ConcreteXAI: A multivariate dataset for concrete strength prediction via deep-learning-based methods.

Concrete is a prominent construction material globally, owing to its reputed attributes such as robustness, endurance, optimal functionality, and adaptability. Formulating concrete mixtures poses a formidable challenge, mainly when introducing novel materials and additives and evaluating diverse design resistances. Recent methodologies for projecting concrete performance in fundamental aspects, including compressive strength, flexural strength, tensile strength, and durability (encompassing homogeneity, porosity, and internal structure), exist. However, actual approaches need more diversity in the materials and properties considered in their analyses. This dataset outlines the outcomes of an extensive 10-year laboratory investigation into concrete materials involving mechanical tests and non-destructive assessments within a comprehensive dataset denoted as ConcreteXAI. This dataset encompasses evaluations of mechanical performances and non-destructive tests. ConcreteXAI integrates a spectrum of analyzed mixtures comprising twelve distinct concrete formulations incorporating diverse additives and aggregate types. The dataset encompasses 18,480 data points, establishing itself as a cutting-edge resource for concrete analysis. ConcreteXAI acknowledges the influence of artificial intelligence techniques in various science fields. Emphatically, deep learning emerges as a precise methodology for analyzing and constructing predictive models. ConcreteXAI is designed to seamlessly integrate with deep learning models, enabling direct application of these models to predict or estimate desired attributes. Consequently, this dataset offers a resourceful avenue for researchers to develop high-quality prediction models for both mechanical and non-destructive tests on concrete elements, employing advanced deep learning techniques.

Effect of the Addition of Agribusiness and Industrial Wastes as a Partial Substitution of Portland Cement for the Carbonation of Mortars.

The present research work shows the effect on the carbonation of Portland cement-based mortars (PC) with the addition of green materials, specifically residues from two groups: agricultural and industrial wastes, and minerals and fibres. These materials have the purpose of helping with the waste disposal, recycling, and improving the durability of concrete structures. The specimens used for the research were elaborated with CPC 30R RS, according to the Mexican standard NMX-C-414, which is equivalent to the international ASTM C150. The aggregates were taken from the rivers Lerma and Huajumbaro, in the State of Michoacan, Mexico, and the water/cement relation was 1:1 in weight. The carbonation analyses were performed with cylinder specimens in an accelerated carbonation test chamber with conditions of 65 +/- 5% of humidity and 25 +/- 2 °C temperature. The results showed that depending on the PC substitutions, the carbonation front advance of the specimens can increase or decrease. It is highlighted that the charcoal ashes, blast-furnace slags, and natural perlite helped to reduce the carbonation advance compared to the control samples, consequently, they contributed to the durability of concrete structures. Conversely, the sugarcane bagasse ash, brick manufacturing ash, bottom ash, coal, expanded perlite, metakaolin, and opuntia ficus-indica dehydrated fibres additions increased the velocity of carbonation front, helping with the sequestration of greenhouse gases, such as CO2, and reducing environmental pollution.

Estimation of the main conditions in (SARS-CoV-2) Covid-19 patients that increase the risk of death using Machine learning, the case of Mexico.

Nowadays, society faces a catastrophic problem related to respiratory syndrome due to the coronavirus SARS-CoV-2: the Covid-19 disease. This virus has changed our coexistence rules and, in consequence, has reshaped the daily activities in modern societies. Thus, there are many efforts to understand the virus behaviour in order to reduce its negative impact, and these efforts produce an incredible amount of information and data sources every week. Data scientists, which use techniques such as Machine learning, are focusing their abilities to develop mathematical models for analysing this critical situation. This paper uses Machine Learning techniques as tools to help understand some specific new patterns in Covid patients that arise from unknown complex interactions in the transmission-dynamic models of the SARS-CoV-2 virus, and their relation with the corresponding social contact patterns which are often known or can be inferred from populations variables. One of the main motivations of this research is to find the diseases that cause an increase in the risk of death in infected people with the Covid-19 virus. Mexico is the case of study in this research. The general conditions of health that cause death are well known generally in the world. However, these conditions in each country can differ depending on different factors such as the general health status of people. The results show that the principal causes of death in Mexico are related to age, bad eating habits, chronic diseases, and contact with infected people having not proper care. Results from the analysis show a remarkable accuracy of 87%, which is considered satisfactory.