• 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.

• Advances in concrete construction
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• 제13권1호
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• pp.71-81
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• 2022

Aluminosilicate materials as precursors are heterogenous in nature, consisting of inert and partially reactive portion, and have varying proportions depending upon source materials. It is essential to assess the reactivity of precursor prior to synthesize geopolymers. Moreover, reactivity may act as decisive factor for setting molar concentration of NaOH, curing temperature and setting proportion of different precursors. In this experimental work, the reactivities of two precursors, low calcium (fly ash (FA)) and high calcium (ground granulated blast furnace slag (GGBS)), were assessed through the dissolution of aluminosilicate at (i) three molar concentrations (8, 12, and 16 M) of NaOH solution, (ii) 6 to 24 h dissolution time, and (iii) 20-100℃. Based on paratermeters influencing the reactivity, different proportions of ternary binders (two precursors and ordinary cement) were activated by the combined NaOH and Na2SiO3 solutions with two alkaline activators to precursor ratios, to synthesize the geopolymer. Reactivity results revealed that GGBS was 20-30% more reactive than FA at 20℃, at all three molar concentrations, but its reactivity decreased by 32-46% with increasing temperature due to the high calcium content. Setting time of geopolymer paste was reduced by adding GGBS due to its fast reactivity. Both GGBS and cement promoted the formation of all types of gels (i.e., C-S-H, C-A-S-H, and N-A-S-H). As a result, it was found that a specified mixing proportion could be used to improve the compressive strength over 30 MPa at both the ambient and hot curing conditions.

• Advances in concrete construction
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• 제12권5호
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• pp.429-437
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• 2021

In the era of building engineering the intensification of Self Compacting Concrete (SCC) is world-shattering magnetism. It has lot of rewards over ordinary concrete i.e., enrichment in production, cutback in manpower, brilliant retort to load and vibration along with improved durability. In the present study, the mechanical strength of CM-2 (SCC containing 10% of rice husk ash (RHA) as cement replacement and 600 grams of glass fibers per cubic meter) was investigated at various dosages of cement replacement by fly ash (FA) and GGBS. A total of 17 SCC mixtures including two control SCC mixtures (CM-1 and CM-2) were developed for investigating fresh and hardened properties in which, ten ternary cementitious blends of SCC by blending OPC+RHA+FA, OPC+RHA+GGBS and five quaternary cementitious blends (OPC+RHA+FA+GGBS) at different replacement dosages of FA and GGBS were developed with reference to CM-2. For constant water-cement ratio (0.42) and dosage of SP (2.5%), the addition of glass fibers (600 grams/m³) in CM-1 i.e., CM-2 shows lower workability but higher mechanical strength. While fly ash based ternary blends (OPC+RHA+FA) show better workability but lower mechanical strength as FA content increases in comparison to GGBS based ternary blends (OPC+RHA+GGBS) on increasing GGBS content. The pattern for mixtures appeared to exhibit higher workablity as that of the concentration of FA+GGBS rises in quaternary blends (OPC+RHA+FA+GGBS). A decrease in compressive strength at 7-days was noticed with an increase in the percentage of FA and GGBS as cement replacement in ternary and quaternary blended mixtures with respect to CM-2. The highest 28-days compressive strength (41.92 MPa) was observed for mix QM-3 and the lowest (33.18 MPa) for mix QM-5.

• 한국건설순환자원학회논문집
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• 제10권4호
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• pp.577-583
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• 2022

본 연구에서는 고로슬래그 미분말, 팽창재 및 무수석고로 이루어진 무기계 자기치유 소재를 사용하여 제조한 자기치유 모르타르의 특성을 조사하였다. 무기계 치유재료의 사용량이 다른 3종류의 자기치유 모르타르를 대상으로 압축강도를 측정하고 정수위 투수시험를 통하여 치유성능을 평가하였다. 치유성능 평가지표로 균열유도재령애 따른 치유율과 등가균열폭을 활용하였다. 자기치유 모르타르의 압축강도 발현, 치유재령에 따른 치유율 변화와 경제성을 고려하여 무기계 혼합재의 최적 사용량을 시멘트 질량 대비 20 %로 제안하였다.

• 대한토목학회논문집
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• 제29권4A호
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• pp.365-371
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• 2009

콘크리트 중의 염화물이온 확산특성의 평가는 농도 차에 의한 방법이 보통이지만, 이 방법은 보통 수개월에서 수년으로 많은 시간이 소요된다. 따라서 최근의 연구는 주로 전위차를 이용하여 염화물이온의 이동을 전기적으로 촉진시켜 전기화학적 이론으로 해석하는 촉진 염화물 확산계수 평가 방법들이 주로 연구되고 있다. 따라서 본 연구에서는 다양한 촉진시험 방법 중 하나인 Tang 등의 방법을 이용하여 구한 염화물이온 확산계수와 인공해수 침지시험을 통하여 구한 농도차 염화물이온 확산계수와의 비교 평가를 실시하였다. 포틀랜드시멘트 콘크리트 및 고로슬래그미분말을 시멘트 질량의 40 및 60% 혼합한 고로슬래그미분말 혼합 콘크리트에 대하여 각각 물-결합재비 40, 45, 50 및 60%로 하여 촉진 염화물이온 침투깊이 및 염화물이온의 확산계수를 평가하였다. 염화물이온의 확산계수 및 침투깊이는 시멘트의 수화가 진행될수록, 물-결합재비가 작을수록 크게 감소하였다. 또한 시험 방법별 염화물이온 확산계수의 회귀분석 결과에 의하면, 촉진 및 침지시험으로부터 구한 염화물이온의 확산계수 사이에는 선형함수 형태의 상관관계가 있었으며 결정계수 0.96으로 좋은 상관성을 나타내었다.

