• Asia-Pacific Journal of Business
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• v.10 no.2
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• pp.13-26
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• 2019

This paper aims to empirically examine the short-run and long-run aggregate demand for the US imports using quarterly economic data for the period 2000-2018 including aggregate imports, final expenditure components, gross fixed capital formation and relative price of imports. According to the results of both multivariate co-integration analysis and error correction model, the above variables are all cointegrated and significant differences are found to exist among the long-run partial elasticities of imports as regards different macro components of final expenditure. Partial elasticities with respect to government expenditure, gross fixed capital formation, exports and relative price of import are found to be positive while imports seems to respond negatively to changes in private consumption, implying that an increase in private consumption could result in a significant reduction in demand for imports in the long run. With regard to the relative import prices, the results appear to indicate a relatively insignificant influence on the aggregate imports in the US in the long run. However, an error correction model designed for predicting the short-term variability shows that only exports have an impact on the imports in the short run.

• Steel and Composite Structures
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• v.49 no.1
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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.

• Journal of the Korean Recycled Construction Resources Institute
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• v.1 no.1
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• pp.89-97
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• 2005

Recently, for the problem solution of demand and supply imbalance of fine aggregate due to the shortage of natural fine aggregate resource and the environment regulation on sea sand extraction in the construction field, the studies for the application of recycled fine aggregate using waste concrete are being progressed versatilely. On the other hand, the treatment of fly-ashes that of industrial by-product originated in the steam power plant is discussed by the continuous increasing of origination quantities. In the ease of using fly-ash, advantages are the improvement of workability, viscosity and long-time strength, and the reduction of hydration heat under the early ages, as the admixtures for concrete, but the studies for the application of fly-ash as recycled concrete admixtures are inadequacy. There fore, in this study, through investigating the properties of fresh, hardened and durability according to the replacement of recycled fine aggregate and fly-ash, it is intended to propose the fundamental data for structural application of recycled concrete using recycled fine aggregate and fly-ash. As the result of this study, they arc shown that the engineering properties and durability, in the case of replacement ratio 100% of recycled fine aggregate, arc similar to those of concrete using natural fine aggregate, so it is considered that recycled fine aggregate could be used as the fine aggregate for concrete. Also, the performances of recycled concrete are improved by replacing fly-ash.

• Computers and Concrete
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• v.12 no.2
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• pp.151-167
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• 2013

Prediction of concrete properties is an important issue for structural engineers and different methods are developed for this purpose. Most of these methods are based on experimental data and use measured data for parameter estimation. Three typical methods of output estimation are Categorized Linear Regression (CLR), Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In this paper a statistical cleansing method based on CLR is introduced. Afterwards, MLR and ANN approaches are also employed to predict the compressive strength of structural lightweight aggregate concrete. The valid input domain is briefly discussed. Finally the results of three prediction methods are compared to determine the most efficient method. The results indicate that despite higher accuracy of ANN, there are some limitations for the method. These limitations include high sensitivity of method to its valid input domain and selection criteria for determining the most efficient network.

• Computers and Concrete
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• v.20 no.1
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• pp.31-38
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• 2017

In the present study an Artificial Neural Network (ANN) was used to predict the compressive strength of self-compacting concrete. The data developed experimentally for self-compacting concrete and the data sets of a total of 99 concrete samples were used in this work. ANN's are considered as nonlinear statistical data modeling tools where complex relationships between inputs and outputs are modeled or patterns are found. In the present ANN model, eight input parameters are used to predict the compressive strength of self-compacting of concrete. These include varying amounts of cement, coarse aggregate, fine aggregate, fly ash, fiber, water, super plasticizer (SP), viscosity modifying admixture (VMA) while the single output parameter is the compressive strength of concrete. The importance of different input parameters for predicting the strengths at various ages using neural network was discussed in the study. There is a perfect correlation between the experimental and prediction of the compressive strength of SCC based on ANN with very low root mean square errors. Also, the efficiency of ANN model is better compared to the multivariable regression analysis (MRA). Hence it can be concluded that the ANN model has more potential compared to MRA model in developing an optimum mix proportion for predicting the compressive strength of concrete without much loss of material and time.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10b
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• pp.961-966
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• 2000

It is difficult to change or remedy concrete structure after hardened. It is usual to evaluate the quality of hardened concrete using several test method. This study was performed to make fundamental data that could be used to evaluate the quality of hardened concrete. This study is to estimate mix proportion of hardened concrete. Each elements of concrete needed different estimation methods. First, the cement that handled by the most important compounds measured by XRF(X-ray fluorecence) machine with scanning Ca-K${\alpha}$. Second, the coarse aggregate that divided by maximum size measured by the area comparison method that starts from the assumption of uniform distribution. Third, the fine aggregate measured by the weight comparison method that needs several prerequsite constants which concerned cement hydration reaction. Fourth, the water content would be estimated by expert system that has data base of design data, the contents of above estimation results, the characteristics of concrete strength. As the result of the above research, some conclusions are as follows. The cement estimation method resulted by reliability of mean 96.7%, standard deviation 3.92. The area comparison method resulted by reliability of mean 95.3%, standard deviation 2.08. The weight comparison method resulted by reliability of mean 93.3%, standard deviation 3.35.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.587-595
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• 2001

Precomputation and materialization of parts. commonly called views of a data cube is a common technique in data warehouses The view is defined as the result of a query which is defined through aggregate functions In this paper we introduce the concept of fine granule view. The fine granule view is the result of a query defined through aggregate functions and the range on each dimension, where the subdivision of each dimension is based on queries access patterns. For the representation and selection of fine granule views to materialize, we define the ANO-OR cube graph and AND-OR minimum cost graph. With these structures, we propose a fine granule view materialization method. And through experiments, we evaluate the performance of the proposed method.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.707-719
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• 2018

Various time series representation methods have been proposed for efficient time series clustering and classification. Lin et al. (DMKD, 15, 107-144, 2007) proposed a symbolic aggregate approximation (SAX) method based on symbolic representations after approximating the original time series using piecewise local mean. The performance of SAX therefore depends heavily on how well the piecewise local averages approximate original time series features. SAX equally divides the entire series into an arbitrary number of segments; however, it is not sufficient to capture key features from complex, large-scale time series data. Therefore, this paper considers data-adaptive local constant approximation of the time series using the unbalanced Haar wavelet transformation. The proposed method is shown to outperforms SAX in many real-world data applications.

• Computers and Concrete
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• v.24 no.2
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• pp.137-150
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• 2019

In this paper, surrogate models such as multivariate adaptive regression splines (MARS) and M5P model tree (M5P MT) methods have been investigated in order to propose a new formulation for the 28-days compressive strength of self-compacting concrete (SCC) incorporating metakaolin as a supplementary cementitious materials. A database comprising experimental data has been assembled from several published papers in the literature and the data have been used for training and testing. In particular, the data are arranged in a format of seven input parameters covering contents of cement, coarse aggregate to fine aggregate ratio, water, metakaolin, super plasticizer, largest maximum size and binder as well as one output parameter, which is the 28-days compressive strength. The efficiency of the proposed techniques has been demonstrated by means of certain statistical criteria. The findings have been compared to experimental results and their comparisons shows that the MARS and M5P MT approaches predict the compressive strength of SCC incorporating metakaolin with great precision. The performed sensitivity analysis to assign effective parameters on 28-days compressive strength indicates that cementitious binder content is the most effective variable in the mixture.