ANALISIS KUAT TEKAN DENGAN APLIKASI GROUND GRANULATED BLAST FURNACE SLAG SEBAGAI PENGGANTI SEBAGIAN SEMEN PADA CAMPURAN BETON

Abstrak : Cara efektif untuk mengurangi dampak lingkungan dari pembuatan semen adalah dengan melakukan subsitusi parsial terhadap campuran mineral di dalamanya. Material yang saat ini bisa dimanfaatkan adalah limbah padat hasil pembakaran, dimana proses pembakaran dapat berdampak terhadap pencemaran lingkungan. PT. Krakatau Semen Indonesia merupakan salah satu penghasil Ground Granulated Blast Furnace Slag yaitu limbah baja yang dihaluskan menjadi bubuk halus. Ground Granulated Blast Furnace Slag memiliki potensi besar dapat dimanfaatkan sebagai bahan pengganti sebagian semen dalam pembuatan beton.  Penelitian ini bertujuan mencari komposisi campuran beton dengan Ground Granulated Blast Furnace Slag sebagai pengganti sebagian semen yang menghasilkan kuat tekan optimum. Pengujian yang dilakukan berupa uji kuat tekan beton dan sifat beton segar. Komposisi pengganti semen dengan Ground Granulated Blast Furnace Slag sebanyak 0%, 20%, 40%, dan 60%. Dari hasil penelitian uji kuat tekan diperoleh kadar Ground Granulated Blast Furnace Slag optimum pada penggunaan 40% dengan kuat tekan sebesar 50.39 Mpa pada umur 28 hari. Nilai waktu ikat semen yang tercepat terjadi pada penggunaan 0% Ground Granulated Blast Furnace Slag yaitu 246 menit. Dapat disimpulan bahwa Ground Granulated Blast Furnace Slag yang berasal dari PT. Krakatau Semen Indonesia baik digunakan sebagai bahan pengganti sebagian semen pada beton. Kata kunci : kuat tekan, ground granulated blast furnace slag, GGBFS Abstract : An effective way to reduce the environmental impact of making cement is to make a partial substitution of the mineral mixture in it. The material that can now be utilized is combustion solid waste, where the combustion process can have an impact on environmental pollution. PT. Krakatau Semen Indonesia is one of the producers of Ground Granulated Blast Furnace Slag, which is refined steel waste into fine powder. The Ground Granulated Blast Furnace Slag has great potential to be used as a partial substitute for cement in the manufacture of concrete. This study aims to find the composition of concrete mixtures with Ground Granulated Blast Furnace Slag as a partial substitute for cement which produces optimum compressive strength. Tests carried out in the form of concrete compressive strength test and the nature of fresh concrete. The composition of cement substitutes with Ground Granulated Blast Furnace Slag is 0%, 20%, 40%, and 60%. From the results of the compressive strength research, the optimum levels of Slag Ground Granulated Blast Furnace were obtained at 40% use with compressive strength of 50.39 Mpa at 28 days. The fastest cement bonding time occurs at the use of 0% Ground Granulated Blast Furnace Slag which is 246 minutes. Can be concluded that the Ground Granulated Blast Furnace Slag originating from PT. Krakatau Semen Indonesia is well used as a partial replacement for cement in concrete. Keywords : compressive strength, ground granulated blast furnace slag, GGBF

Prediction of Mortar Compressive Strengths for Different Cement Grades in the Vicinity of Sodium Chloride Using ANN

AbstractThe compressive strength values of cement mortar usually affect by sodium chloride quantities, chemical admixtures and cement grades so that an artificial neural network model was performed to predict the compressive strength of mortar value for different cement grades and sodium chloride (NaCl) percent. A three layer feed forward artificial neural network (ANN) model having four input neurons such as cement grades, various water to cement ratio, sodium chloride solution content, one output neuron and five hidden neurons was developed to predict of mortar each compressive strength.To this aim, twelve different mixes under three sodium chloride solution of 0%, 5% and 10% submerged after 60 days has been adopted to measure compressive strength.Artificial neural network (ANN) analysis indicated that by using ANN as non-linear statistical data modeling tool, a strong correlation between the sodium chloride percent of cement mortar and compressive strength can be established. Moreover modeling tools has great influence on the different cement grade such as 42.5 and 32.5 MPa

A compressive concrete strength prediction model using artificial neural networks

A building is at a high risk of destruction if the compressive concrete strength does not meet the required specification. Thus, the prediction of compressive concrete strength has become an important research area. Previous prediction models are based on fix numbers of attributes. Consequently, when the number of attributes increase or decrease, the models could not be used. Thus, a compressive concrete strength prediction model which can work with different numbers of attribute is needed. The purpose of this study is to develop compressive concrete strength prediction models using different combinations of attributes. This study includes five stages: data collection, normalization, parameters identification, model construction and evaluation. The employed data set consists of nine attributes: water, cement, fine aggregate, coarse aggregate, age, fly ash, super plasticizer, blast furnace slag and compressive concrete strength. This study produced eight prediction models where each model has different combination of attributes. It also identified appropriate weights, learning rate, momentum and number of hidden nodes for each of the proposed model, and design a general artificial neural network (ANN) architecture. Model eight of the study produced a higher correlation coefficient (i.e., 0.973) than the existing study (i.e., 0.953). This study has successfully produced eight concrete strength prediction models with good coefficient correlation. The compressive strength prediction models would benefit civil engineers as they can use the models to identify the suitability of additional materials in concrete mix

