scholarly journals Predicting the Compressive Strength of Concrete Using an RBF-ANN Model

2021 ◽  
Vol 11 (14) ◽  
pp. 6382
Author(s):  
Nan-Jing Wu
Keyword(s):  
Compressive Strength ◽  
Global Minimum ◽  
Shape Parameters ◽  
Ann Model ◽  
Mix Proportioning ◽  
Concrete Mix ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural Network Ann ◽  
The One ◽  
Gradient Approach

In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in this study is the expansion by adding data from other works to the one used in the author’s previous work. The stochastic gradient approach presented in the textbook is employed for determining the centers of RBFs and their shape parameters. With an extremely large number of training iterations and just a few RBFs in the ANN, all the RBF-ANNs have converged to the solutions of global minimum error. So, the only consideration of whether the ANN can work in practical uses is just the issue of over-fitting. The ANN with only three RBFs is finally chosen. The results of verification imply that the present RBF-ANN model outperforms the BP-ANN model in the author’s previous work. The centers of the RBFs, their shape parameters, their weights, and the threshold are all listed in this article. With these numbers and using the formulae expressed in this article, anyone can predict the 28-day compressive strength of concrete according to the concrete mix proportioning on his/her own.

scholarly journals An ANN Model for Predicting the Compressive Strength of Concrete

2021 ◽  
Vol 11 (9) ◽  
pp. 3798
Author(s):  
Chia-Ju Lin ◽  
Nan-Jing Wu
Keyword(s):  
Compressive Strength ◽  
Back Propagation ◽  
Ann Model ◽  
Bp Network ◽  
Mix Proportioning ◽  
Proper Number ◽  
Concrete Mix ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

An artificial neural network (ANN) model for predicting the compressive strength of concrete is established in this study. The Back Propagation (BP) network with one hidden layer is chosen as the structure of the ANN. The database of real concrete mix proportioning listed in earlier research by another author is used for training and testing the ANN. The proper number of neurons in the hidden layer is determined by checking the features of over-fitting while the synaptic weights and the thresholds are finalized by checking the features of over-training. After that, we use experimental data from other papers to verify and validate our ANN model. The final result of the synaptic weights and the thresholds in the ANN are all listed. Therefore, with them, and using the formulae expressed in this article, anyone can predict the compressive strength of concrete according to the mix proportioning on his/her own.

scholarly journals Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Mehdi Nikoo ◽  
Farshid Torabian Moghadam ◽  
Łukasz Sadowski
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Ann Model ◽  
Ann Models ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Different Characteristics ◽  
Mlr Model

Compressive strength of concrete has been predicted using evolutionary artificial neural networks (EANNs) as a combination of artificial neural network (ANN) and evolutionary search procedures, such as genetic algorithms (GA). In this paper for purpose of constructing models samples of cylindrical concrete parts with different characteristics have been used with 173 experimental data patterns. Water-cement ratio, maximum sand size, amount of gravel, cement, 3/4 sand, 3/8 sand, and coefficient of soft sand parameters were considered as inputs; and using the ANN models, the compressive strength of concrete is calculated. Moreover, using GA, the number of layers and nodes and weights are optimized in ANN models. In order to evaluate the accuracy of the model, the optimized ANN model is compared with the multiple linear regression (MLR) model. The results of simulation verify that the recommended ANN model enjoys more flexibility, capability, and accuracy in predicting the compressive strength of concrete.

scholarly journals Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 485
Author(s):  
Amirreza Kandiri ◽  
Farid Sartipi ◽  
Mahdi Kioumarsi
Keyword(s):  
Compressive Strength ◽  
Optimization Algorithms ◽  
Recycled Aggregate ◽  
Recycled Aggregates ◽  
Tree Model ◽  
Grasshopper Optimization Algorithm ◽  
Compressive Strength Of Concrete ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann ◽  
Industry Needs

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.

scholarly journals The effect of zeolite addition on parameters of concrete containing recycled ceramic aggregate

2018 ◽  
Vol 45 ◽  
pp. 00116
Author(s):  
Jacek Szulej ◽  
Paweł Ogrodnik
Keyword(s):  
Compressive Strength ◽  
Material Properties ◽  
Frost Resistance ◽  
Environmentally Friendly ◽  
Concrete Material ◽  
Dominant Component ◽  
Concrete Mix ◽  
Aluminous Cement ◽  
Compressive Strength Of Concrete ◽  
Zeolite Tuff

In the paper it was decided to recognize the material characteristics of concrete based on ceramic aggregate, aluminous cement with the addition of zeolite (5%, 10%, 15%) and air entraining admixture. Aggregate crushed to 2 fractions was used for designing the concrete mix : 0-4 mm, and 4-8 mm. The research involved the use of clinoptilolite derived from the zeolite tuff deposit at Sokyrnytsya (Transcarpathia, Ukraine). The dominant component in the zeolite is clinoptilolite in an amount of about 75%. The research carried out by the authors showed that the addition of zeolite, among others, increases the compressive strength of concrete, significantly improves the frost resistance, which in the case of using only aluminous cement is very low. The obtained results confirm the possibility of using the above-mentioned components, which improve the concrete material properties and are environmentally friendly.

