AI-Based Digitization of Legacy Ground Information

Abstract

Geotechnical characterization of site materials is of paramount importance in the construction and mining industry. The analysis of large volumes of geotechnical information from multiple sources leads to data-driven decisions that help to minimize uncertainty. For this purpose, a unified digital information platform becomes handy to have a global perspective and improve the analysis of available ground information data. Access to historic ground investigation data from previous projects during the project planning stage might increase efficiency. However, accessing and processing legacy data from companies’ databases is time and resources consuming. In the recent years, software tools that are capable of extracting data in a digital format from images have become popular, but still require human-supervised interpretation. A novel tool combining Optical Character Recognition (OCR), digital data extraction technologies and AI-based data interpretation system is presented herein. The state-of-the-art OCR technology is capable of accurately recognizing and extracting text from various document types, such as scanned documents, images, and PDFs. It utilizes advanced machine learning algorithms to process text, even in challenging conditions, ensuring data is extracted accurately and reliably. Then, a data interpretation system has been trained to identify the type of site characterization data and its structure while retrieving all the content in a digital format. All components work seamlessly together to provide a comprehensive solution for automating the interpretation and extraction of site characterization data, streamlining data management and analysis processes. The capability of gathering data from multiple sources in a unique ground information system provides valuable information for planning and design stages while decreasing costs, time and uncertainties. In addition, all these data are then available within DAARWIN platform to feed the ground model workflow.

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