ML-Based Computational Model to Estimate the Compressive Strength of Sustainable Concrete Integrating Silica Fume and Steel Fibers
Sahota, Sarvanshdeep Singh ; Arora, Harish Chandra ; Kumar, Aman ; Kumar, Krishna LU
and Rai, Hardeep Singh
(2023)
5th International Conference on Information Systems and Management Science, ISMS 2022
In Lecture Notes in Networks and Systems
671 LNNS.
p.231-244
- Abstract
Concrete is one of the most commonly used construction material on the earth after water. The compressive strength of concrete is an important parameter and is considered in all structural designs. Production of the cement is directly proportional to carbon emissions. The cement content in the concrete can be partially replaced with waste materials like steel fibers, silica fumes, etc. Calculating compressive strength in a laboratory takes huge amount of time, manpower, cost and produces a large amount of wastage. Apart from the constituents of concrete, the compressive strength also depends on various factors such as temperature, mixing, types of aggregate, and quality of the water. The analytical models failed to deal with difficult... (More)
Concrete is one of the most commonly used construction material on the earth after water. The compressive strength of concrete is an important parameter and is considered in all structural designs. Production of the cement is directly proportional to carbon emissions. The cement content in the concrete can be partially replaced with waste materials like steel fibers, silica fumes, etc. Calculating compressive strength in a laboratory takes huge amount of time, manpower, cost and produces a large amount of wastage. Apart from the constituents of concrete, the compressive strength also depends on various factors such as temperature, mixing, types of aggregate, and quality of the water. The analytical models failed to deal with difficult problems. Artificial intelligence has enough capabilities to deal with such kind of complex problems. In this work, an artificial neural network (ANN) based model has been developed to predict the compressive strength of steel fiber and silica fumes-based concrete. The R-value of the developed model is 0.9948 and the mean absolute percentage error is 5.47%. The mean absolute error and root mean square error of the proposed model is 1.73 MPa and 6.89 MPa, respectively. The developed model is easy to use and reliable to estimate the compressive strength of concrete incorporating silica fumes and steel fibers.
(Less)
- author
- Sahota, Sarvanshdeep Singh
; Arora, Harish Chandra
; Kumar, Aman
; Kumar, Krishna
LU
and Rai, Hardeep Singh
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- ANN, Compressive Strength, Silica fume, Steel fiber
- host publication
- Key Digital Trends Shaping the Future of Information and Management Science - Proceedings of 5th International Conference on Information Systems and Management Science, ISMS 2022
- series title
- Lecture Notes in Networks and Systems
- editor
- Garg, Lalit ; Sisodia, Dilip Singh ; Kesswani, Nishtha ; Vella, Joseph G. ; Brigui, Imene ; Misra, Sanjay and Singh, Deepak
- volume
- 671 LNNS
- pages
- 14 pages
- publisher
- Springer Science and Business Media B.V.
- conference name
- 5th International Conference on Information Systems and Management Science, ISMS 2022
- conference location
- Msida, Malta
- conference dates
- 2022-10-06 - 2022-10-09
- external identifiers
-
- scopus:85161144003
- ISSN
- 2367-3370
- 2367-3389
- ISBN
- 9783031311529
- DOI
- 10.1007/978-3-031-31153-6_20
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- id
- 91760dfd-1752-4f02-94d4-46bf7386d8cf
- date added to LUP
- 2024-04-15 13:47:16
- date last changed
- 2024-05-16 14:46:50
@inproceedings{91760dfd-1752-4f02-94d4-46bf7386d8cf,
abstract = {{<p>Concrete is one of the most commonly used construction material on the earth after water. The compressive strength of concrete is an important parameter and is considered in all structural designs. Production of the cement is directly proportional to carbon emissions. The cement content in the concrete can be partially replaced with waste materials like steel fibers, silica fumes, etc. Calculating compressive strength in a laboratory takes huge amount of time, manpower, cost and produces a large amount of wastage. Apart from the constituents of concrete, the compressive strength also depends on various factors such as temperature, mixing, types of aggregate, and quality of the water. The analytical models failed to deal with difficult problems. Artificial intelligence has enough capabilities to deal with such kind of complex problems. In this work, an artificial neural network (ANN) based model has been developed to predict the compressive strength of steel fiber and silica fumes-based concrete. The R-value of the developed model is 0.9948 and the mean absolute percentage error is 5.47%. The mean absolute error and root mean square error of the proposed model is 1.73 MPa and 6.89 MPa, respectively. The developed model is easy to use and reliable to estimate the compressive strength of concrete incorporating silica fumes and steel fibers.</p>}},
author = {{Sahota, Sarvanshdeep Singh and Arora, Harish Chandra and Kumar, Aman and Kumar, Krishna and Rai, Hardeep Singh}},
booktitle = {{Key Digital Trends Shaping the Future of Information and Management Science - Proceedings of 5th International Conference on Information Systems and Management Science, ISMS 2022}},
editor = {{Garg, Lalit and Sisodia, Dilip Singh and Kesswani, Nishtha and Vella, Joseph G. and Brigui, Imene and Misra, Sanjay and Singh, Deepak}},
isbn = {{9783031311529}},
issn = {{2367-3370}},
keywords = {{ANN; Compressive Strength; Silica fume; Steel fiber}},
language = {{eng}},
pages = {{231--244}},
publisher = {{Springer Science and Business Media B.V.}},
series = {{Lecture Notes in Networks and Systems}},
title = {{ML-Based Computational Model to Estimate the Compressive Strength of Sustainable Concrete Integrating Silica Fume and Steel Fibers}},
url = {{http://dx.doi.org/10.1007/978-3-031-31153-6_20}},
doi = {{10.1007/978-3-031-31153-6_20}},
volume = {{671 LNNS}},
year = {{2023}},
}