Assessment of mechanical properties with machine learning modeling and durability, and microstructural characteristics of a biochar-cement mortar composite

Sobuz, Md Habibur Rahman and Khan, Mahmudur Hossain and Kabbo, Md Kawsarul Islam and Alhamami, Ali Hussain and Aditto, Fahim Shahriyar and Sajib, Md Saziduzzaman and Alengaram, U. Johnson and Mansour, Walid and Hasan, Noor Md Sadiqul and Datta, Shuvo Dip and Alam, Arafat (2024) Assessment of mechanical properties with machine learning modeling and durability, and microstructural characteristics of a biochar-cement mortar composite. Construction and Building Materials, 411. ISSN 0950-0618, DOI https://doi.org/10.1016/j.conbuildmat.2023.134281.

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Official URL: https://doi.org/10.1016/j.conbuildmat.2023.134281

Abstract

Rising CO2 emissions have become one of the biggest environmental challenges in recent years. Due to the rising carbon footprint of the building industry, CO2 emission regulation and mitigation have become perennial issues. The utilization of Biochar (BC) as a carbon-sequestering component in cement mortar is the novelty and main concern of this study. In this research, the effectiveness of BC in sequestering carbon was examined along with its effect on the mechanical, microstructural, and durability characteristics of the composite cement mortar. It includes a control and eight additional mixes prepared with 1%, 3%, 5%, and 8% BC by weight of cement added to mortar; the BC were prepared at two fixed temperatures of 300 degrees C and 500 degrees C. It also involves testing fresh properties, mechanical properties, durability properties, and microstructure analysis using scanning electron microscopy (SEM) and energy-dispersive X-ray analysis (EDX). A Universal Testing Machine (UTM) was used to determine the mechanical properties of the cement mortar, such as its compressive and tensile strengths. The water permeability and rapid chloride permeability tests (RCPT) were used to evaluate the specimens' long-term stability as a measure of their durability. Based on the test results, it has been found that the inclusion of BC enhanced the strength and durability of cement mortar through its pozzolanic action. In addition, BC is a filler material whose porous structure fills the voids within the cement particles, decreasing water absorption and improving workability. BC sequesters carbon by carbonizing biomass. Its presence in cement mortar stores carbon that would otherwise be emitted into the atmosphere, which helps to reduce the environmental effect. According to the conclusion of the study, BC has the potential to be a sustainable component of cement mortar. In addition, the two distinct algorithms built upon machine learning applied in the analysis using adaptive boosting (AdaBoost) and linear regression (LR); both these analyses demonstrate that it is feasible to predict the characteristics of cement mortar accurately. The AdaBoost methodology outperforms the LR methodology because of its strong correlation value (R2).

Item Type: Article
Funders: Deputy for Research and Innovation - Ministry of Education, Kingdom of Saudi Arabia under the Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia [NU/IFC/2/SERC/-/14], Institutional Funding Committee at Najran University, Kingdom of Saudi Arabia
Uncontrolled Keywords: Mortar; Biochar (BC); Compressive Strength; Pyrolysis; CO2; Machine Learning
Subjects: T Technology > TG Bridge engineering
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 01 Jul 2024 02:09
Last Modified: 01 Jul 2024 02:09
URI: http://eprints.um.edu.my/id/eprint/44270

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