• Journal of the Korea Institute of Building Construction
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• v.18 no.3
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• pp.235-242
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• 2018

The objective of the paper is to investigate the effect of mix designing factors on the workability and rheological parameters of self compacting concrete in order to facilitate the difficulties of quality control of high sensitivity of SCC. Mix proportions of SCC were prepared with various conditions of coarse, and fine aggregate, and unit water content, and the SCC mixtures were tested on workability, rheological properties to provide basic data for quantitative evaluation. Test results indicated that the yield stress of SCC decreased with increasing the coarse aggregate volume ratio, and increased with increasing the amount of VMA. However, unit water content, fine aggregate type, and air content didn't affect the yield stress value. The plastic viscosity according to the mixing factors showed a similar tendency to the yield stress. In addition, there was no correlation between yield stress and workability (flow, T50, V-lot). However, there was closely correlation among plastic viscosity and T50 and V-lot. Especially, T50 and V-lot time decreased with decreasing plastic viscosity.

• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.537-542
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• 2015

Database Shard is a technique that can be queried and stored by dividing one logical table into multiple databases horizontally. In order to analyze the shard data with aggregate or analysis functions, a process is required that integrates partial results on each shard database. In this paper, we introduce the design and implementation of a distributed processing system for aggregation and analysis on the CUBRID Shard distributed database, which is an open source database management system. The implemented system can accelerate the analysis onto multiple shards of partitioned tables; it shows efficient aggregation on shard distributed databases compared to stand-alone databases.

• Fire Science and Engineering
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• v.18 no.1
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• pp.31-36
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• 2004

The purpose of this study is to present data for the reusing, rehabilitation and estimation of safety of RC structure damaged by fire, and for the prevention of explosive spatting by investigation the properties of explosive spalling, compressive strength and ultrasonic pulse velocity according to kinds of fine aggregate, admixture and water-cement ratios. In explosive spalling properties with kinds of aggregate, explosive spalling does not appear or little at surface in the case of used sea sand, but the case of using recycled sand or crushed sand is worse and worse. Property with the kind of admixture does not appear specially. And high strength concrete with W/C 30.5% was taken spalling, but 55% does not appear. It is found that residual compressive strength after exposed at high temperature showed 45％ in W/C 55%, and 64％ in W/C 30.5％ of its original strength averagely. Ultrasonic pulse velocity is different with kinds of aggregate. W/C. and heating time. When 3 month age after heating ultrasonic pulse velocity is recovered abut 1.3％～8.4％ of its 1 month age after heating.

• Geomechanics and Engineering
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• v.14 no.2
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• pp.151-159
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• 2018

Annually, the global production of construction aggregates reaches over 40 billion tons, making aggregates the largest mining sector by volume and value. Currently, the aggregate industry is shifting from sand to hard rock as a result of legislation limiting the extraction of natural sands and gravels. A major implication of this change in the aggregate industry is the need for understanding rock fragmentation and energy absorption to produce more cost-effective aggregates. In this paper, we focused on incorporating dynamic rock and soil mechanics to understand the effects of loading rate and water saturation on the rock fragmentation and energy absorption of three different sandstones (Red, Berea and Buff) with different pore sizes. Rock core samples were prepared in accordance to the ASTM standards for compressive strength testing. Saturated and dry samples were subsequently prepared and fragmented via fast and dynamic compressive strength tests. The particle size distributions of the resulting fragments were subsequently analyzed using mechanical gradation tests. Our results indicate that the rock fragment size generally decreased with increasing loading rate and water content. In addition, the fragment sizes in the larger pore size sample (Buff sandstone) were relatively smaller those in the smaller pore size sample (Red sandstone). Notably, energy absorption decreased with increased loading rate, water content and rock pore size. These results support the conclusion that rock fragment size is positively correlated with the energy absorption of rocks. In addition, the rock fragment size increases as the energy absorption increases. Thus, our data provide insightful information for improving cost-effective aggregate production methods.

