• Computers and Concrete
• /
• 제5권1호
• /
• pp.75-88
• /
• 2008

This paper presents a mathematical model for strength and porosity of mortars made with ternary blends of ordinary Portland cement (OPC), ground rice husk ash (RHA) and classified fly ash (FA). The mortar mixtures were made with Portland cement Type I containing 0-40% FA and RHA. FA and RHA with 1-3% by weight retained on a sieve No. 325 were used. Compressive strength and porosity of the blended cement mortar at the age of 7, 28 and 90 days were determined. The use of ternary blended cements of RHA and FA produced mixes with good strength and low porosity of mortar. A mathematical analysis and two-parameter polynomial model were presented for the strength and porosity estimation with FA and RHA contents as parameters. The computer graphics of strength and porosity of the ternary blend were also constructed to aid the understanding and the proportioning of the blended system.

• 한국콘크리트학회:학술대회논문집
• /
• 한국콘크리트학회 1999년도 학회창립 10주년 기념 1999년도 가을 학술발표회 논문집
• /
• pp.651-654
• /
• 1999

It is required that the compressive strength of concrete should be estimated accurately from the view point of efficient quality control and maintenance of buildings. In this paper, the equations to estimate the compressive strength of concrete using granite aggregates were suggested for both rebound hammer method and ultrasonic pulse velocity method. The results were compared with those for different age or curing condition. The rebound numbers for concrete cured in air were larger than for concrete cured in water. The difference between rebound numbers for concrete cured in water and in air was larger than for concrete cured in water. The difference between rebound numbers for concrete cured in water and in air was larger when water cement ratio was high. Also, with the increase of age, the velocity of ultrasonic pulse for concrete cured in air was measured larger when compared with that in water.

• Steel and Composite Structures
• /
• 제45권6호
• /
• pp.877-894
• /
• 2022

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO₂ emissions in the construction industry. The compressive strength (f_c) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the f_c of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the fc of GPC forecasting process.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2023년도 가을학술발표대회논문집
• /
• pp.187-188
• /
• 2023

This study aimed to evaluate a feasibility of estimating setting time and compressive strength of curing conditions using a Hybrid meter. As a result, It was determined that the measured hardness value at the initial set, final set and at 5MPa of the Hybrid meter were not affected by curing conditions. And the Hybrid meter(A) is confirmed to have a higher correlation, so it is judged to be more suitable for pratical use.

• 한국농공학회논문집
• /
• 제48권3호
• /
• pp.85-94
• /
• 2006

Practially useful method of steel fiber for construction work is presented in this study. The most important purpose of this study is to develop a model which can predict mechanical behavior of the structure according to aspect ratio and volume fraction of steel fiber. Experiments on compressive strength, elastic modulus, and splitting strength were performed with self-made cylindrical specimens of variable aspect ratios and volume fractions. The experiment showed that compressive strength was not in direct proportion to volume fraction which doesn't seem to have great influence over compressive strength. However, splitting strength showed almost direct proportion to aspect ratio and volume fraction. Improvement of optimal efficiency was confirmed when the aspect ratio was 70. Experiments on flexural strength, fracture energy, and characteristic length were carried out with self-manufactured beams with notch. As a result, increases of flexural strength, fracture energy, and characteristic length according to increase of volume fraction tend to be prominent when aspect ratio is 70. The steel fiber improves concrete to be more ductile and tough. Moreover, regression analysis was the performed and predictable model was developed after determining variables. With comparison and analysis of suggested estimated values and measured data, reliance of the model was verified.

• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2020년도 봄 학술논문 발표대회
• /
• pp.25-26
• /
• 2020

In this study, by applying the concrete compressive strength estimation system Concrete IoT Management System (hereinafter referred to as CIMS) to the concrete slab concrete in the domestic field, the purpose of this study is to confirm the practical use of CIMS and to verify the accuracy of estimating the initial strength of concrete. As a result, it shows a high correlation when the compressive strength and CIMS estimated strength of the specimen for structural management are converted and compared with the integrated temperature. However, in order to determine a more accurate experimental constant, it is necessary to consider the results up to 28 days.

