> For the complete documentation index, see [llms.txt](https://2022.istvs.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://2022.istvs.org/papers/4243.md). # 4243 / Soil Compaction Monitoring Technique Using Deep Learning Title: Soil Compaction Monitoring Technique Using Deep Learning Authors: Shota Teramoto and Taizo Kobayashi Abstract: It is commonly known that the dynamic behavior of a vibratory drum of a soil compaction machine changes with soil stiffness. Although real-time monitoring techniques of compaction quality by measuring the acceleration of the vibratory drum have already been put into practice use, their applicability depends on the soil type and condition. In this study, to extend the range of applicability and improve accuracy, we propose a deep learning-based technique that allows the regression estimation of soil stiffness from the acceleration responses of a vibration drum. To collect a large amount of noise-free training data, the acceleration responses of a vibratory drum were simulated by numerically solving the equations of the motion mass-spring-damper system. We also conducted a field experiment to verify the proposed technique. The experimental results show that the estimated values of soil stiffness correlate with the measured values, with the correlation coefficient of approximately 0.79. Thus, the proposed method has potential as a new real-time monitoring technique for soil compaction quality. Order the full paper: ISTVS members: receive three papers per year as part of your membership via the ISTVS Member Portal: --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://2022.istvs.org/papers/4243.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.