• Computers and Concrete
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• 제2권5호
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• pp.411-421
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• 2005

A linear programming problem of the optimal proportioning of concrete aggregates is discussed; and a self-adaptive genetic algorithm is developed to solve this problem. The proposed method is based on changing a range of variables for capturing the feasible region of the optimum solution. A computational verification of this method is compared with the results of the linear programming.

• Computers and Concrete
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• 제23권6호
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• pp.421-431
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• 2019

The alarming rate of depletion of natural stone based coarse aggregates is a cause of great concern. The coarse aggregates occupy nearly 60-70% by volume of concrete being produced. Research efforts are on to look for alternatives to stone based coarse aggregates from sustainability point of view. Response surface methodology (RSM) is adopted to study and address the effect of ferrochrome slag (FCS) replacement to coarse aggregate replacement in the ordinary Portland cement (OPC) based concretes. RSM involves three different factors (ground granulated blast furnace slag (GGBS) as binder, flyash (FA) as binder, and FCS as coarse aggregate), with three different levels (GGBS (0, 15, and 30%), FA (0, 15, and 30%) and FCS (0, 50, and 100%)). Experiments were carried out to measure the responses like, workability, density, and compressive strength of FCS based concretes. In order to optimize FCS replacement in the OPC based concretes, three different traditional optimization techniques were used (grey relational analysis (GRA), technique for order of preference by similarity (TOPSIS), and desirability function approach (DFA)). Traditional optimization techniques were accompanied with principal component analysis (PCA) to calculate the weightage of responses measured to arrive at the final ranking of replacement levels of GGBS, FA, and FCS in OPC based concretes. Hybrid combination of PCA-TOPSIS technique is found to be significant when compared to other techniques used. 30% GGBS and 50% FCS replacement in OPC based concrete was arrived at, to be optimal.

• International Journal of Concrete Structures and Materials
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• 제9권2호
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• pp.185-206
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• 2015

This paper presents statistical models developed to study the influence of key mix design parameters on the properties of lightweight self-consolidating concrete (LWSCC) with expanded shale (ESH) aggregates. Twenty LWSCC mixtures are designed and tested, where responses (properties) are evaluated to analyze influence of mix design parameters and develop the models. Such responses included slump flow diameter, V-funnel flow time, J-ring flow diameter, J-ring height difference, L-box ratio, filling capacity, sieve segregation, unit weight and compressive strength. The developed models are valid for mixes with 0.30-0.40 water-to-binder ratio, high range water reducing admixture of 0.3-1.2 % (by total content of binder) and total binder content of $410-550kg/m^3$. The models are able to identify the influential mix design parameters and their interactions which can be useful to reduce the test protocol needed for proportioning of LWSCCs. Three industrial class ESH-LWSCC mixtures are developed using statistical models and their performance is validated through test results with good agreement. The developed ESH-LWSCC mixtures are able to satisfy the European EFNARC criteria for self-consolidating concrete.

• Steel and Composite Structures
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• 제29권1호
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• pp.67-75
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• 2018

High performance concrete (HPC) depends on various parameters such as the type of cement, aggregate and water reducer amount. Generally, the ready concrete company in various regions according to the requirements and costs, mix design of concrete as well as type of cement, aggregates, and, amount of other components will vary as a result of moment decisions or dynamic optimization, though the ideal conditions will be more applicable for the design of mix proportion of concrete. This study aimed to apply dynamic optimization for mix design of HPC; consequently, the objective function, decision variables, input and output variables and constraints are defined and also the proposed dynamic optimization model is validated by experimental results. Results indicate that dynamic optimization objective function can be defined in such a way that the compressive strength or performance of all constraints is simultaneously examined, so changing any of the variables at each step of the process input and output data changes the dynamic of the process which makes concrete mix design formidable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.391-411
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• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

• Computers and Concrete
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• 제24권3호
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• pp.193-206
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• 2019

