An explanatory machine learning model for forecasting compressive strength of high-performance concrete-Reference-Cited by-同舟云学术

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An explanatory machine learning model for forecasting compressive strength of high-performance concrete

Published:2023-09-07 Issue: Volume: Page:
ISSN:2520-8160
Container-title:Multiscale and Multidisciplinary Modeling, Experiments and Design
language:en
Short-container-title:Multiscale and Multidiscip. Model. Exp. and Des.

Author:

Yan Guifeng,Wu Xu,Zhang Wei,Bao Yuping

Funder

This work was supported by the Development and Application of an Intelligent Park Operation Management System based on BIM Technology

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Mechanics of Materials,General Materials Science

Link

https://link.springer.com/content/pdf/10.1007/s41939-023-00225-1.pdf

Reference33 articles.

1. Aïtcin P-C (1998) High performance concrete. CRC Press

2. Akopyan AV (2015) Geometry of the Cardioid. Am Math Mon 122:144–150

3. Alshihri MM, Azmy AM, El-Bisy MS (2009) Neural networks for predicting compressive strength of structural light weight concrete. Constr Build Mater 23:2214–2219

4. Benemaran RS, Esmaeili-Falak M (2020) Optimization of cost and mechanical properties of concrete with admixtures using MARS and PSO. Comput Concr 26:309–316. https://doi.org/10.12989/cac.2020.26.4.309

5. Bharatkumar B, Narayanan R, Raghuprasad B, Ramachandramurthy D (2001) Mix proportioning of high performance concrete. Cem Concr Compos 23:71–80. https://doi.org/10.1016/S0958-9465(00)00071-8

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