• Structural Engineering and Mechanics
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• 제70권6호
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• pp.671-681
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• 2019

This paper examines the applicability of artificial neural network (ANN) and multivariate adaptive regression splines (MARS) to predict the compressive strength of bacteria incorporated geopolymer concrete (GPC). The mix is composed of new bacterial strain, manufactured sand, ground granulated blast furnace slag, silica fume, metakaolin and fly ash. The concentration of sodium hydroxide (NaOH) is maintained at 8 Molar, sodium silicate ($Na_2SiO_3$) to NaOH weight ratio is 2.33 and the alkaline liquid to binder ratio of 0.35 and ambient curing temperature ($28^{\circ}C$) is maintained for all the mixtures. In ANN, back-propagation training technique was employed for updating the weights of each layer based on the error in the network output. Levenberg-Marquardt algorithm was used for feed-forward back-propagation. MARS model was developed by establishing a relationship between a set of predictors and dependent variables. MARS is based on a divide and conquers strategy partitioning the training data sets into separate regions; each gets its own regression line. Six models based on ANN and MARS were developed to predict the compressive strength of bacteria incorporated GPC for 1, 3, 7, 28, 56 and 90 days. About 70% of the total 84 data sets obtained from experiments were used for development of the models and remaining 30% data was utilized for testing. From the study, it is observed that the predicted values from the models are found to be in good agreement with the corresponding experimental values and the developed models are robust and reliable.

• Structural Engineering and Mechanics
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• 제68권6호
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• 대한건축학회논문집:구조계
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• 제34권11호
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• pp.11-18
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• 2018

In this study, seven R/C beams, designed by the steel fiber with ground granulated blast furnace slag and recycled fine aggregate were constructed and tested under monotonic loading. In the material development, micromechanics was adopted to properly select the optimized range of the composite based on steady-state cracking theory and experimental studies on the matrix and interracial properties. Experimental programs were carried out to improve and evaluate the structural performance of the test specimens: the load-displacement, the failure mode, the maximum strength were assessed. Test results showed that test specimens (BSSR-20, 40, 60, 80) were increased the maximum load carrying capacity by 2~9% and the ductility capacity by 10~22% in comparison with the standard specimen (BSS) respectively. And the specimens (BSSR-100) was decreased the maximum load carrying capacity by 5% and the ductility capacity by 44% in comparison with the standard specimen (BSS) respectively.

• Computers and Concrete
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• 제27권1호
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• pp.21-28
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• 2021

The Artificial Neural Network (ANN) is a system, which is utilized for solving complicated problems by using nonlinear equations. This study aims to investigate compressive strength, rebound hammer number (RN), and ultrasonic pulse velocity (UPV) of sustainable concrete containing various amounts of fly ash, silica fume, and blast furnace slag (BFS). In this study, the artificial neural network technique connects a nonlinear phenomenon and the intrinsic properties of sustainable concrete, which establishes relationships between them in a model. To this end, a total of 645 data sets were collected for the concrete mixtures from previously published papers at different curing times and test ages at 3, 7, 28, 90, 180 days to propose a model of nine inputs and three outputs. The ANN model's statistical parameter R2 is 0.99 of the training, validation, and test steps, which showed that the proposed model provided good prediction of compressive strength, RN, and UPV of sustainable concrete with the addition of cement.

• 대한건축학회연합논문집
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• 제21권5호
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• pp.117-124
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• 2019

When manufacturing concrete, several mineral admixture is added to improve the basic physical property and durability and to make economical concrete. Such mineral admixture includes fly ash, granulated blast furnace slag, silica fume, etc., and not only the studies about mixing these mineral admixtures but also the studies for the development of new materials have been steadily in progress. Recently, some researchers have found, as a part of the development of new materials, the rice straw ash can also be used as a pozzolanic material for concrete considering similar chemical properties of rice straw ash to that of rice husk ash. But there has been insufficient amount of study about it. So, this study was to investigate the possibility as mineral admixture of agriculture by-product, by analyzing properties of concretes using rice straw ash with replacement ratio in comparison with other mineral admixture. In order to measure amount of SiO2 of rice straw ash, XRF(X-ray fluorescence) analysis was tested. For the measure pozzolanic reaction of rice straw ash, pH change and color change was tested according to curing day. Also to evaluate properties of concrete using rice straw ash, slump test, air contents test and compressive strength was tested.