• Steel and Composite Structures
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• v.38 no.1
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• pp.103-120
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• 2021

This research examines the eccentric compression performance of composite columns composed of recycled aggregate concrete (RAC)-filled square steel tube and profile steel. A total of 17 specimens on the composite columns with different recycled coarse aggregate (RCA) replacement percentage, RAC strength, width to thickness ratio of square steel tube, profile steel ratio, eccentricity and slenderness ratio were subjected to eccentric compression tests. The failure process and characteristic of specimens under eccentric compression loading were observed in detail. The load-lateral deflection curves, load-train curves and strain distribution on the cross section of the composite columns were also obtained and described on the basis of test data. Results corroborate that the failure characteristics and modes of the specimens with different design parameters were basically similar under eccentric compression loads. The compression side of square steel tube yields first, followed by the compression side of profile steel. Finally, the RAC in the columns was crushed and the apparent local bulging of square steel tube was also observed, which meant that the composite column was damaged and failed. The composite columns under eccentric compression loading suffered from typical bending failure. Moreover, the eccentric bearing capacity and deformation of the specimens decreased as the RCA replacement percentage and width to thickness ratio of square steel tube increased, respectively. Slenderness ratio and eccentricity had a significantly adverse effect on the eccentric compression performance of composite columns. But overall, the composite columns generally had high-bearing capacity and good deformation. Meanwhile, the mechanism of the composite columns under eccentric compression loads was also analysed in detail, and the calculation formulas on the eccentric compression capacity of composite columns were proposed via the limit equilibrium analysis method. The calculation results of the eccentric compression capacity of columns are consistent with the test results, which verify the validity of the formulas, and the conclusions can serve as references for the engineering application of this kind of composite columns.

• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.17-25
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• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

• Steel and Composite Structures
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• v.52 no.4
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• pp.419-434
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• 2024

A flat slab is a structural system where columns directly support it without the presence of beam elements. However, despite its wide advantages, this structural system undergoes a major deficiency where stresses are concentrated around the column perimeter, resulting in the progressive collapse of the entire structure as a result of losing the shear transfer mechanisms at the cracked interface. Predicting the punching shear capacity of RC flat slabs is a challenging problem where the factors contributing to the overall slab strength vary broadly in their significance and effect extent. This study proposed a new expression for predicting the slab's capacity in punching shear using a nonuniform concrete tensile stress distribution assumption to capture, as well as possible, the induced strain effect within a thick RC flat slab. Therefore, the overall punching shear capacity is composed of three parts: concrete, aggregate interlock, and dowel action contributions. The factor of the shear span-to-depth ratio (a_v/d) was introduced in the concrete contribution in addition to the aggregate interlock part using the maximum aggregate size. Other significant factors were considered, including the concrete type, concrete grade, size factor, and the flexural reinforcement dowel action. The efficiency of the proposed model was examined using 86 points of published experimental data from 19 studies and compared with five code standards (ACI318, EC2, MC2010, CSA A23.3, and JSCE). The obtained results revealed the efficiency and accuracy of the model prediction, where a covariance value of 4.95% was found, compared to (13.67, 14.05, 15.83, 19.67, and 20.45) % for the (ACI318, CSA A23.3, MC2010, EC2, and JSCE), respectively.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.1-9
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• 2014

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.

• Computers and Concrete
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• v.17 no.4
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• pp.489-498
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• 2016

In this paper, the flexural strength ($f_{fs}$) and splitting tensile strength ($f_{sts}$) of concrete containing different proportions of fly ash have been modeled by using gene expression programming (GEP). Two GEP models called GEP-I and GEP-II are constituted to predict the $f_{fs}$ and $f_{sts}$ values, respectively. In these models, the age of specimen, cement, water, sand, aggregate, superplasticizer and fly ash are used as independent input parameters. GEP-I model is constructed by 292 experimental data and trisected into 170, 86 and 36 data for training, testing and validating sets, respectively. Similarly, GEP-II model is constructed by 278 experimental data and trisected into 142, 70 and 66 data for training, testing and validating sets, respectively. The experimental data used in the validating set of these models are independent from the training and testing sets. The results of the statistical parameters obtained from the models indicate that the proposed empirical models have good prediction and generalization capability.

• Fire Science and Engineering
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• v.18 no.3
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• pp.51-56
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• 2004

A fire outbreak in a reinforcement concrete structure looses the organism by the different contraction and expansion of hardened cement pastes and aggregate, and causes cracks by thermal stress, leading to the deterioration of the durability. So concrete reinforcement structure is damaged partial or whole structure system. Therefore diagnosis of deterioration is needed based on mechanism of fire deterioration in general concrete structures. Fundamental information and data on the properties of concrete exposed to high temperature are necessary for accurate diagnosis of deterioration. In this study, it was presented data for the accurate diagnosis and selection of repair and reinforcement system for the deteriorated concrete heated highly, various concrete such as standard design compressive strength, fine aggregate and admixture were exposed to a high temperature environment. And fundamental data were measured engineering properties such as explosive spatting, ultrasonic pulse velocity and compressive strength.