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Slaked lime is traditionally a suitable material for use in construction activities in historic and traditional buildings whose constitutive characteristics demand compatible and appropriate material solutions. Therefore, knowing the physical and mechanical characterization of lime pastes and mortars for use in restoration is considered an important step in the process of scientific development of a material that, in the Mexican case, has lacked scientific rigor in its production, use and commercialization. With this in mind, the present article aims to investigate the characteristics of lime pastes and mortars that have been used in the traditional way and the new limes that exist in the market, offering suitable options for restoration, for which an integrated methodology was used consisting of physical tests such as surface area, electrical resistivity, pulse velocity, colorimetry, and mechanical tests such as compressive strength, giving as a result that “traditional slaked” lime is more likely to deteriorate than powdered lime, which leads us to conclude that powdered hydrated lime has optimal characteristics for restoration activities.

The State of Michoacan has a wide and rich architectural heritage which is represented by different vernacular typologies which employ different materials and techniques according to the local resources. However, this wide knowledge and cultural heritage are disappearing, with several monuments and buildings being abandoned due to new dynamics and social changes. The traditional techniques have been substituted for modern and industrial construction systems since they are associated with obsolete ways of life. Through international cooperation for development and research projects, many activities were carried out to analyze the durability of local materials and vernacular constructions of the state. The main ones included the architectural survey and inspection of these buildings, photographic and drone surveys, characterization in the laboratory and oral interviews with local users. During the field work, these interviews with the local population, the creation of multimedia content and the use of audiovisual methods had crucial importance in the documentation of this local knowledge and culture, being great methods for scientific communication and dissemination. The main product is the documentary Xirangua, which means ‘roots’ in the Purépecha language, the main indigenous ethnicity in the state. This documentary explores the traditional architecture and construction of Michoacan, showing the reality within the loss of this vernacular heritage and portraying the importance of its conservation through the perspective of the local population, users and artisans. The use of these audiovisual techniques has demonstrated to be a suitable option to promote and preserve cultural heritage and educate, include the local actors in the process, and make the general public aware of the current issues, with this being one of the first works which explores this approach.

The freight transport industry in Mexico has grown significantly since the establishment of trade agreements in North America, which has brought significant environmental consequences to the main transport corridors. This paper proposes a methodology for the estimation of emissions for freight vehicles on road transportation corridors. The variables included in this analysis allow adequate characterization of the conditions of the vehicle fleet, the geometry and the quality of the road, the environment, and the average annual daily traffic (AADT) of heavy vehicles. The results were structured to show two indicators, the amount of CO2 emissions per kilometer and the amount of emissions per tonne transported. These results will allow establishing a baseline of CO2 emissions through which we can implement actions in the road transport sector to reduce greenhouse gases (GHG) to mitigate climate change and develop parameter values for use in Cost Benefit Analysis. The indicators can also be applied to geospatial modeling of emissions in road transport corridors and forecast its growth.

Adobe masonry is one of the oldest construction systems still in use today, Mexico has an enormous cultural heritage with traditional adobe houses being very representative of the rural communities and their culture. The 2017 Puebla Earthquake on September 19th struck the country causing the loss, destruction, and damage of historic buildings in several Mexican states, with the traditional earthen dwellings being the most vulnerable structures to these events. The fast abandonment of the local materials and techniques entails further research regarding the characterization of these construction systems, therefore, reconstruction efforts first require the recovery of the construction technique. After the seismic events, adobe samples of the remaining adobe structures of Jojutla de Juarez were collected. This population was one of the most affected in all the country, and, because of the major losses suffered, the study was conducted to determine the material properties of the dwellings’ adobe shards and natural quarry clays of the region. The characterization included destructive and non-destructive tests, mineralogical and granulometry analyses, and composition of the adobe samples of the buildings, as well as the aggregates. As a novelty, the compressive strength of the pieces was tested by two methods: the traditional compression strength test and the point-load test, in order to obtain the indicative values and the correlation equations between both tests. From the formal analysis and the laboratory, it was observed that the adobes from Jojutla presented different compositions which combined with the building malpractices and alterations to the traditional systems caused unpredictable behavior during the earthquake. The conduction of point-load tests in situ, as a part of a complete characterization methodology, could be an alternative to study the mechanical properties of patrimonial or damaged building samples before its disappearance.

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.

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.

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.

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.

This study takes a unique approach, using satellite remote sensing to analyze the land surface temperature (LST) in seven coastal cities located at latitudes between 18° and 22° and longitudes between 106° and 91°. The methodology consists of obtaining the LST on two selected days, one in July 2019 and one in July 2020, to determine how the temperature was modified by the atypical mobility caused by Coronavirus Disease 2019 (COVID-19) and social distancing. Using these data, we determine the representative surface temperature that tended to rise (RHST) and the representative surface temperature that tended to decrease (RLST), depending on the affected area. This approach allows us to comprehensively compare how mobility modified the four variables studied: territorial extension, population, types of soil (paving), and vegetation. We concluded that, among the factors analyzed, the types of paving and vegetation were those that generated a more significant decrease in temperature; the variables of territorial extension and the number of inhabitants had a smaller impact. This study paves the way for a discussion of the significant influence of mobility on the behavior of the LST.

There are many different ways to estimate whether a concrete structure will be suffering corrosion degradation as experimental processes and numerical estimations relied on theoretical equations. Nevertheless, these estimations require higher certainty and satisfactory estimation accuracy. Deep Learning is causing profound changes in society. It has inspired unprecedented advances in historically challenging problems. This paper proposes a mathematician approach. A Deep Learning model capable of classifying the risk of corrosion on concrete specimens relied on a standard methodology. It employs the Electrical resistivity measurement for achieving this goal. The deep neural network architecture presented in this study displays a merging of activation functions. Also, a regularized method to avoid overfitting problems was implemented. The model comprises different standard tests as input data and classification of the corrosion risk as output. The outcomes depict an accuracy of about 98%, given the nature of the stochastic learning algorithm. Different concrete samples were analyzed in this research in order to provide data variety to the algorithm and accomplish a robust and stable model.

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.

Anthropogenic heat (QF) is one of the parameters that contributes to the urban heat island (UHI) phenomenon. Usually, this variable is studied holistically, among other anthropogenic flux such as industrial, vehicular, buildings, and human metabolism, due to the complexity of data collection through field measurements. The aim of this paper was to weigh vehicular anthropogenic heat and its impact on the thermal profile of an urban canyon. A total of 108 simulations were carried out, using the ANSYS Fluent® software, incorporating variables such as the number of vehicles, wind speed, urban canyon orientation, and urban canyon aspect ratio. The results were compared with a database of 61 American cities in 2015 and showed that orientation is the main factor of alteration in vehicular heat flow, increasing it in a range of 2 °C to 6.5 °C, followed by the wind speed (1.2 to 2.2 m/s), which allows for decreases of 1 to 3.8 °C. The exploration of these variables and their weighing in the definition of urban street canyon temperature profiles at the canopy level of urban structures provides valuable information on the hygrothermal comfort of its inhabitants; its appropriate quantification can be an example of many urban energy balances altering processes.