Although concrete is the most widely used construction material, its deficiency in shrinkage and low tensile resistance is undeniable. However, the aforementioned defects can be partially modified by addition of fibers. On the other hand, possibility of adding waste materials in concrete has provided a new ground for use of recycled concrete aggregates in the construction industry. In this study, a constant combination of recyclable coarse and fine concrete aggregates was used to replace the corresponding aggregates at 50% substitution percentage. Moreover, in order to investigate the effects of fibers on mechanical and durability properties of recycled aggregate concrete, the amounts of 0.5%, 1%, and 1.5% steel fibers (ST) and 0.05%, 0.1% and 0.15% polypropylene (PP) fibers by volumes were used individually and in hybrid forms. Compressive strength, tensile strength, flexural strength, ultrasonic pulse velocity (UPV), water absorption, toughness, elastic modulus and shrinkage of samples were investigated. The results of mechanical properties showed that PP fibers reduced the compressive strength while positive impact of steel fibers was evident both in single and hybrid forms. Tensile and flexural strength of samples were improved and the energy absorption of samples containing fibers increased substantially before and after crack presence. Growth in toughness especially in hybrid fiber-reinforced specimens retarded the propagation of cracks. Modulus of elasticity was decreased by the addition of PP fibers while the contrary trend was observed with the addition of steel fibers. PP fibers decreased the ultrasonic pulse velocity slightly and had undesirable effect on water absorption. However, steel fiber caused negligible decline in UPV and a small impact on water absorption. Steel fibers reduce the drying shrinkage by up to 35% when was applied solely. Using fibers also resulted in increasing the ductility of samples in failure. In addition, mechanical properties changes were also evaluated by statistical analysis of MATLAB software and smoothing spline interpolation on compressive, flexural, and indirect tensile strength. Using shell interpolation, the optimization process in areas without laboratory results led to determining optimal theoretical points in a two-parameter system including steel fibers and polypropylene.

• Journal of The Korean Society of Agricultural Engineers
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• 제55권3호
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• pp.25-33
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• 2013

In this study, the optimum mix proportions of rain garden structure concrete were decided and the mechanical properties were evaluated. Experimental parameters were blast furnace slag, hwang-toh, recycled aggregates and natural jute fibers. The target compressive strength and chloride ion penetration were more than 24 MPa and less than 1000 coulombs, respectively. The response surface method was used for statistical optimization of experimental results. The optimal mixing ratios of the blast furnace slag, hwang-toh, recycled coarse aggregate and jute fiber volume fraction were determined 59.98 %, 8.74 %, 12.12 % and 0.2 %, respectively. The compressive strength, flexural strength and chloride ion penetration test results of optimum mix ratio showed that the 24.56 MPa, 3.88 MPa and 999.08 columbs, respectively.

• Computers and Concrete
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• 제32권2호
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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.

• Steel and Composite Structures
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• 제49권1호
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• pp.91-107
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• 2023

An effective approach to promoting sustainability within the construction industry is the use of recycled aggregate concrete (RAC) as a substitute for natural aggregates. Ensuring the frost resilience of RAC technologies is crucial to facilitate their adoption in regions characterized by cold temperatures. The main aim of this study was to use the Random Forests (RF) approach to forecast the frost durability of RAC in cold locations, with a focus on the durability factor (DF) value. Herein, three optimization algorithms named Sine-cosine optimization algorithm (SCA), Black widow optimization algorithm (BWOA), and Equilibrium optimizer (EO) were considered for determing optimal values of RF hyperparameters. The findings show that all developed systems faithfully represented the DF, with an R² for the train and test data phases of better than 0.9539 and 0.9777, respectively. In two assessment and learning stages, EO - RF is found to be superior than BWOA - RF and SCA - RF. The outperformed model's performance (EO - RF) was superior to that of ANN (from literature) by raising the values of R² and reducing the RMSE values. Considering the justifications, as well as the comparisons from metrics and Taylor diagram's findings, it could be found out that, although other RF models were equally reliable in predicting the the frost durability of RAC based on the durability factor (DF) value in cold climates, the developed EO - RF strategy excelled them all.

• Biotechnology and Bioprocess Engineering:BBE
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• 제5권2호
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• pp.99-105
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• 2000

Cell-Cell interaction and the extracellular matrix (ECM) are belisved to play essential roles during in vitro culturing of primary hepatocytes in the control of differentiation and in the maintenance of tissue spcific functions. The objective of this study was to examine the effects of degree of cell-cell contact (DCC) on liver sperific function of rat promary hepatocytes. Hepatocyte aggregates with various with various degrees of cell-cell contantact, I. e., dispersed cell, longish aggregate, rugged aggregate, and smooth spheroid were obtained at 1, 5-6, 15-20, and 36-48 hrs, respectively in suspension cultures grown in spinner flasks embedded in Caalginate bead and collagen gel in order. The may result from mass transfer limitation and shear damage caused by agitation during aggregation. The rugged aggregate showed a higer viability and albumin secretion rate than the dispersed cells or the other aggregates. This result indicates the possible enhancement of a bioartificial liver's (BAL) performance using primary hepatocytes and the reduction in time to prepare a BAL through optimization of the immobilization time.