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Application of Artificial Neural Networks in Performance Prediction of Cement Mortars with Various Mineral Additives

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2023
bitstream_1207.pdf (750.3Kb)
Authors
Terzić, Anja
Pezo, Milada
Pezo, Lato
Article (Published version)
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Abstract
The machine learning technique for prediction and optimization of building material performances became an essential feature in the contemporary civil engineering. The Artificial Neural Network (ANN) prognosis of mortar behavior was conducted in this study. The model appraised the design and characteristics of seventeen either building or high-temperature mortars. Seven different cement types were employed. Seventeen mineral additives of primary and secondary origin were embedded in the mortar mixtures. Cluster Analysis and Principal Component Analysis designated groups of similar mortars assigning them a specific purpose based on monitored characteristics. ANN foresaw the quality of designed mortars. The impact of implemented raw materials on the mortar quality was assessed and evaluated. ANN outputs highlighted the high suitability level of anticipation, i.e., 0.999 during the training period, which is regarded appropriate enough to correctly predict the observed outputs in a wide ra...nge of processing parameters. Due to the high predictive accuracy, ANN can replace or be used in combination with standard destructive tests thereby saving the construction industry time, resources, and capital. Good performances of altered cement mortars are positive sign for widening of economical mineral additives application in building materials and making progress towards achieved carbon neutrality by reducing its emission.

Keywords:
Masonry Cements / High-temperature Cements / Industrial byproducts / Low-cost primary raw materials / Circular economy
Source:
Science of Sintering, 2023, 55, 1, 11-27
Funding / projects:
  • Ministry of Education, Science and Technological Development, Republic of Serbia, Grant no. 200012 (Istitute of Material Testing of Serbia - IMS, Belgrade) (RS-200012)

DOI: 10.2298/SOS2301011T

[ Google Scholar ]
URI
http://rims.institutims.rs/handle/123456789/499
Collections
  • Radovi istraživača / Researchers' publications
Institution/Community
Institut za ispitivanje materijala
TY  - JOUR
AU  - Terzić, Anja
AU  - Pezo, Milada
AU  - Pezo, Lato
PY  - 2023
UR  - http://rims.institutims.rs/handle/123456789/499
AB  - The machine learning technique for prediction and optimization of building material performances became an essential feature in the contemporary civil engineering. The Artificial Neural Network (ANN) prognosis of mortar behavior was conducted in this study. The model appraised the design and characteristics of seventeen either building or high-temperature mortars. Seven different cement types were employed. Seventeen mineral additives of primary and secondary origin were embedded in the mortar mixtures. Cluster Analysis and Principal Component Analysis designated groups of similar mortars assigning them a specific purpose based on monitored characteristics. ANN foresaw the quality of designed mortars. The impact of implemented raw materials on the mortar quality was assessed and evaluated. ANN outputs highlighted the high suitability level of anticipation, i.e., 0.999 during the training period, which is regarded appropriate enough to correctly predict the observed outputs in a wide range of processing parameters. Due to the high predictive accuracy, ANN can replace or be used in combination with standard destructive tests thereby saving the construction industry time, resources, and capital. Good performances of altered cement mortars are positive sign for widening of economical mineral additives application in building materials and making progress towards achieved carbon neutrality by reducing its emission.
T2  - Science of Sintering
T1  - Application of Artificial Neural Networks in Performance Prediction of Cement Mortars with Various Mineral Additives
EP  - 27
IS  - 1
SP  - 11
VL  - 55
DO  - 10.2298/SOS2301011T
ER  - 
@article{
author = "Terzić, Anja and Pezo, Milada and Pezo, Lato",
year = "2023",
abstract = "The machine learning technique for prediction and optimization of building material performances became an essential feature in the contemporary civil engineering. The Artificial Neural Network (ANN) prognosis of mortar behavior was conducted in this study. The model appraised the design and characteristics of seventeen either building or high-temperature mortars. Seven different cement types were employed. Seventeen mineral additives of primary and secondary origin were embedded in the mortar mixtures. Cluster Analysis and Principal Component Analysis designated groups of similar mortars assigning them a specific purpose based on monitored characteristics. ANN foresaw the quality of designed mortars. The impact of implemented raw materials on the mortar quality was assessed and evaluated. ANN outputs highlighted the high suitability level of anticipation, i.e., 0.999 during the training period, which is regarded appropriate enough to correctly predict the observed outputs in a wide range of processing parameters. Due to the high predictive accuracy, ANN can replace or be used in combination with standard destructive tests thereby saving the construction industry time, resources, and capital. Good performances of altered cement mortars are positive sign for widening of economical mineral additives application in building materials and making progress towards achieved carbon neutrality by reducing its emission.",
journal = "Science of Sintering",
title = "Application of Artificial Neural Networks in Performance Prediction of Cement Mortars with Various Mineral Additives",
pages = "27-11",
number = "1",
volume = "55",
doi = "10.2298/SOS2301011T"
}
Terzić, A., Pezo, M.,& Pezo, L.. (2023). Application of Artificial Neural Networks in Performance Prediction of Cement Mortars with Various Mineral Additives. in Science of Sintering, 55(1), 11-27.
https://doi.org/10.2298/SOS2301011T
Terzić A, Pezo M, Pezo L. Application of Artificial Neural Networks in Performance Prediction of Cement Mortars with Various Mineral Additives. in Science of Sintering. 2023;55(1):11-27.
doi:10.2298/SOS2301011T .
Terzić, Anja, Pezo, Milada, Pezo, Lato, "Application of Artificial Neural Networks in Performance Prediction of Cement Mortars with Various Mineral Additives" in Science of Sintering, 55, no. 1 (2023):11-27,
https://doi.org/10.2298/SOS2301011T . .

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