are several common sensor options, such as accelerometers, strain gauges, inclinometers, thermometers, and wind pressure sensors, which produce semi-structured data for vital monitoring applications of data matrix entries and sensor type, sensitivities, locations, and orientations [100]. Now, big data is defined by its volume. In wired and wireless applications of SHM, infrastructures are aimed to be monitored during their lifetime and by monitoring, the data collected files up. In the case of full-field imaging, large datasets are generated. For instance, common camera frame rates vary from 25 to 60 frames each second (fps), with high-speed cameras capable of reaching 240 fps [102]. In dynamic applications, this amount rises to 500 frames per second for full-field data [103]. Given that each shot is 2 MB in size, monitoring even a single structure may quickly generate terabytes of data every week [104]. One of the most challenging challenges in big data is managing and keeping up with the high velocity of data output in terms of data transmission, storage, and processing. The SHM goal is not achieved until such high-resolution datasets are processed and timely structural health decisions are made, despite continual developments in sensing technology that enable the collection of structural data with high resolution in time and space. This is a significant barrier to making timely judgments in buildings with real-time SHM in the wake of catastrophic events like natural and man-made disasters. [100]. Furthermore, another problem for SHM is the complexity of the datasets and the changing interactions between observations. Long-term health monitoring causes data source uncertainty and dataset unpredictability, which makes administering SHM applications complicated. HD cameras [69; 86] and unmanned aerial vehicles [55; 65] are the most common tools to collect pictures and videos from concrete columns and slabs and other steel structures. The development of crack width relates to the evolution of temperature and humidity because they indicate stiffness loss.