Prediction of Compressive Strength of Ultra-High-Performance Concrete Using Machine Learning Algorithms—SFS and ANN

2020 ◽  
pp. 17-27
Author(s):  
Deepak Choudhary ◽  
Jaishanker Keshari ◽  
Imran Ahmed Khan
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
High Performance ◽  
High Performance Concrete ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Ultra High Performance Concrete

scholarly journals Predicting Ultra-High-Performance Concrete Compressive Strength Using Tabular Generative Adversarial Networks

Materials ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 4757
Author(s):  
Afshin Marani ◽  
Armin Jamali ◽  
Moncef L. Nehdi
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Compressive Strength ◽  
High Performance ◽  
High Performance Concrete ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Ultra High Performance Concrete ◽  
Adversarial Networks ◽  
Insight Into

There have been abundant experimental studies exploring ultra-high-performance concrete (UHPC) in recent years. However, the relationships between the engineering properties of UHPC and its mixture composition are highly nonlinear and difficult to delineate using traditional statistical methods. There is a need for robust and advanced methods that can streamline the diverse pertinent experimental data available to create predictive tools with superior accuracy and provide insight into its nonlinear materials science aspects. Machine learning is a powerful tool that can unravel underlying patterns in complex data. Accordingly, this study endeavors to employ state-of-the-art machine learning techniques to predict the compressive strength of UHPC using a comprehensive experimental database retrieved from the open literature consisting of 810 test observations and 15 input features. A novel approach based on tabular generative adversarial networks was used to generate 6513 plausible synthetic data for training robust machine learning models, including random forest, extra trees, and gradient boosting regression. While the models were trained using the synthetic data, their ability to generalize their predictions was tested on the 810 experimental data thus far unknown and never presented to the models. The results indicate that the developed models achieved outstanding predictive performance. Parametric studies using the models were able to provide insight into the strength development mechanisms of UHPC and the significance of the various influential parameters.

scholarly journals Ultra-high-performance concrete with local high unburned carbon fly ash

DYNA ◽  
2021 ◽  
Vol 88 (216) ◽  
pp. 38-47
Author(s):  
Joaquín Abellán García ◽  
Nancy Torres Castellanos ◽  
Jaime Antonio Fernandez Gomez ◽  
Andres Mauricio Nuñez Lopez
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
High Performance ◽  
High Performance Concrete ◽  
Unburned Carbon ◽  
High Tech ◽  
Ultra High Performance Concrete ◽  
Conventional Concrete ◽  
The Cost

Ultra-high-performance concrete (UHPC) is a kind of high-tech cementitious material with superb mechanical and durability properties compared to other types of concrete. However, due to the high content of cement and silica fume used, the cost and environmental impact of UHPC is considerably higher than conventional concrete. For this reason, several efforts around the world have been made to develop UHPC with greener and less expensive local pozzolans. This study aimed to design and produce UHPC using local fly ash available in Colombia. A numerical optimization, based on Design of Experiments (DoE) and multi-objective criteria, was performed to obtain a mixture with the proper flow and highest compressive strength, while simultaneously having the minimum content of cement. The results showed that, despite the low quality of local fly ashes in Colombia, compressive strength values of 150 MPa without any heat treatment can be achieved.

scholarly journals Mechanical Properties and Environmental Evaluation of Ultra-High-Performance Concrete with Aeolian Sand

Materials ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3148 ◽  
Author(s):  
Hongyan Chu ◽  
Fengjuan Wang ◽  
Liguo Wang ◽  
Taotao Feng ◽  
Danqian Wang
Keyword(s):  
Compressive Strength ◽  
Environmental Impact ◽  
Young’S Modulus ◽  
High Performance ◽  
High Performance Concrete ◽  
Young's Modulus ◽  
Ultra High Performance Concrete ◽  
Aeolian Sand ◽  
Binder Ratio ◽  
Water Binder Ratio

Ultra-high-performance concrete (UHPC) has received increasing attention in recent years due to its remarkable ductility, durability, and mechanical properties. However, the manufacture of UHPC can cause serious environmental issues. This work addresses the feasibility of using aeolian sand to produce UHPC, and the mix design, environmental impact, and mechanical characterization of UHPC are investigated. We designed the mix proportions of the UHPC according to the modified Andreasen and Andersen particle packing model. We studied the workability, microstructure, porosity, mechanical performance, and environmental impact of UHPC with three different water/binder ratios. The following findings were noted: (1) the compressive strength, flexural strength, and Young’s modulus of the designed UHPC samples were in the ranges of 163.9–207.0 MPa, 18.0–32.2 MPa, and 49.3–58.9 GPa, respectively; (2) the compressive strength, flexural strength, and Young’s modulus of the UHPC increased with a decrease in water/binder ratio and an increase in the steel fibre content; (3) the compressive strength–Young’s modulus correlation of the UHPC could be described by an exponential formula; (4) the environmental impact of UHPC can be improved by decreasing its water/binder ratio. These findings suggest that it is possible to use aeolian sand to manufacture UHPC, and this study promotes the application of aeolian sand for this purpose.

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