Support Vector Machine (SVM)-Based Optimal Design Procedure of Fly Ash Blended Concrete

2021 ◽  
Vol 894 ◽  
pp. 103-108
Author(s):  
Tae Wan Kim ◽  
Jong Yeon Lim ◽  
Xiao Yong Wang ◽  
Yi Han
Keyword(s):  
Support Vector Machine ◽  
Compressive Strength ◽  
Fly Ash ◽  
Optimal Design ◽  
Design Procedure ◽  
Support Vector ◽  
Replacement Ratio ◽  
Svm Model ◽  
Binder Ratio ◽  
Water To Binder Ratio

A support vector machine (SVM) is widely used for predicting the properties of fly ash blended concrete. However, the studies about the optimal design of fly ash blended concrete based on SVM are very limit. This study shows an SVM-based optimal design procedure of fly ash blended concrete. First, we built an SVM model and evaluated the compressive strength of fly ash blended concrete considering the effects of water to binder ratio, fly ash replacement ratio, and test ages. Second, we made parameter studies based on the SVM model. The parameter studies show that fly ash can improve the late age strength of concrete. This improvement is obvious for concrete with lower water to binder ratio. The optimal fly ash replacement ratio increases as the water to binder ratio decreases.

Analysis of the Effect of Fly Ash on Compressive Strength of Concrete Using Neural Network and Parameter Analysis

2020 ◽  
Vol 995 ◽  
pp. 130-135
Author(s):  
Xiao Yong Wang
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Fly Ash ◽  
Network Model ◽  
Neural Network Model ◽  
Parameter Analysis ◽  
Replacement Ratio ◽  
The Neural Network ◽  
Binder Ratio ◽  
Water To Binder Ratio

Compressive strength is a crucial design index of fly ash blended concrete. This study presents an estimation model to show the effect of fly ash on the strength development of concrete. First, a neural network model is proposed to estimate the compressive strength of fly ash blended concrete. The input variables of the neural network are water-to-binder ratio, fly ash replacement ratio, and curing ages. The output result of the neural network is a strength. The range of water-to-binder ratio is from 0.3 to 0.5, the range of fly ash replacement ratio is from 0 to 0.55, and the range of test age is from 3 days to 180 days. The neural network gives an accurate evaluation of compressive strength. Second, parameter analysis is carried out based on the neural network model. The results of parameter analysis show that the effect of fly ash on strength is dependent on water-to-binder ratio. The using of high-volume fly ash with low water-to-binder ratio concrete is a rational option.

Influence of Fly Ash on Abrasion Resistance of Concretes with their Additions

2013 ◽  
Vol 592-593 ◽  
pp. 651-654
Author(s):  
Aneta Nowak-Michta
Keyword(s):  
Compressive Strength ◽  
Fly Ash ◽  
Abrasion Resistance ◽  
Test Results ◽  
Reference Test ◽  
Binder Ratio ◽  
Alternative Test ◽  
Water To Binder Ratio ◽  
Standard Tests ◽  
Class F

The influence of fly ash quality and quantity on abrasion resistance of hardened concretes with siliceous fly ash addition is analysed in the paper. Abrasion resistance was measured in two standard tests according to EN 1338: 2005: reference test of the Wide Wheel and alternative test of the Bohme. Cement was replaced with 20, 35, and 50% of Class F siliceous fly ash in three categories of losses of ignition A, B and C by mass. The water to binder ratio, the air-entraining and the workability of mixtures were maintained constant at 0.38, 4.5% and 150 mm respectively. Test results indicated that in both methods, all tested concretes according to EN 1338: 2005 could be classified to 4-the highest class of abrasion resistance. In reference test of the Wide Wheel fly ash quality and quantity not influences abrasion resistance. However, in alternative, Böhme test abrasion resistance lowering with growth quantity of fly ash in binder, while loss of ignition of fly ash no influenced abrasion resistance. There were no correlation between the abrasion resistance and the compressive strength.

