Suiwei Pan¹, Anqi Ren¹, Yongli Peng¹, Min Wu², Wanguo Dong³, Chunlin Liu², Depeng Chen^2,*

Journal of Renewable Materials, Vol.11, No.2, pp. 881-904, 2023, DOI:10.32604/jrm.2022.023000

Abstract This study focuses on the workability and compressive strength of ceramsite self-compacting concrete with fine aggregate partially substituted by steel slag sand (CSLSCC) to prevent the pollution of steel slag in the environment. The SF, J-ring, visual stability index, and sieve analysis tests are primarily employed in this research to investigate the workability of freshly mixed self-compacting concrete containing steel slag at various steel slag sand replacement rates. The experiment results indicate that CSLSCC with the 20% volume percentage of steel slag (VPS) performs better workability, higher strength, and higher specific strength. The 7-day compressive strength of CSLSCC with the… More > Graphic Abstract

Yongshuai Sun^1,*, Guihe Wang², Yixuan li²

Journal of Renewable Materials, Vol.10, No.8, pp. 2287-2302, 2022, DOI:10.32604/jrm.2022.019660

Abstract To study the relationship between acoustic emission characteristic parameters of self-compacting concrete(SCC) and its destruction evolution, under uniaxial compression, acoustic emission(AE) tests are performed on C30 selfcompacting concrete test blocks that are preserved for 7 days and 28 days, the corresponding relationship among energy, amplitude, ring count and different failure stages of the specimens are analyzed by AE experiment, and the spatial distribution of AE in each stage is described by introducing location map. The test shows that there are two rules for the failure of SCC specimens cured for 7 days and 28 days: (1) The first failure law… More >

Song Yang¹, Bing Qi¹, Zubin Ai¹, Zhensheng Cao¹, Shiqin He^2,*, Lijun Li³

Journal of Renewable Materials, Vol.10, No.4, pp. 897-908, 2022, DOI:10.32604/jrm.2022.016653

Abstract It has become a research hotspot to explore raw material substitutes of concrete. It is important to research the mechanical properties of self-compacting concrete (SCC) with slag powder (SP) and rubber particle (RP) replacing cement and coarse aggregate, respectively. 12 kinds of composite modified self-compacting concrete (CMSCC) specimens were prepared by using 10%, 20% and 30% SP and 30%, 40%, 50% and 60% RP. The rheological properties, mechanical properties and microstructure of the CMSCC were investigated. Results indicate that the workability, compressive strength, splitting tensile strength and flexural strength of CMSCC prepared by 20% SP and less than 40% RP… More >

Lei Tian, Liuchao Qiu^*, Jingjun Li, Yongsen Yang

Journal of Renewable Materials, Vol.8, No.2, pp. 154-170, 2020, DOI:10.32604/jrm.2020.08701

Abstract In recent decades, the utilization of waste tires, plastic and artificial shale ceramsite as alternative fine aggregate to make self-compacting concrete (SCC) has been recognized as an eco-friendly and sustainable method to manufacture renewable construction materials. In this study, three kinds of recycled aggregates: recycled tire rubber particles, wood-plastic particles, artificial shale ceramsite were used to replace the sand by different volume (5%, 10%, 20% and 30%), and their effects on the fresh and hardened properties of SCC were investigated. The slump flow and V-funnel tests were conducted to evaluate the fresh properties of modified-SCC mixtures. The hardened properties include… More >

G. Jayaprakash¹, M. P. Muthuraj^2,*

CMC-Computers, Materials & Continua, Vol.54, No.1, pp. 83-102, 2018, DOI:10.3970/cmc.2018.054.083

Abstract This paper discusses the applicability of relevance vector machine (RVM) based regression to predict the compressive strength of various self compacting concrete (SCC) mixes. Compressive strength data various SCC mixes has been consolidated by considering the effect of water cement ratio, water binder ratio and steel fibres. Relevance vector machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM has an identical functional form to the support vector machine, but provides probabilistic classification and regression. RVM is based on a Bayesian formulation of a linear model with an appropriate… More >

Susom Dutta¹, A. Ramach,ra Murthy², Dookie Kim³, Pijush Samui⁴

CMC-Computers, Materials & Continua, Vol.53, No.2, pp. 157-174, 2017, DOI:10.3970/cmc.2017.053.167

Abstract In the present scenario, computational modeling has gained much importance for the prediction of the properties of concrete. This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete (SCC). Three models, namely, Extreme Learning Machine (ELM), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multi Adaptive Regression Spline (MARS) have been employed in the present study for the prediction of compressive strength of self compacting concrete. The contents of cement (c), sand (s), coarse aggregate (a), fly ash (f), water/powder (w/p) ratio and superplasticizer (sp) dosage have been taken as inputs… More >