• 콘크리트학회논문집
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• 제21권2호
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• pp.179-186
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• 2009

고강도콘크리트의 취약점으로 지적되고 있는 화재시의 폭렬현상에 대한 대책을 마련하기 위해 각 계의 노력이 활발한 현 상황에서 각종 폭렬 저감성 재료 및 새로운 개념의 소재에 대한 적정 혼입비율을 구명해야 하는 긴요한 상황이다. 본 연구에서는 메타카올린, 페타이어칩, 폴리프로필렌섬유 및 강섬유의 4가지 기능성 소재를 대상으로, 기본적인 품질 요건은 물론, 내화성능에 최적의 효과를 나타낼 수 있는 배합비를 실험적, 통계적으로 도출하고자 하였다. 여기서, 실험은 4인자 3수준의 직교배열표를 이용하여 최소실험법으로 계획하고, 통계적 분석은 반응표면분석 기법을 이용하였다. 그 결과, 80 MPa급 고강도콘크리트의 내화성능 보강인자로 선정된 기능성 소재간에는 복합 사용시 상호 보완적인 기여를 하는 것으로 확인되었다. 한편, 반응표면분석을 통해 도출한 내화성능 보강인자의 최적조건은 메타카올린을 실리카퓸 대신 80% 수준으로 용적치환하고, 폐타이어칩은 잔골재 대신 3% 수준으로 용적치환하는 경우와 폴리프로필렌 섬유를 전체용적에 대하여 0.2% 수준으로 첨가하는 한편, 강섬유를 혼입하지 않는 것이 고강도콘크리트의 기초 특성과 내화특성을 고루 만족할 수 있는 것으로 분석되었다.

• Computers and Concrete
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• 제19권2호
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• pp.133-142
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• 2017

Two-stage concrete (TSC), also known as pre-placed aggregate concrete, is characterized by its unique placement technique, whereby the coarse aggregate is first placed in the formwork, then injected with a special grout. Despite its superior sustainability and technical features, TSC has remained a basic concrete technology without much use of modern chemical admixtures, new binders, fiber reinforcement or other emerging additions. In the present study, an experimental database for TSC was built. Different types of cementitious binders (single, binary, and ternary) comprising ordinary portland cement, fly ash, silica fume, and metakaolin were used to produce the various TSC mixtures. Different dosages of steel fibres having different lengths were also incorporated to enhance the mechanical properties of TSC. The database thus created was used to develop fuzzy logic models as predictive tools for the grout flowability and mechanical properties of TSC mixtures. The performance of the developed models was evaluated using statistical parameters and error analyses. The results indicate that the fuzzy logic models thus developed can be powerful tools for predicting the TSC grout flowability and mechanical properties and a useful aid for the design of TSC mixtures.

• Computers and Concrete
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• 제26권4호
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• pp.327-342
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• 2020

The objective of this study is to manufacture environmentally-friendly synthetic lightweight aggregates that may be used in the structural lightweight concrete production. The cold-bonding pelletization process has been used in the agglomeration of the pozzolanic materials to achieve these synthetic lightweight aggregates. In this context, it was aimed to recycle the waste fly ash by employing it in the manufacturing process as the major cementitious component. According to the well-known facts reported in the literature, it is stated that the main disadvantage of the synthetic lightweight aggregate produced by applying the cold-bonding pelletization technique to the pozzolanic materials is that it has a lower strength in comparison with the natural aggregate. Therefore, in this study, the metakaolin made of high purity kaolin and calcined kaolin obtained from impure kaolin have been employed at particular contents in the synthetic lightweight aggregate manufacturing as a cementitious material to enhance the particle crushing strength. Additionally, to propose a curing condition for practical attempts, different curing conditions were designated and their influences on the characteristics of the synthetic lightweight aggregates were investigated. Three substantial features of the aggregates, specific gravity, water absorption capacity, and particle crushing strength, were measured at the end of 28-day adopted curing conditions. Observed that the incorporation of thermally treated kaolin significantly influenced the crushing strength and water absorption of the aggregates. The statistical evaluation indicated that the investigated properties of the synthetic lightweight aggregate were affected by the thermally treated kaolin content more than the kaoline type and curing regime. Utilizing the thermally treated kaolin in the synthetic aggregate manufacturing lead to a more than 40% increase in the crushing strength of the pellets in all curing regimes. Moreover, two numerical formulations having high estimation capacity have been developed to predict the crushing strength of such types of aggregates by using soft-computing techniques: gene expression programming and artificial neural networks. The R-squared values, indicating the estimation performance of the models, of approximately 0.97 and 0.98 were achieved for the numerical formulations generated by using gene expression programming and artificial neural networks techniques, respectively.

