Kolli Ramujee^1,*, Pooja Sadula¹, Golla Madhu², Sandeep Kautish³, Abdulaziz S. Almazyad⁴, Guojiang Xiong⁵, Ali Wagdy Mohamed^6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1455-1486, 2024, DOI:10.32604/cmes.2023.043384

Abstract Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems. Its attributes as a non-toxic, low-carbon, and economical substitute for conventional cement concrete, coupled with its elevated compressive strength and reduced shrinkage properties, position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure. In this context, this study sets out the task of using machine learning (ML) algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field. To achieve this goal, a new approach using convolutional… More >

Mark Andrew Bradford¹, Yong-Lin Pi¹

CMES-Computer Modeling in Engineering & Sciences, Vol.89, No.5, pp. 389-416, 2012, DOI:10.3970/cmes.2012.089.389

Abstract This paper is devoted to the development of a finite composite beam element for the nonlinear elastic-plastic analysis of composite high strength steel and geopolymer concrete members. For this, geometric nonlinearity is derived using a special orthogonal rotation matrix, so that scalar product of vectors is preserved during rotation from the initial configuration to a deformed configuration and rigid body movements are excluded from the finite strains. The material nonlinearities of the geopolymer concrete are based on constitutive models in association with its axial stress-strain relationship that is consistent with the experimental results. To consider the slip due to possible… More >