MAT-701: PREDICTING THE COMPRESSIVE STRENGTH OF ULTRA-LIGHTWEIGHT CONCRETE BY AN ARTIFICIAL NEURAL NETWORK

Ultra-lightweight concrete (ULWC) has potential applications for floating structures and architectural elements because of its dry density coming in at under 1000 kg/m3. The objective was to develop an artificial neural network (ANN) to aid the ULWC designer according to his needs. Boundary conditions were set for each material and 13 constraints based on the water binder ratio, density, air content, binder and aggregate content. The ANN predicted the compressive strength with a comfortable margin of error, with the gap encountered being attributed to variability in workability. Precise constraints and boundary conditions are needed to ensure a lower variability in workability. The ANN, coupled with a genetic algorithm, can generate millions of mixes for a given compressive strength in a short amount of time. The designer is able to choose mixes according to additional needs, such as the carbon footprint, absolute density, polymer content, cost, etc

Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks

[[abstract]]This paper is aimed at demonstrating the possibilities of adapting Quantile Regression Neural Network (QRNN) to estimate the distribution of compressive strength of high performance concrete (HPC). The database containing 1030 compressive strength data were used to evaluate QRNN. Each data includes the amounts of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate (in kilograms per cubic meter), the age, and the compressive strength. This study led to the following conclusions: (1) The Quantile Regression Neural Networks can build accurate quantile models and estimate the distribution of compressive strength of HPC. (2) The various distributions of prediction of compressive strength of HPC show that the variance of the error is inconstant across observations, which imply that the prediction is heteroscedastic. (3) The logarithmic normal distribution may be more appropriate than normal distribution to fit the distribution of compressive strength of HPC. Since engineers should not assume that the variance of the error of prediction of compressive strength is constant, the ability of estimating the distribution of compressive strength of HPC is an important advantage of QRNN.[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]CH

Behaviour of ground cupola furnace slag blended concrete at elevated temperature

Fires adversely affect the performance of concrete when expose to extreme temperatures. However, it is important to study the effects of elevated temperature on the concrete properties. Concrete often contains other cementitious materials such as ground granulated blast furnace slag (GGBFS) and this has been successfully used to improve its properties. Hence, little or no study has been carried out on the use of ground cupola furnace slag (GCFS) in concreting. Therefore, this paper investigates the behavior of concrete blended with GCFS at elevated temperatures. A total of 300 samples were prepared with four different GCFS contents. The test specimens were cured for 28-d and 56-d and subjected to elevated temperatures ranging from 200oC to 800oC up to 24 h. The slump, residual compressive and tensile strength tests were carried out on fresh and hardened concrete. The results showed that the compressive strength and splitting tensile strengths of concrete generally increased with increasing % GCFS content but decreased as temperature increases. At 28-d and 56-d, the strengths were observed to be maximum at 10% replacement when the temperature is 200oC compared to other mixes. It can be concluded that the strength drastically decreased at temperature above 200oC. An analysis of variance (ANOVA) was also carried out to determine the effect of the elevated temperature and percentage replacement of cement with GCFS on the 28-d and 56-d compressive strength of concrete. The results showed that temperature and % GCFS content had a statistically significant effect on the concrete performance. Based on Tukey’s honestly significant difference (HSD), the effect of GCFS was found to be statistically non-significant for 4% and 6% GCFS content at 28-d; and 2% and 4% GCFS content at 56-d. The effect of temperature was also found to be statistically non-significant for 600oC and 800oC at 28-d; and 27oC and 600oC; 200oC and 400oC at 56-d

Predicción de la resistencia a la compresión de concreto de 210 KG/CM² y 175 KG/CM² con redes neuronales artificiales, Cajamarca 2022

La investigación "Predicción de la resistencia a la compresión de concreto de 210 y 175 kg/cm2 con redes neuronales artificiales" tuvo como objetivo determinar la precisión de la predicción de una red neuronal artificial (RNA) para determinar la resistencia a la compresión de concreto por medio del análisis estadístico de los datos de resistencia predichos en comparación con los obtenidos en laboratorio. Para ello, se creó una base de datos históricos de diseños de mezcla y se programó una RNA en Python capaz de predecir la resistencia a la compresión de ambos tipos de concreto. Luego, se elaboraron probetas de concreto de 15 x 30 cm para cada tipo de resistencia, siendo 54 en total y se determinó su resistencia a la compresión a los 7, 14 y 28 días mediante ensayos de laboratorio. Los resultados mostraron que la RNA tiene un error máximo promedio de 4.3 % en los 28 días para concreto de 175 Kg/cm2 y de 4.7 % para 7 días para un concreto con diseño 210 kg/cm2

