Recycling and sustainable applications of waste printed circuit board in concrete application and validation using response surface methodology

• Published in: Scientific reports Vol. 13; no. 1; p. 16509
• Main Authors: Priyan, M. Vishnu, Annadurai, R., Onyelowe, Kennedy C., Alaneme, George Uwadiegwu, Giri, Nimay Chandra
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 02.10.2023; Nature Publishing Group UK; Nature Portfolio
• Subjects: Built environment; Carbon; Concrete mixes; Fibers; Mathematical models; Mechanical properties; Printed circuit boards; Reinforced concrete; Statistical analysis; Statistical models; Urban environments
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-43919-9

The present investigation aims to examine the mechanical and durability properties of concrete that has been reinforced with a waste printed circuit board (WPCB) towards a low-carbon built environment. It assessed the fresh and hardened characteristics of the low-carbon concrete reinforced with WPCB fibres, after a curing period of 7 and 28 days. The evaluation was done by quantifying slump, compressive strength, split tensile strength, flexural strength, sorptivity, rapid, and acid tests. It further analysed eleven discrete concrete mixes with WPCB fibres at a weight percentage ranging from 1 to 5% in the cement mixture. The results indicate that incorporating WPCB fibre into concrete improves its mechanical strength. The results revealed that incorporating 5% WPCB fibre yielded the most favourable outcomes. The properties of WPCB fibre-reinforced concrete have been theoretically validated through Response Surface Methodology (RSM), which employs various statistical and mathematical tools to analyse the experimental data. The results derived from RSM were compared with the experimental results. It was found that the RSM model demonstrated a high level of accuracy (R2 ≥ 0.98) in validating the mechanical properties of WPCB fibre concrete. The statistical model exhibited no indication of prediction bias and demonstrated a statistically significant outcome, with a p-value below 0.5.