The process of concrete production involves mixing cement, water, and other materials. The quantity of each of these materials results in a performance that is particularly estimated in terms of compressive or flexural strength. It has been observed that the final performance of concrete has a high variance and that traditional formulation methods do not guarantee consistent results. Consequently, designs tend to be over-designed, generating higher costs than required, to ensure the performance committed to the client. This study proposes the construction of predictive machine learning models to estimate compressive or flexural strength and concrete slump. The study was carried out following the Team Data Science Process (TDSP) methodology, using a dataset generated by the Colombian Ready Mix (RMX) company Cementos Argos S.A. over five years, containing the quantity of materials used for different concrete mixes, as well as performance metrics measured in the laboratory. Predictive models such as XGBoost and neural networks were trained, and hyperparameter tuning was performed using advanced techniques such as genetic algorithms to obtain three models with high performance for estimating compressive strength, flexural strength, and slump. This study concludes that it is possible to use machine learning techniques to design reliable concrete mixes that, when combined with traditional analytical methods, could reduce costs and minimize over-designed concrete mixes.

This study presents a methodology to optimize concrete mixtures by integrating machine learning (ML) and genetic algorithms. ML models are used to predict compressive strength, while genetic algorithms optimize the mixture cost under quality constraints. Using a dataset of over 19,000 samples from a local ready-mix concrete producer, various predictive ML models were trained and evaluated regarding cost-effective solutions. The results show that the optimized mixtures meet the desired compressive strength range and are cost-efficient, thus having $50\%$ of the solutions yielding a cost below $98\%$ of the test cases. CatBoost emerged as the best ML technique, thereby achieving a mean absolute error (MAE) below 5 MPa. This combined approach enhances quality, reduces costs, and improves production efficiency in concrete manufacturing.

Landmine victims require an engaging and immersive rehabilitation process to maintain motivation and therapeutic adherence, such as virtual reality games. This paper proposes a virtual reality exercise game called Exogames, which works with Nukawa, a lower limb rehabilitation robot (LLRR). Together, they constitute the general Kina system. The design and development process of Exogames is reported, as well as the evaluation of its potential for physical and emotional rehabilitation. In an initial survey designed ad-hoc, 13 health professionals evaluated compliance with various requirements. They agreed that Exogames would help the user focus on rehabilitation by providing motivation; 92.3% said that the user will feel safe in the virtual world, 66.7% of them agreed or totally agreed that it presents characteristics that may enhance the physical rehabilitation of lower limbs for amputees, 83.3% stated that it would promote the welfare of landmine victims, and 76.9% responded that the graphical interface and data report are useful for real-time assessment, and would be helpful for four interventional areas in all rehabilitation stages. In a second evaluation, using standardized surveys, five physical therapists and one lower limb amputee tried the Kina system as users. They filled out the System Usability Scale (SUS), the Physical Activity Enjoyment Scale (PACES), and the Game Experience Questionnaire (GEQ). The usability of the Kina system overall score was 69 (66, 79) out of 100, suggesting an acceptable though improvable usability. The overall PACES score of 110 (108, 112) out of 126 suggests that users enjoyed the game well. Finally, users indicated a positive effect with a good sense of immersion and smooth of gameplay during the tests, as indicated by the GEQ results. In summary, the evaluations showed that Exogames has the potential to be used as a virtual reality game for the physical and emotional rehabilitation of landmine victims.

Hydropower plants generate large volumes of data with high-dimensional time series, making early anomaly detection essential for monitoring, preventive maintenance and cost reduction. This study addresses the challenge of detecting anomalies in real time without labeled failure data by proposing an unsupervised approach that combines time series clustering, autoencoder-based models, and an adaptative anomaly thresholding. Initially, a clustering process is applied to historical time series data from multiple sensors in a hydroelectric power plant to identify groups of variables with similar temporal dynamics. Subsequently, for each cluster, various unsupervised models are trained to learn the normal behavior of the variables, including ARIMA, Autoencoders, Variational Autoencoders, Long Short-Term Memory networks, Sliding-window Autoencoders and Sliding-window Variational Autoencoders. Among these, the Autoencoder model demonstrated superior performance and was selected for real-time deployment. Finally, anomalies were detected by comparing predicted and actual values, using an adaptative threshold based on prediction errors. The system was tested on a real hydropower plant with over 150 time-dependent variables. The results show that 97% of the variables achieved an R² score above 0.8, with low MAE values indicating high reconstruction accuracy. The proposed approach, deployed in a real-time system integrated with Grafana dashboards, demonstrates the system’s capability to detect anomalies.