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Journal article
Digitalization of Analysis of a Concrete Block Layer Using Machine Learning as a Sustainable Approach
Sustainability, Vol.16(17), pp.1-31
2024
Abstract
The concrete block pavement (CBP) system has a surface layer consisting of concrete block pavers and joint sand over a bedding sand layer. The non-homogeneous nature of the surface course of CBP, along with different laying patterns and shapes of block pavers, makes the analysis of CBP cumbersome. In this study, the surface course of CBP was modeled based on the slab action of the block pavers and joint sand, which are connected together in full contact. Four different laying patterns, including herringbone, stretcher, parquet, and square, were modeled using a finite element model. The elastic moduli of the block pavers varied from 2500 MPa to 45,000 MPa, with thicknesses ranging from 60 mm to 120 mm. As a result, modeling of CBP based on slab action can be considered a realistic strategy. In addition, a dataset was created based on quantitative inputs, e.g., elastic modulus and thickness of the block pavers, and qualitative input, i.e., block laying patterns. The approaches of machine learning adopted were support vector regression, Gaussian process regression, single-layer and deep artificial neural networks, and least squares boosting to implement prediction approach based on input and output. The analyses of statistical accuracy of all five machine learning methods showed high accuracy; however, the Gaussian process and deep artificial neural network methods resulted in the most accurate outputs and are recommended for further studies. Based on the machine learning models, digitalization is achieved through the development of simple, user-friendly software for electronic devices in order to perform a preliminary analysis of different laying patterns of CBP. Such a platform may result in less laboratory work and boosts the level of sustainability in concrete block pavement technology.
Details
- Title
- Digitalization of Analysis of a Concrete Block Layer Using Machine Learning as a Sustainable Approach
- Authors
- Parviz Narimani (Author) - University of TehranMohsen Dehghanpour Abyaneh (Author) - Politecnico di TorinoMarzieh Golabchi (Author) - Politecnico di TorinoBabak Golchin (Author) - University of Mohaghegh ArdabiliRezwanul Haque (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringAli Jamshidi (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- Sustainability, Vol.16(17), pp.1-31
- Publisher
- MDPI AG
- Date published
- 2024
- DOI
- 10.3390/su16177591
- ISSN
- 2071-1050
- Copyright note
- © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Data Availability
- The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991058198902621
- Output Type
- Journal article
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