# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Model Deployment and Accessibility  
To maximize the practical utility of this research for geotechnical engineers and researchers, the uncertainty quantification system has been deployed as a publicly accessible web application, fundamentally transforming academic research into an immediately available professional tool. This implementation represents a crucial bridge between advanced machine learning research and day-to-day engineering practice, ensuring that the sophisticated uncertainty quantification capabilities developed in this study directly benefit the professional community rather than remaining confined to academic publications.  
The deployment strategy reflects a comprehensive understanding of how engineering professionals work and the barriers they face when attempting to utilize advanced analytical techniques. Traditional machine learning implementations often require specialized programming knowledge, specific software installations, or substantial computational resources that may not be readily available to all practitioners. By creating a web-based solution, we eliminate these barriers entirely, enabling even individual consultants and smaller firms to access state-of-the-art uncertainty quantification capabilities through nothing more than a standard web browser.  
The deployment architecture utilizes a modern, robust approach centered on FastAPI, a high-performance web framework specifically chosen for its efficiency in handling prediction requests and its ability to scale with user demand. The trained Random Forest models--both the primary prediction model and the uncertainty estimation model--were serialized and integrated into a comprehensive RESTful API service. This service processes the three input parameters (cement percentage, curing period, and compaction velocity) and returns not only central UCS predictions but complete uncertainty information including confidence intervals at multiple levels, all computed and delivered in real time.  
This architectural choice ensures both computational efficiency and scalability for multiple concurrent users while maintaining the full sophistication of the uncertainty quantification framework. The system operates efficiently on standard web servers using CPU-based processing, deliberately avoiding GPU dependencies that might complicate deployment or increase operational costs. Response times are optimized to support real-time decision-making during design sessions and field consultations, making the tool practical for integration into existing engineering workflows without disrupting established practices.  
The web interface, accessible at [[http://www.bite-at.tuiasi.ro/ucs-prediction/](http://www.bite-at.tuiasi.ro/ucs-prediction/)]([http://www.bite-at.tuiasi.ro/ucs-prediction/](http://www.bite-at.tuiasi.ro/ucs-prediction/)), provides an intuitive user experience that requires no specialized software or machine learning expertise from practitioners. Engineers simply input the three key stabilization parameters and immediately receive comprehensive predictions including central UCS estimates, uncertainty bounds, and confidence intervals at 68%, 80%, 90%, and 95% levels. The interface incorporates intelligent value range validation based on the experimental bounds, clear input guidance that explains the expected parameter ranges, and immediate visualization of prediction results with uncertainty bands that enhance interpretability and practical utility.