Borehole data analysis is a critical aspect of any construction project. It provides valuable insights into the geological and geophysical properties of the area, helping engineers make informed decisions about the design, safety, and stability of the structure. By analyzing the data obtained from the borehole, engineers can determine the type of soil or rock formation present, the depth of the bedrock, and other important factors that can impact the foundation of the structure.
With this information, they can design and construct a more efficient, safer, and stable building. One of the challenges in borehole data analysis is the sheer volume of data that needs to be processed. The data is typically recorded in PDFs or other unstructured formats, making it difficult to extract meaningful information.
This is where computer vision techniques such as restoration, detection and OCR come into play. To train OCR and detection algorithms, engineers feed the algorithms large amounts of data, including text, shapes, and images. The training process is critical to ensure that the algorithms can accurately identify the information in the PDFs.
The algorithms are trained to recognize specific patterns and shapes within the data, ensuring that they can accurately identify and classify all relevant data. OCR training involves teaching the algorithm to recognize different fonts, sizes, and styles of text. This is particularly important as PDFs may contain text that is faded, distorted, or broken, making it difficult for traditional OCR algorithms to recognize.
Engineers use data augmentation techniques to train the OCR algorithm on a diverse range of text types and backgrounds to ensure that it can recognize all types of text accurately. Detection training involves teaching the algorithm to recognize specific patterns or shapes within the data, such as graphs or diagrams. Engineers use data augmentation techniques to train the detection algorithm on a diverse range of shapes and patterns to ensure that it can accurately identify and classify all relevant data.
Once the OCR and detection algorithms are trained, they can accurately extract the relevant data from the PDFs, allowing engineers to gain insights into the geological and geophysical properties of the area. This information can be used to design and construct a more efficient, safer, and stable building.
The use of computer vision techniques to digitize borehole data is critical in modern construction practices, leading to identify potential issues or risks early on in the construction process, allowing engineers to take proactive measures to mitigate them.
Parichehr Behjati
PhD. Artificial Intelligence geotechnical, software engineer, geotechnical engineering software, construction AI, civil engineering software