• Computers and Concrete
• /
• 제19권3호
• /
• pp.275-282
• /
• 2017

The aim of this study is to build Machine Learning models to evaluate the effect of blast furnace slag (BFS) and waste tire rubber powder (WTRP) on the compressive strength of cement mortars. In order to develop these models, 12 different mixes with 288 specimens of the 2, 7, 28, and 90 days compressive strength experimental results of cement mortars containing BFS, WTRP and BFS+WTRP were used in training and testing by Random Forest, Ada Boost, SVM and Bayes classifier machine learning models, which implement standard cement tests. The machine learning models were trained with 288 data that acquired from experimental results. The models had four input parameters that cover the amount of Portland cement, BFS, WTRP and sample ages. Furthermore, it had one output parameter which is compressive strength of cement mortars. Experimental observations from compressive strength tests were compared with predictions of machine learning methods. In order to do predictive experimentation, we exploit R programming language and corresponding packages. During experimentation on the dataset, Random Forest, Ada Boost and SVM models have produced notable good outputs with higher coefficients of determination of R2, RMS and MAPE. Among the machine learning algorithms, Ada Boost presented the best R2, RMS and MAPE values, which are 0.9831, 5.2425 and 0.1105, respectively. As a result, in the model, the testing results indicated that experimental data can be estimated to a notable close extent by the model.

• Geomechanics and Engineering
• /
• 제24권4호
• /
• pp.349-358
• /
• 2021

The Brazilian test has been widely used to determine the indirect tensile strength of rock, concrete and other brittle materials. The basic assumption for the calculation formula of Brazilian tensile strength is that the elastic moduli of rock are the same both in tension and compression. However, the fact is that the elastic moduli in tension and compression of most rocks are different. Thus, the formula of Brazilian tensile strength under the assumption of isotropy is unreasonable. In the present study, we conducted Brazilian tests on flat disk-shaped rock specimens and attached strain gauges at the center of the disc to measure the strains of rock. A tension-compression bi-modular model is proposed to interpret the data of the Brazilian test. The relations between the principal strains, principal stresses and the ratio of the compressive modulus to tensile modulus at the disc center are established. Thus, the tensile and compressive moduli as well as the correct tensile strength can be estimated simultaneously by the new formulas. It is found that the tensile and compressive moduli obtained using these formulas were in well agreement with the values obtained from the direct tension and compression tests. The formulas deduced from the Brazilian test based on the assumption of isotropy overestimated the tensile strength and tensile modulus and underestimated the compressive modulus. This work provides a new methodology to estimate tensile strength and moduli of rock simultaneously considering tension-compression bi-modularity.

• Computers and Concrete
• /
• 제30권3호
• /
• pp.225-236
• /
• 2022

In this article, Multi-Gene Genetic Programming (MGGP) is proposed for the estimation of the compressive strength of concrete. MGGP is known to be a powerful algorithm able to find a relationship between certain input space features and a desired output vector. With respect to most conventional machine learning algorithms, which are often used as "black boxes" that do not provide a mathematical formulation of the output-input relationship, MGGP is able to identify a closed-form formula for the input-output relationship. In the study presented in this article, MGPP was used to predict the compressive strength of plain concrete, concrete with fly ash, and concrete with furnace slag. A formula was extracted for each mixture and the performance and the accuracy of the predictions were compared to the results of Artificial Neural Network (ANN) and Extreme Learning Machine (ELM) algorithms, which are conventional and well-established machine learning techniques. The results of the study showed that MGGP can achieve a desirable performance, as the coefficients of determination for plain concrete, concrete with ash, and concrete with slag from the testing phase were equal to 0.928, 0.906, 0.890, respectively. In addition, it was found that MGGP outperforms ELM in all cases and its' accuracy is slightly less than ANN's accuracy. However, MGGP models are practical and easy-to-use since they extract closed-form formulas that may be implemented and used for the prediction of compressive strength.

• 대한조선학회지
• /
• 제25권1호
• /
• pp.33-46
• /
• 1988

This paper describes a simplified method for estimation of the ultimate compressive strength of actual ship panels with initial deflection of complex shape. The proposed method consists of the elastic analysis using the large deflection theory and the rigid-plastic analysis based on the collapse mechanism which also includes the large deformation effect. In order to reduce the computing time for the elastic large deflection theory and the rigid-plastic analysis based on the collapse mechanism which also includes the large deformation effect. In order to reduce the computing time for the elastic large deflection analysis, only one term of Fourier series for the plate deflection is considered. The results of the proposed method are in good agreement with those calculated by the elasto-plastic large deflection analysis using F.E.M. and the computing time of the proposed method is extremely short compared with that of F.E.M.