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Computational prediction of workability and mechanical properties of bentonite plastic concrete using multi-expression programming
Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, 22060, Abbottabad, Pakistan.
Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8 Street, 40-019, Katowice, Poland.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0003-4895-5300
Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia.ORCID iD: 0000-0002-0036-8417
2024 (English)In: Scientific Reports, E-ISSN 2045-2322, Vol. 14, no 1, article id 6105Article in journal (Refereed) Published
Abstract [en]

Bentonite plastic concrete (BPC) demonstrated promising potential for remedial cut-off wall construction to mitigate dam seepage, as it fulfills essential criteria for strength, stiffness, and permeability. High workability and consistency are essential attributes for BPC because it is poured into trenches using a tremie pipe, emphasizing the importance of accurately predicting the slump of BPC. In addition, prediction models offer valuable tools to estimate various strength parameters, enabling adjustments to BPC mixing designs to optimize project construction, leading to cost and time savings. Therefore, this study explores the multi-expression programming (MEP) technique to predict the key characteristics of BPC, such as slump, compressive strength (fc), and elastic modulus (Ec). In the present study, 158, 169, and 111 data points were collected from the experimental studies for the slump, fc, and Ec, respectively. The dataset was divided into three sets: 70% for training, 15% for testing, and another 15% for model validation. The MEP models exhibited excellent accuracy with a correlation coefficient (R) of 0.9999 for slump, 0.9831 for fc, and 0.9300 for Ec. Furthermore, the comparative analysis between MEP models and conventional linear and non-linear regression models revealed remarkable precision in the predictions of the proposed MEP models, surpassing the accuracy of traditional regression methods. SHapley Additive exPlanation analysis indicated that water, cement, and bentonite exert significant influence on slump, with water having the greatest impact on compressive strength, while curing time and cement exhibit a higher influence on elastic modulus. In summary, the application of machine learning algorithms offers the capability to deliver prompt and precise early estimates of BPC properties, thus optimizing the efficiency of construction and design processes.

Place, publisher, year, edition, pages
Nature Research , 2024. Vol. 14, no 1, article id 6105
Keywords [en]
Bentonite plastic concrete, Machine learning, Mechanical properties, MEP, Slump
National Category
Materials Engineering
Research subject
Operation and Maintenance Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-104889DOI: 10.1038/s41598-024-56088-0PubMedID: 38480772Scopus ID: 2-s2.0-85187738302OAI: oai:DiVA.org:ltu-104889DiVA, id: diva2:1846955
Note

Validerad;2024;Nivå 2;2024-04-05 (marisr);

Full text license: CC BY

Available from: 2024-03-26 Created: 2024-03-26 Last updated: 2024-04-05Bibliographically approved

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Najeh, TaoufikGamil, Yaser

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