• Journal of the Korea Institute of Building Construction
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• v.21 no.3
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• pp.195-202
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• 2021

Concrete quality is mainly managed through the reliable prediction and control of compressive strength. Although related industries have established a relevant datasets based on the mixture proportions and compressive strength gain, whereas they have not been shared due to various reasons including technology leakage. Consequently, the costs and efforts for quality control have been wasted excessively. This study aimed to develop a web-based numerical model, which would present diverse optimal values including concrete strength prediction to the user, and to establish a sustainable database (DB) collection system by inducing the data entered by the user to be collected for the DB. The system handles the overall technology related to the concrete. Particularly, it predicts compressive strength at a mean accuracy of 89.2% by applying the artificial neural network method, modeled based on extensive DBs.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.35-43
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• 2023

In this study, the mechanical properties of concrete mixed with non-sintered hwangto(NHT) as an alternate material for cement were evaluated, and the compressive strength prediction equation of concrete based on ultrasonic pulse velocity analysis was proposed. Cement replacement rates for mixed NHT were set to 0, 15, and 30%, and design compressive strength was set to 30 and 45MPa to evaluate the effect on the amount of cement and NHT powder. The mechanical properties items analyzed were compressive strength, ultrasonic pulse velocity, and elastic modulus, and were measured on days 1, 3, 7, and 28. As the replacement rate of NHT increased, the mechanical properties tended to decrease. In addition, as a result of analyzing the correlation between compressive strength and ultrasonic pulse velocity, the correlation coefficient(R²) showed a high relationship(R²=0.95) on concrete mixed with NHT.

• Computers and Concrete
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• v.21 no.4
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• International Journal of Concrete Structures and Materials
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• v.10 no.2
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• pp.205-219
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• 2016

High-early-strength-concrete (HESC) made of Type III cement reaches approximately 50-70 % of its design compressive strength in a day in ambient conditions. Experimental investigations were made in this study to observe the effects of temperature, curing time and concrete strength on the accelerated development of compressive strength in HESC. A total of 210 HESC cylinders of $100{\times}200mm$ were tested for different compressive strengths (30, 40 and 50 MPa) and different curing regimes (with maximum temperatures of 20, 30, 40, 50 and $60^{\circ}C$) at different equivalent ages (9, 12, 18, 24, 36, 100 and 168 h) From a series of regression analyses, a generalized rate-constant model was presented for the prediction of the compressive strength of HESC at an early age for its future application in precast prestressed units with savings in steam supply. The average and standard deviation of the ratios of the predictions to the test results were 0.97 and 0.22, respectively.

• Magazine of the Korean Society of Agricultural Engineers
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• v.42 no.2
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• pp.99-107
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• 2000

The objective of this study are to compare the development of compressive strength of epoxy mortars used as repairing materials with respect to maturity , and to propose a model predicting strength development of epoxy mortars. A series of tests are carried out for the hardener contents of 30, 40 and 50 percentage of epoxy resin and compressive strengths are measured at the age of 6, 12, 24, 72, 120 and 168 hours respectively under the cure temperature of 0, 10, 20 and 3$0^{\circ}C$. The datum temperature is estimated by measured strengths, and the maturity is calculated with the estimated datum temperature. The compressive strength of epoxy mortars can be predicted by regression analysis from the maturity-compressive strength relationship.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.2
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• pp.177-183
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• 1999

The major object of this study is to investigate experimentally the experimental equation by the non-destructive testing methods of ultrasonic pulse velocity, rebound number, combined method of ultrasonic pulse velocity and rebound number, maturity which are applicable to the evaluation of compressive strength of concrete at early ages. Also test result of mix are statistically analyzed to infer the correlation coefficient between the maturity and the compressive strength of concrete. The results show good application of Logistic curve for estimating strength development under various curing temperature. The relation between ultrasonic pulse velocity, rebound number, combined method of ultrasonic pulse velocity and rebound number and compressive strength of concrete have low correlation coefficient, but maturity method show good correlation coefficient.

