The use of concrete in civil infrastructure is highly demanded in structural and nonstructural elements. However, the high production of concrete could lead to severe pollution in the world. This pollution can be decreased using sustainable materials mixed with cement to obtain sustainable concrete. These sustainable materials include reinforcing fibers (e.g., steel, polypropylene, carbon fibers), recycled materials (e.g., tire rubber, crushed glass, plastic, industrial waste) as well as organic and inorganic elements as concrete aggregates and reinforcement elements. The sustainable construction materials can reduce the amount constitutive elements of concrete required for civil constructions. In addition, some sustainable materials added to cement could improve some properties of the concrete, like the compressive and flexural strength of concrete structural elements. Thus, the maintenance requirements or early replacement of these structural elements could be decreased. This review presents recent investigations about the performance of different sustainable concrete types. In addition, we include the effects on the mechanical properties of the concrete caused by the incorporation of several sustainable materials. In addition, recommendations for the use and testing of sustainable concrete are reported. These materials have potential applications in the sustainable concrete infrastructure in future smart cities.
In the educational field, reading comprehension is connected to learning achievement, and through it, one can interpret, retain, organize and value what has been read. It is an essential ability for the understanding and processing of information in learning. Furthermore, it is an essential skill to developing sustainable education. In this sense, sustainable development needs an advanced reading comprehension ability at elementary school in order to teach and learn future knowledge areas such as climate change, disaster risk reduction, biodiversity, poverty reduction, and sustainable consumption. Nevertheless, there have been few works focused on analyzing reading comprehension, particularly in Mexico, where the reference is the Programme for International Student Assessment (PISA) test on how well the Mexican students have developed this skill. Hence, this article shows the usefulness of employing Bayesian techniques in the analysis of reading comprehension at elementary school. The Bayesian network model allows for the determination of the language and communication level of achievement based on parameters such as learning style, learning pace, speed, and reading comprehension, obtaining an 85.36% precision. Moreover, the results confirm that teachers could determine changes in lesson planning and implement new pedagogical mechanisms to improve the level of learning and understanding contents.
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