• Advances in concrete construction
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• 제12권5호
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• pp.419-427
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• 2021

The overuse level of cement for civil industry has several undesirable social and ecological consequences. Substitution of cement with industrial wastes, called by-products, such as fly ash, ground granulated blast furnace slag, silica fume, metakaoline, rice husk ash, etc. as the mineral admixtures offers various advantages such as technical, economical and environmental which are very important in the era of sustainability in construction industry. The paper presents the experimental investigations for assessing the mechanical properties of the concrete made using the Pozzolanic waste materials (supplementary cementitious materials) such as fly ash and silica fume as the cement replacing materials. These materials were used in eight trial mixes with varying amount of ordinary Portland cement. These SCMs were kept in equal proportions in all the eight trial mixes. The chemical admixture (High Range Water Reducing Admixture) was also added to improve the workability of concrete. The compressive strengths for 7, 28, 40 and 90 days curing were evaluated whereas the flexural and tensile strengths corresponding to 7, 28 and 40 days curing were evaluated. The study corroborates that the Pozzolanic materials used in the present investigation as partial replacement for cement can render the sustainable concrete which can be used in the rigid pavement construction.

• Advances in concrete construction
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• 제12권6호
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• pp.499-505
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• 2021

Geopolymer binders fascinate the attention of researchers as a replacement to cement binder in conventional concrete. One-ton production of cement releases one ton of carbon-dioxide in the atmosphere. In the replacement of cement by geopolymer material, there are two advantages: one is the reduction of CO₂ in the atmosphere, second is the utilization of Fly ash and Ground granulated blast furnace slag (GGBFS) are by-products from coal and steel industries. This paper focuses on the mechanical properties of steel fiber reinforced geopolymer concrete. The framework considered in this research work is geopolymer source (Fly ash, GGBFS and crimped steel fibre) and alkaline activator which consists of NaOH and Na₂SiO₃ of molarity 8M. Here the Na₂SiO₃ / NaOH ratio was taken as 2.5. The variables considered in this experimental work include Binder content (360,420 and 450 kg/m³), the proportion of Fly ash and GGBS (70-30, 60-40 and 50-50) for three different grades of Geopolymer concrete (GPC) GPC 20, GPC 40 and GPC 60. The percentage of crimped steel fibres was varied as 0.1%, 0.2%, 0.3%, 0.4% and 0.5%. Generally, the inclusion of steel fibres increases the flexural and split tensile strength of Geopolymer concrete. The optimum dosage of steel fibres was found to be 0.4% (by volume fraction).

• Steel and Composite Structures
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• 제43권3호
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• pp.389-401
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• 2022

This study investigated the flowability and mechanical properties of cost-effective steel fiber reinforced ultra-high performance concrete (UHPC) by using locally available materials for field-cast application. To examine the effect of mixture constituents, five mixtures with different fractions of silica fume, silica powder, ground granulated blast furnace slag (GGBS), silica sand, and crushed natural sand were proportionally prepared. Comprehensive experiments for different mixture designs were conducted to evaluate the fresh- and hardened-state properties of self-consolidating UHPC. The results showed that the proposed UHPC had similar mechanical properties compared with conventional UHPC while the flow retention over time was enhanced so that the field-cast application seemed appropriately cost-effective. The self-consolidating UHPC with high flowability and low viscosity takes less total mixing time than conventional UHPC up to 6.7 times. The X-ray computed tomographic imaging was performed to investigate the steel fiber distribution inside the UHPC by visualizing the spatial distribution of steel fibers well. Finally, the tensile stress-strain curve for the proposed UHPC was proposed for the implementation to the structural analysis and design.

• Structural Engineering and Mechanics
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• 제81권5호
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• pp.575-589
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• 2022

In order to enhance the greenness in the strain-hardening composites and to reduce the high cost of typical polyvinyl alcohol fiber reinforced engineered cementitious composite (PVA-ECC), an affordable strain-hardening composite with green binder content has been proposed. For optimizing the strain-hardening behavior of cementitious composites, this paper investigates the effects of polypropylene fibers on the first cracking strength, fracture properties, and micromechanical parameters of cementitious composites. For this purpose, digital image correlation (DIC) technique was utilized to monitor crack propagation. In addition, to have an in-depth understanding of fiber/matrix interaction, scanning electron microscope (SEM) analysis was used. To understand the effect of fibers on the strain hardening behavior of cementitious composites, ten mixes were designed with the variables of fiber length and volume. To investigate the micromechanical parameters from fracture tests on notched beam specimens, a novel technique has been suggested. In this regard, mechanical and fracture tests were carried out, and the results have been discussed utilizing both fracture and micromechanical concepts. This study shows that the fiber length and volume have optimal values; therefore, using fibers without considering the optimal values has negative effects on the strain-hardening behavior of cementitious composites.

• Smart Structures and Systems
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• 제30권2호
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.