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.

Reinforced Concrete Structure Performance in Marine Structures: Analyzing Durability Indexes to Obtain More Accurate Corrosion Initiation Time Predictions.

This paper presents a comparison of six index properties collected during durability inspections of five Mexican seaports. Typical durability indicators such as compressive strength, saturated electrical resistivity, ultrasonic pulse velocity, percent total void content, capillary porosity, and chloride concentration profiles were analyzed to obtain empirical correlations with the non-steady-state chloride diffusion coefficient. These indices were compared to determine correlation coefficients that are the most important for obtaining better corrosion initiation forecasting. Two models of corrosion initiation time (ti) were used: Fick's second law of diffusion and the reported UNE-83994-2 (Spanish Association for Standardization, UNE) in which electrical resistivity was used to calculate concrete service life. The data from both models were cleaned using correlated variables, and the initial variables were compared with ti. The main result achieved was the verification of the feasibility of using correlations of variables to clean unnecessary data in order to calculate ti. Additionally, electrical resistivity was identified as one of the main durability indexes for in-service concrete structures exposed to marine environments. This is important because electrical resistivity is a non-destructive and reliable test that can be measured both in the laboratory and in the field very easily.

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.

Asphalt Mixes Processed with Recycled Concrete Aggregate (RCA) as Partial Replacement of the Natural Aggregate.

Materials play a fundamental role in any branch of civil engineering. From ancient times to the present day, society has required enormous amounts of construction materials, which implies an excessive exploitation of the natural environment. The present research work consisted in the design and development of asphalt mixes with a partial substitution of the natural aggregate (NA) by means of recycled concrete aggregate (RCA). The mix was designed with the Marshall methodology, considering the next percentages of substitution and addition by mass: 90% NA and 10% RCA; 80% NA and 20% RCA; 70% NA and 30% RCA. The mixtures were elaborated and analysed under the international standards and the Mexican regulation of the Communications and Transport Ministry, to determine the best option regarding their performance. The materials were characterized according to the current regulations and later employed in the mixes design. A total of 38 specimens were elaborated for each mixture, determining the optimum asphalt content; after that, mechanical tests were performed to analyse and determine the best results. In the aftermath of the examination of all mixtures, we concluded that the 70%AN/30%RCA is the best alternative option according to its performance and numeric results, complying with the cited regulations, and allowing a lower content of asphalt during the process.