2. METHODOLOGY 2.1 Development environments The primary development environments for this web application were TypeScript (v5.2.2) and Python (v3.9.18), with Node.js (v20.10.0) employed for back-end development and PostgreSQL (v16.1) for the database. CentOS Stream release 9 was utilized as the operating system for development purposes. TypeScript, a language that features static typing, is employed to enhance code readability and maintainability. Python, conversely, leveraged its extensive library and flexibility to optimize data processing and scripting; Node.js capitalized on its high scalability and asynchronous processing capabilities to streamline server-side processing; and PostgreSQL facilitated stable database management and potent query processing. The proposed system leverages these development environments, thereby attaining high performance and scalability. 2.2 RQD measurement The application has the capability to efficiently and accurately measure Rock Quality Designation (RQD). As demonstrated in the accompanying Figure 1, an image of a borehole core captured using an RGB camera can be uploaded to the application to facilitate the automatic detection of cracks present within the image. The user can select any range on the uploaded image by mouse dragging, and the RQD value within that range is automatically calculated. It is imperative to note that for accurate evaluation, it is necessary to input in advance how many metres the width (actual size) of the entire image corresponds to. This function has been shown to greatly accelerate the RQD measurement process, thereby reducing subjective variations among operators when compared to conventional manual visual measurement. It is anticipated that this will position the RQD as a pragmatic instrument for expeditious and precise on-site rock mass evaluation within the domains of geological engineering and civil and construction engineering.
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