Payam Shafigh;Sumra Yousuf;Belal Alsubari;Zainah Ibrahim
Advances in concrete construction
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v.15
no.3
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pp.191-202
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2023
Palm oil fuel ash (POFA) is a newly emerging pozzolanic material having high amount of silica content. Various forms of POFA were used in cement-based materials (CBMs) in replacement of cement in different dosages of low and high volume. Although, there are many researches on POFA to be used in concrete and mortar, however, this material was not practically used in the construction industry. Engineers and designers need to be confident to use any new developed materials by knowing all engineering properties at short and long terms. As durability concern, concrete pH value is one of the most important properties. Portland cement produces are alkaline initially, however, it may be reduced due to aging and its components. It is believed that by incorporation of supplementary cementitious materials in CBMs the pH value reduces due to utilization of Ca(OH)2 in pozzolanic reaction. This study is the first attempts to understand the pH value of mortars containing up to 30% POFA under different curing conditions and its changes with time. The results were also compared with the pH of ground granulated ballast furnace slag (GGBFS) and fly ash (FA) content mortars. In addition, the compressive strength of different mortars under different curing conditions were also studied. The results showed that the pH value of control mix (without cementitious materials) was more than all the blended cement mortars indifferent curing conditions at the same ages. However, there was a reducing trend in the pH value of all mortar mixes containing POFA.
Zhang Chengquan;Hamidreza Aghajanirefah;Kseniya I. Zykova;Hossein Moayedi;Binh Nguyen Le
Computers and Concrete
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v.32
no.2
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pp.149-163
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2023
One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R2). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R2=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.
Journal of The Korean Society of Agricultural Engineers
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v.65
no.1
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pp.51-59
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2023
The study is to evaluate the effect of bank protection concrete block products to streams and soils. The effect on three types of bank protection concrete blocks was evaluated.. The first type was manufactured using fly ash, and the second and third type products used fine blast furnace slag powder. The laboratory and field Experiments test results showed the pHs of 9 or less. Also, any heavy metals were not detected in the heavy metal leaching tests. Although some iron (Fe) was partially detected, it still met the water quality standards. In addition, heavy metal was detected from all blocks by the US drinking water evaluation standards method. An on-site water quality and soil contamination tests were performed at the places that the blocks were implemented in practice. The test results showed that the application of the bank protection concrete block product did not lead to the water and soil quality degradation. Therefore, it was found that the hardened bank protection concrete block product did not elute harmful substances such as heavy metals that affect water and soil quality degradation.
KSCE Journal of Civil and Environmental Engineering Research
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v.28
no.1A
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pp.155-163
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2008
On the construction site with trends of large scale, high rise and specialization, testing construction of high performance concrete, superior to conventional concrete, is continued to increase. For bridge construction, application of full staging method is gradually decreasing due to noise, dust, and prolonged construction period. Recently, precast construction, which is optimized to urban environment and shorter work period, gains popularity significantly. In bridge structure, overcrowding arrangement of bar is used to ensure its safety. For the manufacturing of overcrowding arrangement of bar, High flowing self-compacting concrete, which is superior to conventional concrete in flowability and compacting property, should be implemented. In this study, the application of blast-furnace slag and fly ash to binary and ternary blended system on the High flowing self-compacting concrete for bridge structure with overcrowding arrangement of bar is evaluated by flowability in accordance with the first class regulations of Japan Society of Civil Engineering (JSCE).
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.
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.
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.
Ibrahim Albaijan;Hanan Samadi;Arsalan Mahmoodzadeh;Danial Fakhri;Mehdi Hosseinzadeh;Nejib Ghazouani;Khaled Mohamed Elhadi
Steel and Composite Structures
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v.52
no.3
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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.
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.
Journal of the Korea institute for structural maintenance and inspection
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v.10
no.3
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pp.97-105
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2006
The autogenous shrinkage of high-performance concrete is important in that it can lead the early cracks in concrete structures. The purpose of the present study is to explore the autogenous shrinkage of high-performance concrete with admixture and to derive a realistic equation to estimate the autogenous shrinkage model of that. For this purpose, comprehensive experimental program has been set up to observe the autogenous shrinkage for various test series. Major test variables were the type and contents of admixture and water-cement ratio is fixed with 30%. The autogenous shrinkage of HPC with fly ash slightly decreased than that of OPC concrete, but the use of blast furnace slag increased the autogenous shrinkage. Also, the autogenous shrinkage of HPC is found to decrease with increasing shrinkage reduction agent and expansive additive. A prediction equation to estimate the autogenous shrinkage of HPC with admixture was derived and proposed in this study. The proposed equation show reasonably good correlation with test data on autogenous shrinkage of HPC with mineral and chemical admixture.
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