• Computers and Concrete
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• 제34권4호
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• pp.489-501
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• 2024

Geopolymers are part of a class of materials characterized by properties combining polymers, ceramics, and cement. These include exceptionally high thermal and chemical stability, excellent mechanical strength and durability in aggressive environments. This work deals with the synthesis, characterization, and sustainability evaluation of GP_GBFS-MK geopolymers by alkaline activation of a granulated blast furnace slag-metakaolin mixture. In the first step, elemental and oxide analyses by XRF and EDS showed that the main constituents of GP_GBFS-MK geopolymers are silicon, sodium, and aluminium oxides. The structural analyses by XRD and FTIR confirmed that the geopolymerization for GP_GBFS-MK geopolymers did occur, accompanied by the formation of disordered networks from the blends and a modification to the microstructure by the geopolymerization process. Similarly, the microstructural study made by SEM showed that the GP_GBFS-MK geopolymers are constituted by aluminosilicates in the form of dense clusters on which are adsorbed particles of unreacted GBFS in the form of spheroids and white residues of the alkaline activating solution. In addition, the study of the sustainability evaluation of GP_GBFS-MK geopolymers showed that the water absorption of geopolymeric materials is lower than that of OPC cement. As for the elevated temperature resistance, the analyses indicated an excellent elevated temperature resistance of GP_GBFS-MK. In the same way, the study of the resistance to chemical aggressions showed that the GP_GBFS-MK geopolymeric materials are unattackable, contrary to the OPC cement-based materials which are strongly altered.

• 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.

• Computers and Concrete
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• 제27권4호
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• pp.319-332
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• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• International Journal of Concrete Structures and Materials
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• 제7권3호
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• pp.193-202
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• 2013

Geopolymer mortar compressed blocks were prepared using fly ash, ground granulated blast furnace slag, silica fume and metakaolin as binders and sand/quarry dust/pond ash as fine aggregate. Alkaline solution was used to activate the source materials for synthesizing the geopolymer mortar. Fresh mortar was used to obtain the compressed blocks. The strength development with reference to different parameters was studied. The different parameters considered were fineness of fly ash, binder components, type of fine aggregate, molarity of alkaline solution, age of specimen, fluid-to-binder ratio, binder-to-aggregate ratio, degree of saturation, etc. The compressed blocks were tested for compression at different ages. It was observed that some of the blocks attained considerable strength within 24 h under ambient conditions. The cardinal aim was to analyze the experimental data generated to formulate a phenomenological model to arrive at the combinations of the ingredients to produce geopolymer blocks to meet the strength development desired at the specified age. The strength data was analyzed within the framework of generalized Abrams' law. It was interesting to note that the law was applicable to the analysis of strength development of partially saturated compressed blocks when the degree of saturation was maintained constant. The validity of phenomenological model was examined with an independent set of experimental data. The blocks can replace the traditional masonry blocks with many advantages.

