Optimal matching measurement of AI based field surveys using deep learning network and smart monitoring

This research introduces an innovative method for targetless displacement measurement of reinforced soil retaining walls, employing an optimal AI deep learning network in conjunction with advanced smart monitoring technologies. Conventional displacement measurement techniques often rely on physical targets, which can introduce inaccuracies and complicate real-time internet big data collection. Our approach eliminates the need for these targets by utilizing a AI deep learning framework that processes high-dimensional sensor data to accurately detect and quantify displacements by digital platform. By optimizing the AI deep learning network architecture, we enhance the model's ability to learn complex patterns associated with soil-structure interactions with AI knowledge management. Field experiments validate the efficacy of our method, demonstrating significant improvements in measurement precision and responsiveness. The findings indicate that this targetless technique not only streamlines the monitoring process but also provides critical insights into the dynamic behavior of AI based field surveys under varying environmental and load conditions. This advancement has substantial implications for the design, safety, and maintenance based on geotechnical infrastructures.