scholarly journals Reuse of Sintered Sludge from Municipal Sewage Treatment Plants for the Production of Lightweight Aggregate Building Mortar

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 999
Author(s):  
Changyong Li ◽  
Xiaoyan Zhang ◽  
Bingxin Zhang ◽  
Yunfei Tan ◽  
Fenglan Li
Keyword(s):  
Fly Ash ◽  
Sewage Treatment ◽  
Lightweight Aggregate ◽  
Limestone Powder ◽  
Municipal Sewage ◽  
Dry Density ◽  
Replacement Ratio ◽  
Sewage Treatment Plants ◽  
Binder Ratio ◽  
Water To Binder Ratio

In recent years, the sludge produced by municipal sewage treatment plants has become an important recyclable resource for producing green building materials. After the systematic processing of incineration and particle formation, the sintered sludge can be processed into fine lightweight aggregate to produce building mortar with the controlled leaching of heavy metals and radioactivity. In this paper, to increase its economic and environmental benefits, mortar with sintered sludge aggregate was made by cement admixing of fly ash or limestone powder. The water-to-binder ratio was set at three levels—0.82, 0.68, and 0.62—and either flay ash or limestone powder was used to replace equal masses of cement at 10%, 20%, or 30%. Eighteen groups of mortar were studied to evaluate their workability, air content, compressive strength, tensile adhesive strength, dry density, and thermal conductivity. The results indicate that with a proper water-to-binder ratio, and the replacement ratio of fly ash or limestone powder, the mortar can be produced with good workability, consistency, water-retention rate, layering degree, and setting time. The mortar made with sintered sludge lightweight aggregate, designated by the mix-proportion method for conventional lightweight aggregate mortar, did not meet the target strength, although the compressive strength of mortar was no less than 3.0 MPa, which meets the strength grade M2.5. The tensile adhesive strength reached 0.18 MPa. The mortar was super lightweight with a dry density less than 400 kg/m3, and a thermal conductivity within 0.30~0.32 W/(m⋅K). The effects of water-to-binder ratio and replacement ratio of fly ash or limestone powder on the above properties are discussed with test results. The study provides a basis for using sintered sludge lightweight aggregate for building mortar.

scholarly journals Effect of Fly Ash on Some Properties of Reactive Powder Concrete

2021 ◽  
Vol 27 (11) ◽  
pp. 32-46
Author(s):  
Zahraa F Muhsin ◽  
Nada Mahdi Fawzi
Keyword(s):  
Mechanical Properties ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Fly Ash ◽  
Industrial Wastes ◽  
Experimental Program ◽  
Reactive Powder Concrete ◽  
Binder Ratio ◽  
Reactive Powder ◽  
Water To Binder Ratio

To achieve sustainability in the field of civil engineering, there has become a great interest in developing reactive powder concrete RPC through the use of environmentally friendly materials to reduce the release of CO2 gas produced from cement factories as well as contribute to the recycling of industrial wastes that have a great impact on environmental pollution. In this study, reactive powder concrete was prepared using total binder content of 800 kg/m3, water to binder ratio (0.275), and micro steel fibers  1% by volume of concrete. The experimental program included replacing fly ash with (8, 12, 16) % by cement weight to find the optimal ratio, which achieved the best mechanical properties of (RPC) at 7, 28, and 90 days with standard curing. Some mechanical properties of reactive powder concrete (flow, compressive strength, tensile strength, and density) were verified. The results at 28 days showed that the compressive strength (96.5) Mpa, tensile strength (9.38) Mpa, and density (2395 kg/m3). The results showed that the percentage of replacement of 8% of fly ash with cement is the optimal percentage, which achieved the highest resistance compared to the others. The results also indicated that it is possible to develop RPC using fly ash with a high withstand stress, tensile strength, and density.

scholarly journals Prediction of Splitting Tensile Strength from Cylinder Compressive Strength of Concrete by Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Kezhen Yan ◽  
Hongbing Xu ◽  
Guanghui Shen ◽  
Pei Liu
Keyword(s):  
Experimental Data ◽  
Support Vector Machine ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Splitting Tensile Strength ◽  
Support Vector ◽  
Svm Model ◽  
Concrete Mechanical Properties ◽  
Compressive Strength Of Concrete ◽  
Good Agreement

