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Buildings
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Machine Learning Applications for Engineered Geomaterials Development
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Machine Learning Applications for Engineered Geomaterials Development

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Materials, and Repair & Renovation".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 2780

Special Issue Editors

Prof. Dr. Gobinath Ravindran

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Guest Editor
Department of Civil Engineering, SR University, Warangal, Telangana, India
Interests: civil engineering materials; sustainable development; artificial intelligence; material innovation; geotechnology; environmental geotechnology
Special Issues, Collections and Topics in MDPI journals
Dr. Isaac Akinwumi

E-Mail Website
Guest Editor
Department of Civil Engineering, Covenant University, Ota, Nigeria
Interests: sustainability; construction materials; soil, environment; geotechnical engineering; water supply; sanitation and hygiene (WASH); waste management; infrastructure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geomaterials are materials that are influenced by geological systems and that have served humankind for multiple centuries. Recent urbanisation and unprecedented usage have put pressure on these materials and has caused rapid depletion. Newly developed multiphase/scale analysis methods should improve our understanding of geomaterial behaviour. If a clear understanding can be achieved, it could greatly benefit the safety and reliability of geotechnical infrastructures built on/with geomaterials. All structural applications now produce huge loads both directly and indirectly, which removes the need for generic geomaterials, and hence, a newer dimension has come into use, which is engineered geomaterials. Engineered geomaterials are used in a wide range of applications including structures under severe environments. The application of AI and ML is steadily growing due to their versatility and application standards. Material design requires many resources in analysing and understanding a material’s behaviour, which is currently widely supported by machine learning applications. The aim of this Special Issue is to understand the recent research ongoing in geomaterial development for the creation of sustainable and resilient infrastructure with a prime focus on machine learning applications.

Prof. Dr. Gobinath Ravindran
Dr. Isaac Akinwumi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • geomaterials
  • engineered geomaterials
  • artificial intelligence
  • machine learning
  • characterisation

Published Papers (1 paper)

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Research

20 pages, 3023 KiB  
Article
Predicting California Bearing Ratio of Lateritic Soils Using Hybrid Machine Learning Technique
by T. Vamsi Nagaraju, Alireza Bahrami, Ch. Durga Prasad, Sireesha Mantena, Monalisa Biswal and Md. Rashadul Islam
Buildings 2023, 13(1), 255; https://doi.org/10.3390/buildings13010255 - 16 Jan 2023
Cited by 7 | Viewed by 2328
Abstract
The increase in population has made it possible for better, more cost-effective vehicular services, which warrants good roadways. The sub-base that serves as a stress-transmitting media and distributes vehicle weight to resist shear and radial deformation is a critical component of the pavement [...] Read more.
The increase in population has made it possible for better, more cost-effective vehicular services, which warrants good roadways. The sub-base that serves as a stress-transmitting media and distributes vehicle weight to resist shear and radial deformation is a critical component of the pavement structures. Developing novel techniques that can assess the sub-base soil’s geotechnical characteristics and performance is an urgent need. Laterite soil abundantly available in the West Godavari area of India was employed for this research. Roads and highways construction takes a chunk of geotechnical investigation, particularly, California bearing ratio (CBR) of subgrade soils. Therefore, there is a need for intelligent tool to predict or analyze the CBR value without time-consuming and cumbersome laboratory tests. An integrated extreme learning machine-cooperation search optimizer (ELM-CSO) approach is used herein to predict the CBR values. The correlation coefficient is utilized as cost functions of the CSO to identify the optimal activation weights of the ELM. The statistical measures are separately considered, and best solutions are reported in this article. Comparisons are provided with the standard ELM to show the superiorities of the proposed integrated approach to predict the CBR values. Further, the impact of each input variable is studied separately, and reduced models are proposed with limited and inadequate input data without loss of prediction accuracy. When 70% training and 30% testing data are applied, the ELM-CSO outperforms the CSO with Pearson correlation coefficient (R), coefficient of determination (R2), and root mean square error (RMSE) values of 0.98, 0.97, and 0.84, respectively. Therefore, based on the prediction findings, the newly built ELM-CSO can be considered an alternative method for predicting real-time engineering issues, including the lateritic soil properties. Full article
(This article belongs to the Special Issue Machine Learning Applications for Engineered Geomaterials Development)
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