Liang Jia, Chunxiang Li, Jian Guo^*

Journal of Renewable Materials, Vol.8, No.10, pp. 1357-1373, 2020, DOI:10.32604/jrm.2020.012136

Abstract Lime-fly ash stabilized loess has a poor early strength, which results in a later traffic opening time when it is used as road-base materials. Consideration of the significant early strength characteristics of sulphate aluminum cement (SAC), it is always added into the lime-fly ash mixtures to improve the early strength of stabilized loess. However, there is a scarcity of research on the mechanical behavior of lime-fly ash-SAC stabilized loess and there is a lack of quantitative evaluation of loess stabilized with binder materials. This research explored the effects of the amount of binder materials, curing time and porosity on the… More >

Sanghyo Lee¹, Yonghan Ahn², Ha Young Kim^{3, *}

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 1-17, 2020, DOI:10.32604/cmc.2020.011104

Abstract In this study, we examined the efficacy of a deep convolutional neural network (DCNN) in recognizing concrete surface images and predicting the compressive strength of concrete. A digital single-lens reflex (DSLR) camera and microscope were simultaneously used to obtain concrete surface images used as the input data for the DCNN. Thereafter, training, validation, and testing of the DCNNs were performed based on the DSLR camera and microscope image data. Results of the analysis indicated that the DCNN employing DSLR image data achieved a relatively higher accuracy. The accuracy of the DSLR-derived image data was attributed to the relatively wider range… More >

Tao Zhang, Jinglan Deng^*, Jihui Wang

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 545-559, 2020, DOI:10.32604/cmes.2020.09536

Abstract The out-of-plane fold is a common defect of composite materials during the manufacturing process and will greatly affect the compressive strength as well as the service life. Making it of great importance to investigate the influence of out-of-plane defects to the compressive strength of laminate plates of composite materials, and to understand the patterns of defect evolution. Therefore, the strip method is applied in this article to create out-of-plane defects with different aspect ratios in laminated plates of composite materials, and a compressive performance test is conducted to quantify the influence of out-of-plane defects. The result shows that the compressive… More >

Isamar Rosa¹, Henning Roedel¹, Maria I. Allende¹, Michael D. Lepech^1,*, David J. Loftus²

Journal of Renewable Materials, Vol.8, No.8, pp. 845-861, 2020, DOI:10.32604/jrm.2020.09844

Abstract Biopolymer-bound Soil Composites (BSC), are a novel bio-based construction material class, produced through the mixture and desiccation of biopolymers with inorganic aggregates with applications in soil stabilization, brick creation and in situ construction on Earth and space. This paper introduces a mixture design methodology to produce maximum strength for a given soil-biopolymer combination. Twenty protein and sand mix designs were investigated, with varying amounts of biopolymer solution and compaction regimes during manufacture. The ultimate compressive strength, density, and shrinkage of BSC samples are reported. It is observed that the compressive strength of BSC materials increases proportional to tighter particle packing… More >

Haikuan Wu^1,2, Changwu Liu^1,2,*, Song Shi^1,2, Kangliang Chen^1,2

Journal of Renewable Materials, Vol.8, No.7, pp. 727-738, 2020, DOI:10.32604/jrm.2020.09589

Abstract In order to study the effect of recycled plastic particles on the physical and mechanical properties of concrete, recycled plastic concrete with 0, 3%, 5% and 7% content (by weight) was designed. The compressive strength, splitting tensile strength and the change of mass caused by water absorption during curing were measured. The results show that the strength of concrete is increased by adding recycled plastic into concrete. Among them, the compressive strength and the splitting tensile strength of concrete is the best when the plastic content is 5%. With the increase of plastic content, the development speed of early strength… More >

Hyun Kyu Shin¹, Yong Han Ahn², Sang Hyo Lee³, Ha Young Kim^4,*

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 911-928, 2019, DOI:10.32604/cmc.2019.08269

Abstract Compressive strength of concrete is a significant factor to assess building structure health and safety. Therefore, various methods have been developed to evaluate the compressive strength of concrete structures. However, previous methods have several challenges in costly, time-consuming, and unsafety. To address these drawbacks, this paper proposed a digital vision based concrete compressive strength evaluating model using deep convolutional neural network (DCNN). The proposed model presented an alternative approach to evaluating the concrete strength and contributed to improving efficiency and accuracy. The model was developed with 4,000 digital images and 61,996 images extracted from video recordings collected from concrete samples.… More >

Nadezda Stevulova^1,*, Viola Hospodarova¹, Adriana Estokova¹, Eva Singovszka¹, Marian Holub¹, Stefan Demcak¹, Jaroslav Briancin², Anton Geffert³, Frantisek Kacik³, Vojtech Vaclavik⁴, Tomas Dvorsky⁴

Journal of Renewable Materials, Vol.7, No.11, pp. 1121-1145, 2019, DOI:10.32604/jrm.2019.07556

Abstract The aim of this study was to characterize two types of cellulosic fibers obtained from bleached wood pulp and unbleached recycled waste paper with different cellulose content (from 47.4 percent up to 82 percent), to compare and to analyze the potential use of the recycled fibers for building application, such as plastering mortar. Changes in the chemical composition, cellulose crystallinity and degree of polymerization of the fibers were found. The recycled fibers of lower quality showed heterogeneity in the fiber sizes (width and length), and they had greater surface roughness in comparison to high purity wood pulp samples. The high… More >

Ch,ra S. Yerramalli¹, Anthony M. Waas²

CMES-Computer Modeling in Engineering & Sciences, Vol.6, No.1, pp. 1-16, 2004, DOI:10.3970/cmes.2004.006.001

Abstract Results from a 3D finite element based study of the compression response of unidirectional fiber reinforced polymer matrix composites (FRPC) are presented in this paper. The micromechanics based study was used to simulate the compressive response of glass and carbon fiber reinforced polymer matrix composites, with a view to understanding the effect of fiber diameter on compression strength. Results from the modeling and simulation indicate the presence of a complex three dimensional stress state in the matrix of the FRPC. Results from the simulation highlight the role of fiber diameter on the compressive response of FRPC. In particular, it is… More >

M. Pankow¹, A.M. Waas², C.F. Yen³

CMC-Computers, Materials & Continua, Vol.32, No.2, pp. 81-106, 2012, DOI:10.3970/cmc.2012.032.081

Abstract The compression response of 3D woven textile composites (3DWC) that consist of glass fiber tows and a polymer matrix material is studied using a combination of experiments and finite element based analyses. A previous study reported by the authors consisted of an experimental investigation of 3DWC under high strain rate loading, Pankow, Salvi, Waas, Yen, and Ghiorse (2011). Those experimental results were explained by using the finite element method to analyze the high rate deformation response of representative volume elements (RVEs) of the 3DWC, Pankow, Waas, Yen, and Ghiorse (2012). In this paper, the same modeling strategy is used to… 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 >