• Computers and Concrete
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• 제33권2호
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.

• Advances in concrete construction
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• 제16권3호
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• pp.155-167
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• 2023

The penetrated chloride in concrete has different behavior with mix proportions and local exposure conditions, even in the same environments, so that it is very important to quantify surface chloride contents for durability design. As well known, the surface chloride content which is a key parameter like external loading in structural safety design increases with exposure period. In this study, concrete samples containing OPC (Ordinary Portland Cement), GGBFS (Ground Granulated Blast Furnace Slag), and FA (Fly Ash) had been exposed to submerged, tidal, and splash area for 5 years, then the surface chloride contents changing with exposure period were evaluated. The surface chloride contents were obtained from the chloride profile based on the Fick's 2nd Law, and the regression analysis for them was performed with exponential and square root function. After exposure period of 5 years in submerged and tidal area conditions, the surface chloride content of OPC concrete increased to 6.4 kg/m³ - 7.3 kg/m³, and the surface chloride content of GGBFS concrete was evaluated as 7.3 kg/m³ - 11.5 kg/m³. In the higher replacement ratio of GGBFS, the higher surface chloride contents were evaluated. The surface chloride content in FA concrete showed a range of 6.7 kg/m³ to 9.9 kg/m³, which was the intermediate level of OPC and GGBFS concrete. In the case of splash area, the surface chloride contents in all specimens were from 0.59 kg/m³ to 0.75 kg/m³, which was the lowest of all exposure conditions. Experimental constants available for durability design of chloride ingress were derived through regression analysis over exposure period. In the concrete with GGBFS replacement ratio of 50%, the increase rate of surface chloride contents decreased rapidly as the water to binder ratio increased.

• Advances in concrete construction
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• 제16권6호
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• pp.303-316
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• 2023

Using industrial wastes and construction and demolition (C&D) wastes is potentially advantageous for concrete production in terms of sustainability improvement. In this paper, a sustainable Self-Compacting Concrete (SCC) made with industrial wastes and C&D wastes was proposed by considerably replacing natural counterparts with recycled coarse aggregates (RCAs) and supplementary cementitious materials (SCMs) (i.e., Fly ash (FA), ground granulated blast furnace slag (GGBS) and silica fume (SF)). A total of 12 SCC mixes with various RCAs and different combination SCMs were prepared, which comprise binary, ternary and quaternary mixes. The mechanical properties in terms of compressive strength and static elasticity modulus of recycled aggregates (RA-SCC) mixes were determined and analyzed. Microstructural study was implemented to analyze the reason of improvement on mechanical properties. By means of life cycle assessment (LCA) method, the environmental impacts of RA-SCC with various RCAs and SCMs were quantified, analyzed and compared in the system boundary of "cradle-to-gate". In addition, the comparison of LCA results with respect to mechanical properties was conducted. The results demonstrate that the addition of proposed combination SCMs leads to significant improvement in mechanical properties of quaternary RA-SCC mixes with FA, GGBS and SF. Furthermore, quaternary RA-SCC mixes emit lowest environmental burdens without compromising mechanical properties. Thus, using the combination of FA, GGBS and SF as cement substitution to manufacture RA-SCC significantly improves the sustainability of SCC by minimizing the depletion of cement and non-renewable natural resources.

• Steel and Composite Structures
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• 제52권3호
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• pp.293-312
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• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.

• Computers and Concrete
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• 제34권2호
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• pp.213-230
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• 2024

The use of waste tires and industrial wastes such as fly ash (FA) and ground granulated blast furnace slag (GGBS) in concrete is an important issue in terms of sustainability. In this study, the effect of parameters affecting the physical, mechanical and microstructural properties of FA/GGBS-based geopolymer concretes with waste rubber fiber was investigated. For this purpose, the effects of rubber fiber percentage (0.6%, 0.9%, 1.2%), binder (75FA25GGBS, 50FA50GGBS, 25FA75GGBS) and curing temperature (75 ℃, 90 ℃ and 105 ℃) were investigated. The Taguchi-Grey Relational Analysis (TGRA) method was used to obtain optimum parameter levels of rubber fiber geopolymer concrete (RFGC). The slump, fresh and hardened density, compressive strength, flexural strength, static and dynamic modulus of elasticity, ultrasonic pulse velocity (UPV) tests and scanning electron microscopy (SEM) analysis were performed on the produced concretes. The analysis of variance (ANOVA) method was used to statistically determine the effects of the parameters on the experimental results. A confirmation test was performed to test the accuracy of the optimum values found by the TGRA method. With the increase of GGBS percentage, the compressive strength of RFGC increased up to 196%. The increase in rubber fiber percentage and curing temperature adversely affected the mechanical properties of RFGC. As a result of TGRA, the optimum value was found to be A1B3C1. ANOVA results showed that the most effective parameter on the experimental results was the binder with 99% contribution percentage. It is understood from the SEM images that the optimum concrete had a denser microstructure and less capillary cracks and voids. For this study, the use of the TGRA method in multiple optimization has proven to provide very useful and reliable results. In cases where many factors are effective on its strength and durability, such as geopolymer concrete, using the TGRA method allows for finding the optimum value of the parameters by saving both time and cost.