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  • Open Access

    ARTICLE

    Predicting Concrete Compressive Strength Using Deep Convolutional Neural Network Based on Image Characteristics

    Sanghyo Lee1, Yonghan Ahn2, Ha Young Kim3, *

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 1-17, 2020, DOI:10.32604/cmc.2020.011104 - 23 July 2020

    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… More >

  • Open Access

    ARTICLE

    Progressive Damage Analysis (PDA) of Carbon Fiber Plates with Out-of-Plane Fold under Pressure

    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 - 20 July 2020

    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… More >

  • Open Access

    ARTICLE

    On Designing Biopolymer-Bound Soil Composites (BSC) for Peak Compressive Strength

    Isamar Rosa1, Henning Roedel1, Maria I. Allende1, Michael D. Lepech1,*, David J. Loftus2

    Journal of Renewable Materials, Vol.8, No.8, pp. 845-861, 2020, DOI:10.32604/jrm.2020.09844 - 10 July 2020

    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 More >

  • Open Access

    ARTICLE

    Experimental Research on the Physical and Mechanical Properties of Concrete with Recycled Plastic Aggregates

    Haikuan Wu1,2, Changwu Liu1,2,*, Song Shi1,2, Kangliang Chen1,2

    Journal of Renewable Materials, Vol.8, No.7, pp. 727-738, 2020, DOI:10.32604/jrm.2020.09589 - 01 June 2020

    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… More >

  • Open Access

    ARTICLE

    Digital Vision Based Concrete Compressive Strength Evaluating Model Using Deep Convolutional Neural Network

    Hyun Kyu Shin1, Yong Han Ahn2, Sang Hyo Lee3, Ha Young Kim4,*

    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 More >

  • Open Access

    ARTICLE

    Characterization of Manmade and Recycled Cellulosic Fibers for Their Application in Building Materials

    Nadezda Stevulova1,*, 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… More >

  • Open Access

    ARTICLE

    Prediction of Compressive Strength of Various SCC Mixes Using Relevance Vector Machine

    G. Jayaprakash1, M. P. Muthuraj2,*

    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 More >

  • Open Access

    ARTICLE

    Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

    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 More >

  • Open Access

    ARTICLE

    Prediction of Concrete Cubic Compressive Strength Using ANN Based Size Effect Model

    Q.W. Yang1, S.G. Du1,2

    CMC-Computers, Materials & Continua, Vol.47, No.3, pp. 217-236, 2015, DOI:10.3970/cmc.2015.047.217

    Abstract Size effect is a major issue in concrete structures and occurs in concrete in any loading conditions. In this study, size effect on concrete cubic compressive strength is modeled with a back-propagation neural network. The main advantage in using an artificial neural network (ANN) technique is that the network is built directly from experimental data without any simplifying assumptions via the self-organizing capabilities of the neural network. The proposed ANN model is verified by using 27 experimental data sets collected from the literature. For the large specimens, a modified ANN is developed in the paper More >

  • Open Access

    ARTICLE

    Modeling the Response of 3D Textile Composites under Compressive Loads to Predict Compressive Strength

    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 More >

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