Compressive strength and splitting tensile strength are both important parameters that are utilized for characterization concrete mechanical properties. This paper aims to show a possible applicability of support vector machine (SVM) to predict the splitting tensile strength of concrete from compressive strength of concrete, a SVM model was built, trained, and tested using the available experimental data gathered from the literature. All of the results predicted by the SVM model are compared with results obtained from experimental data, and we found that the predicted splitting tensile strength of concrete is in good agreement with the experimental data. The splitting tensile strength results predicted by SVM are also compared to those obtained by using empirical results of the building codes and various models. These comparisons show that SVM has strong potential as a feasible tool for predicting splitting tensile strength from compressive strength.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

scholarly journals Three-Dimensional Site Characterization Model of Bangalore Using Support Vector Machine

2012 ◽  
Vol 2012 ◽  
pp. 1-10
Author(s):  
Pijush Samui
Keyword(s):  
Support Vector Machine ◽  
Three Dimensional ◽  
Site Characterization ◽  
Standard Penetration Test ◽  
Support Vector ◽  
Characterization Model ◽  
Svm Model ◽  
Input Variables ◽  
Learning Machine ◽  
A Site

The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values () spread over 220 sq km area of Bangalore. The model is applied for corrected () values. The three input variables (, , and , where , , and are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, , and ε) has been also presented.

scholarly journals Simulating the Stress-Strain Relationship of Geomaterials by Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hongbo Zhao ◽  
Zenghui Huang ◽  
Zhengsheng Zou
Keyword(s):  
Support Vector Machine ◽  
Constitutive Relationship ◽  
Support Vector ◽  
Nonlinear Relationship ◽  
Ann Model ◽  
Stress Strain ◽  
Svm Model ◽  
Strain Relationship ◽  
Artificial Neural Network Ann ◽  
Relationship Of

Stress-strain relationship of geomaterials is important to numerical analysis in geotechnical engineering. It is difficult to be represented by conventional constitutive model accurately. Artificial neural network (ANN) has been proposed as a more effective approach to represent this complex and nonlinear relationship, but ANN itself still has some limitations that restrict the applicability of the method. In this paper, an alternative method, support vector machine (SVM), is proposed to simulate this type of complex constitutive relationship. The SVM model can overcome the limitations of ANN model while still processing the advantages over the traditional model. The application examples show that it is an effective and accurate modeling approach for stress-strain relationship representation for geomaterials.

Effect of Fly Ash and Nano-CaCO3 on the Viscosity of Cement Paste

2013 ◽  
Vol 357-360 ◽  
pp. 968-971 ◽  
Author(s):  
Ren Juan Sun ◽  
Zhi Qin Zhao ◽  
Da Wei Huang ◽  
Gong Feng Xin ◽  
Shan Shan Wei ◽  
...  
Keyword(s):  
Fly Ash ◽  
Cement Paste ◽  
Bingham Model ◽  
Reduced Viscosity ◽  
Binder Ratio ◽  
Water To Binder Ratio

The effect of fly ash and nanoCaCO3 on the viscosity of pastes was studied. The rheological value of cement paste was determined by the rotation rheometer NXS-11B. In the study, five different dosages (0%, 20%, 30%, 40%, and 50%) of fly ash and three levels of nanoCaCO3, 0.5%, 1%, and 2.5%, were considered. Viscosity of the pastes, made with fly ash and nanoCaCO3 at a constant water-to-binder ratio of 0.35, were measured and analyzed. The results indicate that the pastes with fly ash or/and nanoCaCO3 still fit the Bingham model. The addition of fly ash reduced viscosity, however, the addition of nanoCaCO3 increased viscosity. The effect of nanoCaCO3 is more significantly than fly ash on viscosity.

Sign in / Sign up

Export Citation Format

Share Document