• Computers and Concrete
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• v.33 no.1
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• pp.77-90
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• 2024

Waste foundry sand (WFS) is the waste product that cause environmental hazards. WFS can be used as a partial replacement of cement or fine aggregates in concrete. A database comprising 234 compressive strength tests of concrete fabricated with WFS is used. To construct the machine learning-based prediction models, the water-to-cement ratio, WFS replacement percentage, WFS-to-cement content ratio, and fineness modulus of WFS were considered as the model's inputs, and the compressive strength of concrete is set as the model's output. A base extreme gradient boosting (XGBoost) model together with two hybrid XGBoost models mixed with the tunicate swarm algorithm (TSA) and the salp swarm algorithm (SSA) were applied. The role of TSA and SSA is to identify the optimum values of XGBoost hyperparameters to obtain the higher performance. The results of these hybrid techniques were compared with the results of the base XGBoost model in order to investigate and justify the implementation of optimisation algorithms. The results showed that the hybrid XGBoost models are faster and more accurate compared to the base XGBoost technique. The XGBoost-SSA model shows superior performance compared to previously published works in the literature, offering a reduced system error rate. Although the WFS-to-cement ratio is significant, the WFS replacement percentage has a smaller influence on the compressive strength of concrete. To improve the compressive strength of concrete fabricated with WFS, the simultaneous consideration of the water-to-cement ratio and fineness modulus of WFS is recommended.

• Journal of the Korea Institute of Building Construction
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• v.19 no.6
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• pp.503-510
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• 2019

Recently, construction technology of RC structures must be examined for creep in concrete. The factors affecting the creep prediction of concrete and the results of creep in domestic construction field were reviewed. The longer the creep test period and the higher the compressive strength, the higher the creep prediction accuracy. The higher the curing temperature, the higher the initial strength development of the concrete, but the difference in the creep coefficients increased over time. Based on the results of creep evaluation in the domestic construction field and lab. tests, a modified predictive model that complements the ACI-209 model was proposed. In the creep prediction of real members using general to high strength concrete, the test period and temperature should be considered precisely.

• Computers and Concrete
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• v.10 no.6
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• pp.649-662
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• 2012

This paper presents the results of an investigation on the compressive strength and weight loss of mortars containing three types of fillers as cement replacements; Limestone Filler (LF), Silica Fume (SF) and Trass (TR), subjected to elevated temperatures including $400^{\circ}C$, $600^{\circ}C$, $800^{\circ}C$ and $1000^{\circ}C$. Results indicate that addition of TR to blended cements, compared to SF addition, leads to higher compressive strength and lower weight loss at elevated temperatures. In order to model the influence of the different parameters on the compressive strength and the weight loss of specimens, artificial neural networks (ANNs) were adopted. Different diagrams were plotted based on the predictions of the most accurate networks to study the effects of temperature, different fillers and cement content on the target properties. In addition to the impressive RMSE and $R^2$ values of the best networks, the data used as the input for the prediction plots were chosen within the range of the data introduced to the networks in the training phase. Therefore, the prediction plots could be considered reliable to perform the parametric study.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2004.05a
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• pp.101-104
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• 2004

If predicting of compressive strength of construction in construction field at early age is possibile, rational strength management & schedule plan is possible. With method for predicting strength of concrete, many researchers have been making study of maturity method. On the other hand, nowadays rationalization of construction capacity and reduction of a term of works due to improvement of construction capacity and application of a new method of construction is gathering strength with important issue. In accordance with this present condition, construction is being progressed in winter, but proper construction mothed and countermeasure for strength management is not established in case of winter construction. Therefore to investigate application in construction field at winter of strength prediction model that developed at former study, this study aim to measure application of developed strength prediction model through manufacture of mock-up concrete according to kind of strength level at 5$^{\circ}C$.