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- W4211078084 abstract "Consuming a huge proportion of raw materials and resources, the construction industry could significantly affect the environment. In addition, the production of cement, the most widely used construction material in the world, causes CO 2 greenhouse gas emissions to a serious environmental challenge. The aforementioned problems have triggered researchers to modify the traditional construction materials in parallel to produce the new generation of high-performance materials. In this way, detecting material properties and mixture proportioning are two main approaches to develop high-performance construction materials. For the former, instead of the time-consuming and expensive experimental tests, we can develop predictive models to estimate material properties. For the latter, we have to take care of different objectives such as the cost of mixture design, its mechanical properties, workability, durability, and environmental sustainability. This is formulated as a nonlinear multiobjective optimization problem that cannot be solved by classical methods. These two concerns appear in the initial stage of construction. In the service life of structures, damages should be detected with a high level of certainty for effective rehabilitation. The recent advances in machine learning give us a rich set of tools such as artificial neural networks, evolutionary algorithms, and support vector machines to address the above challenges. In this chapter, we will cover the main results of this line of work." @default.
- W4211078084 created "2022-02-13" @default.
- W4211078084 creator A5031160941 @default.
- W4211078084 creator A5043481897 @default.
- W4211078084 creator A5063680574 @default.
- W4211078084 date "2022-01-01" @default.
- W4211078084 modified "2023-10-14" @default.
- W4211078084 title "Machine learning applications for developing sustainable construction materials" @default.
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- W4211078084 doi "https://doi.org/10.1016/b978-0-323-90508-4.00002-2" @default.
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