Digitalization of Analysis of a Concrete Block Layer Using Machine Learning as a Sustainable Approach

Parviz Narimani, Mohsen Dehghanpour Abyaneh, Marzieh Golabchi, Babak Golchin, Rezwanul Haque and Ali Jamshidi ()
Additional contact information
Parviz Narimani: School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran 7787131587, Iran
Mohsen Dehghanpour Abyaneh: Department of Mechanical and Aerospace Engineering (DIMEAS), Politechnico Di Torino, 10129 Torino, Italy
Marzieh Golabchi: Department of Energy (DENERG), Politechnico Di Torino, 10129 Torino, Italy
Babak Golchin: Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
Rezwanul Haque: School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia
Ali Jamshidi: School of Science, Technology and Engineering, University of the Sunshine Coast, Sippy Downs, QLD 4556, Australia

Sustainability, 2024, vol. 16, issue 17, 1-31

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.

Keywords: concrete block pavement (CBP); stress analysis; machine learning; artificial neural network; digitalization; digital transformation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/17/7591/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/17/7591/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:17:p:7591-:d:1469545

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().