Performance Prediction of Hybrid Bamboo-Reinforced Concrete Beams Using Gene Expression Programming for Sustainable ConstructionShow others and affiliations
2023 (English)In: Materials, E-ISSN 1996-1944, Vol. 16, no 20, article id 6788Article in journal (Refereed) Published
Abstract [en]
The building and construction industry’s demand for steel reinforcement bars has increased with the rapid growth and development in the world. However, steel production contributes to harmful waste and emissions that cause environmental pollution and climate change-related problems. In light of sustainable construction practices, bamboo, a readily accessible and ecofriendly building material, is suggested as a viable replacement for steel rebars. Its cost-effectiveness, environmental sustainability, and considerable tensile strength make it a promising option. In this research, hybrid beams underwent analysis through the use of thoroughly validated finite element models (FEMs), wherein the replacement of steel rebars with bamboo was explored as an alternative reinforcement material. The standard-size beams were subjected to three-point loading using FEMs to study parameters such as the load–deflection response, energy absorption, maximum capacity, and failure patterns. Then, gene expression programming was integrated to aid in developing a more straightforward equation for predicting the flexural strength of bamboo-reinforced concrete beams. The results of this study support the conclusion that the replacement of a portion of flexural steel with bamboo in reinforced concrete beams does not have a detrimental impact on the overall load-bearing capacity and energy absorption of the structure. Furthermore, it may offer a cost-effective and feasible alternative.
Place, publisher, year, edition, pages
MDPI , 2023. Vol. 16, no 20, article id 6788
Keywords [en]
green building material; hybrid beams; bamboo-reinforced concrete beam; finite element model; replacement; gene expression programming
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:hig:diva-43156DOI: 10.3390/ma16206788ISI: 001089968500001PubMedID: 37895769Scopus ID: 2-s2.0-85175307028OAI: oai:DiVA.org:hig-43156DiVA, id: diva2:1806412
2023-10-202023-10-202024-07-04Bibliographically approved