• 한국건설순환자원학회논문집
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• 제7권4호
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• pp.128-135
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• 2012

ALC는 경량이며 단열 및 차열 등의 성능이 우수한 반면 낮은 강도로 인한 모서리부의 취성파괴가 발생하기 쉬우므로 운반 및 취급 시 상당한 주의를 요구한다. 본 연구에서는 ALC의 물리적 성능개선을 위해 메타카올린 및 실리카퓸 등의 혼화재나 규석 분말 및 석고의 혼입율을 조절하여 제조한 ALC의 성능을 평가하였다. 연구결과 메타카올린이나 규석 분말의 혼입율이 18%인 경우 강도의 개선이 현저하였다. 이와 같은 결과는 공극의 충전효과에 의한 것으로 강도는 개선되나 밀도가 증가하므로 밀도를 낮추면서 강도를 개선할 수 있는 배합이나 제조법에 대한 연구가 필요하다.

• 콘크리트학회논문집
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• 제17권1호
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• pp.129-137
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• 2005

최근 양질의 하천골재가 고갈상태에 직면함에 따라 쇄석 골재의 사용이 보편화되고 있지만 쇄석 골재 사용으로 야기될 수 있는 알칼리-실리카 반응에 대한 문제를 검토하지 않은 채 콘크리트 재료로 사용하고 있는 실정이다. 알칼리-실리카 반응은 콘크리트에 유해한 팽창을 일으키는 작용으로서, 반응결과 알칼리-실리카 겔이 형성되고 이러한 겔이 수분을 계속 흡수함으로써 체적 팽창을 일으켜 콘크리트에 균열이 발생된다. 골재의 알칼리-실리카 반응성을 판정하는 방법은 암석학적 판정법, 화학법 및 모르타르 바 법이 일반적으로 사용되지만, 이 중에서 모르타르 바 법이 비교적 신뢰성이 높다. 본 연구에서는 모르타르바 시험방법 중 ASIM C 227과 ASIM C 1260을 선택하여 암석 유형별로 수집한 12종의 골재들을 대상으로 쇄석 골재의 반응성을 비교, 분석하였다. 또한 본 연구에서는 반응성 골재의 입자크기 및 입도가 모르타르 바의 알칼리-실리카 반응 팽창에 미치는 영향에 관하여 검토하였다 혼화재의 용도는 상당히 많지만 본 연구에서는 쇄석 골재 사용으로 문제되고 있는 알칼리-실리카 반응에 있어서 플라이애쉬, 고로슬래그미분말, 실리카퓸 및 메타카올린을 혼화재료로 사용할 경우 알칼리-실리카 반응에 미치는 영향을 알아보고자 시멘트에 대한 혼화재의 치환율을 달리하여 ASTM C 1260 시험법으로 알칼리-실리카 반응에 대한 팽창 저감효과를 평가하여 보았다. 본 연구에서는 혼화재의 치환율을 0, 5, 10, 15, 25 및 $35\%$로 하였으며, 모르타르 유동성 시험을 병행하여 어느 정도의 유동성을 갖는 혼화재는 45, $55\%$까지 치환율을 증가하여 길이변화 시험을 수행하였다. 시멘트 중량에 대한 혼화재 치환율이 플라이애쉬는 $25\%$, 실리카퓸은 $10\%$, 메타카올린은 $25\%$, 고로슬래그미분말은 $35\%$일 경우 알칼리-실리카 반응에 의한 팽창을 가장 효과적으로 방지할 수 있는 것으로 판단된다.

• Computers and Concrete
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• 제24권4호
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• pp.329-345
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• 2019

Despite the extensive use of mortar materials in constructions over the last decades, there is not yet a robust quantitative method, available in the literature, which can reliably predict mortar strength based on its mix components. This limitation is due to the highly nonlinear relation between the mortar's compressive strength and the mixed components. In this paper, the application of artificial neural networks for predicting the compressive strength of mortars has been investigated. Specifically, surrogate models (such as artificial neural network models) have been used for the prediction of the compressive strength of mortars (based on experimental data available in the literature). Furthermore, compressive strength maps are presented for the first time, aiming to facilitate mortar mix design. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of mortars in a reliable and robust manner.