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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.

This paper proposes a deep learning model for predicting the durability benchmark on concrete specimens. The durability benchmark on concrete samples is commonly estimated throughout the Ultrasonic Pulse Velocity measurements. This test establishes a relationship with concrete durability taken into consideration the material's homogeneity. The model proposed in this paper is feed by standard laboratory tests as input parameters, making the model a practical and efficient alternative to predict durability concrete benchmark, saving time, short-cut laboratory work, and avoiding sophisticated instrumentation use. Furthermore, it is an attractive alternative to the need for sophisticated instrumentation for estimating the Ultrasonic Pulse Velocity. The outcomes depict a high predictive accuracy about of 96% in the validation stage. In addition, the model was tested by a new dataset with different properties to demonstrate robustness and certainty in the model. Finally, the model achieves an impressive accuracy of 95.89% in the new validation dataset.