The determination of ground granulated concrete compressive strength based machine learning models

The advancement of machine learning (ML) models has received remarkable attention by several science and engineering applications. Within the material engineering, ML models are usually utilized for building an expert system for supporting material design and attaining an optimal formulation material sustainability and maintenance. The current study is conducted on the based of the utilization of ML models for modeling compressive strength (Cs) of ground granulated blast furnace slag concrete (GGBFSC). Random Forest (RF) model is developed for this purpose. The predictive model is constructed based on multiple correlated properties for the concrete material including coarse aggregate (CA), curing temperature (T), GGBFSC to total binder ratio (GGBFSC/B), water to binder ratio (w/b), water content (W), fine aggregate (FA), superplasticizer (SP). A total of 268 experimental dataset are gather form the open-source previous published researches, are used to build the predictive model. For the verification purpose, a predominant ML model called support vector machine (SVM) is developed. The efficiency of the proposed predictive and the benchmark models is evaluated using statistical formulations and graphical presentation. Based on the attained prediction accuracy, RF model demonstrated an excellent performance for predicting the Cs using limited input parameters. Overall, the proposed methodology showed an exceptional predictive model that can be utilized for modeling compressive strength of GGBFSC

Utilizing Artificial Neural Network and Multiple Linear Regression to Model the Compressive Strength of Recycled Geopolymer Concrete

Based on the heterogeneity nature of concrete constituents and variation in its compressive strength over several orders of magnitude for various types of concrete, predictive methods to evaluate its compressive strength has now been given an appropriate consideration. Therefore, this study investigates, the performance of Artificial Neural Network, ANN in forecasting the compressive strength of hybrid alkali-activated recycled concrete (HAARC) and compared with the traditional Multiple Linear Regression, MLR. The developed models utilized the experimental results where varying material quantities were used. The ANN and MLR models were developed using eight input variables namely; Ordinary Portland cement (OPC), Rice Husk Ash (RHA), Coal Fly Ash (CFA), Crushed granite (CG), Cupola Furnace Slag (CFS), Alkaline Solution (AS), Water-Binder Ratio (WB) and the Concrete Age (CA) while compressive strength was the only response (predicted) variable. The input data were trained, tested and validated using feedforward back-proportion and backward elimination approach for ANN and MLR, respectively. The most probable model architecture containing eight-input layer, thirteen-hidden layer, and one-output layer neurons was selected based on satisfactory performance in terms of means square error MSE, after several trials. MLR results revealed that only three input variables; CFA, CG and CA proves to be statistically significant with p-values below 0.05. Performance evaluations of the developed models using coefficient of determination, R2, Mean Square Error, MSE, Root Mean-Square Error, RMSE and Mean Absolute Percentage Error, MAPE, showed that ANN prediction accuracy is better than that of MLR with R2 = 0.9972, MSE = 0.4177, RMSE = 1.8201, MAPE = 2.2935 for ANN and R2 = 0.7410, MSE = 66.6308, RMSE = 290.4370, MAPE = 385.5221 for MLR

Behaviour of ground cupola furnace slag blended concrete at elevated temperature

Abstract: Fires adversely affect the performance of concrete when expose to extreme temperatures. However, it is important to study the effects of elevated temperature on the concrete properties. Concrete often contains other cementitious materials such as ground granulated blast furnace slag (GGBFS) and this has been successfully used to improve its properties. Hence, little or no study has been carried out on the use of ground cupola furnace slag (GCFS) in concreting. Therefore, this paper investigates the behavior of concrete blended with GCFS at elevated temperatures. A total of 300 samples were prepared with four different GCFS contents. The test specimens were cured for 28-d and 56-d and subjected to elevated temperatures ranging from 200oC to 800oC up to 24 h. The slump, residual compressive and tensile strength tests were carried out on fresh and hardened concrete. The results showed that the compressive strength and splitting tensile strengths of concrete generally increased with increasing % GCFS content but decreased as temperature increases. At 28-d and 56-d, the strengths were observed to be maximum at 10% replacement when the temperature is 200oC compared to other mixes. It can be concluded that the strength drastically decreased at temperature above 200oC. An analysis of variance (ANOVA) was also carried out to determine the effect of the elevated temperature and percentage replacement of cement with GCFS on the 28-d and 56-d compressive strength of concrete. The results showed that temperature and % GCFS content had a statistically significant effect on the concrete performance. Based on Tukey’s honestly significant difference (HSD), the effect of GCFS was found to be statistically non-significant for 4% and 6% GCFS content at 28-d; and 2% and 4% GCFS content at 56-d. The effect of temperature was also found to be statistically non-significant for 600oC and 800oC at 28-d; and 27oC and 600oC; 200oC and 400oC at 56-d