scholarly journals The Effect of Adding Slag, Achieved from Wastewater Sludge Incineration in Fluidized-Bed Furnace, on the Quality of Concrete

2019 ◽  
Vol 1 (1) ◽  
pp. 244-250
Author(s):  
Alina Pietrzak
Keyword(s):  
Compressive Strength ◽  
Frost Resistance ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Wastewater Sludge ◽  
Hardened Concrete ◽  
Sludge Incineration ◽  
Concrete Mix ◽  
Compressive Strength Of Concrete

Abstract Due to a constant increase in generating the amount of sewage waste it is necessary to find an alternative method of its use or disposal. One of such methods can be utilization of sewage sludge in construction materials industry, particularly in concrete technology and other materials based on cement. It allows using waste materials as a passive additive (filler) or also as an active additive (replacement of part of bonding material). The article aims at presenting the analysis of the effect of adding slag, achieved from wastewater sludge incineration in sewage treatment plant, on properties and quality of concrete mix and hardened concrete. Using an experimental method, the researcher designed the composition of the control concrete mix, which was then modified by means of slag. For all concrete mixtures determined – air content with the use of pressure method and consistency measured by the use of concrete slump test. For all concrete series the following tests were conducted: compressive strength of concrete after 7, 28 and 56 days of maturing, frost resistance for 100 cycles of freezing and thawing, water absorption. The use of slag, ground once in the disintegrator, causes a decrease of in compressive strength of concrete samples in relation to the control concrete series as well as bigger decrease in compressive strength after frost resistance test.

scholarly journals PENGARUH VARIASI DIAMETER MAKSIMUM AGREGAT DALAM CAMPURAN TERHADAP KEKUATAN TEKAN BETON

2019 ◽  
Vol 3 (1) ◽  
pp. 11-23
Author(s):  
Helwiyah Zain
Keyword(s):  
Compressive Strength ◽  
Maximum Diameter ◽  
Concrete Aggregate ◽  
Test Results ◽  
Natural Mineral ◽  
Size Selection ◽  
Concrete Mix ◽  
Compressive Strength Of Concrete ◽  
Average Compressive Strength

Aggregate is a natural mineral grains that serve as filler in concrete mix, and the greatest material contained in the concrete. These material influence on the properties of concrete, so that the diameter size selection is essential in making the concrete. This study aims to determine the effect of variations of aggregate maximum diameter to the compressive strength of concrete. In this study used 15 specimens, were divided into 3 groups witch each of 5 specimens. Each group is distinguished aggregate maximum diameter: 31.5 mm, 16 mm, and 8 mm. Specimens used in this study is the specimen cylinder with a diameter of 15 cm and 30 cm high. Speciment tested done at age of concrete 28 days. The average compressive strength of concrete for each group of test based on variations of  the aggregate maximum diameter is: for the aggregate maximum diameter of 31.5 mm = 249.67 kg / cm2; the aggregate maximum diameter 16 mm = 274.91 kg / cm2; and the aggregate maximum diameter of 8 mm = 326.74 kg / cm2. Based on these test results, show that the average compressive strength of the concrete for the aggregate maximum diameter of 16 mm is 10.11% stronger than the concrete with the aggregate maximum diameter of 31.5 mm; and the strength of concrete aggregate maximum diameter of 8 mm, 30.87% stronger than the concrete with aggregate maximum diameter of 31.5 mm.

scholarly journals Effects of Disparate Zones of Sand on the Compressive Strength of Concrete

2021 ◽  
Vol 9 (11) ◽  
pp. 1368-1375
Author(s):  
Suhaib Bakshi
Keyword(s):  
Compressive Strength ◽  
Coarse Aggregate ◽  
Testing Machine ◽  
Concrete Compressive Strength ◽  
Research Papers ◽  
Concrete Material ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Concrete Mix ◽  
Compressive Strength Of Concrete

Abstract: Compressive strength of concrete is the capacity of concrete to bear loads of materials or structure sans breaking or being deformed. Specimen under compression shrinks in size whilst under tension the size elongates. Compressive strength essentially gives concept about the properties of concrete. Compressive strength relies on many aspects such as water-cement ratio, strength of cement, calidad of concrete material. Specimens are tested by compression testing machine after the span of 7 or 28 days of curing. Compressive strength of the concrete is designated by the load on the area of specimen. In this research various proportions of such aggregate mixed in preparing M 30 grade and M 40 grade of Concrete mix and the effect is studied on its compressive strength . Several research papers have been assessed to analyze the compressive strength of concrete and the effect of different zones of sand on compressive strength are discussed in this paper. Keywords: Sand, Gradation, Coarse aggregate, Compressive strength

scholarly journals RECYCLE GLASS WASTE AS A REPLACEMENT OF FINE AGGREGATE IN CONCRETE MIX STANDARD COMPARISON

2021 ◽  
Vol 5 (2) ◽  
pp. 74-84
Author(s):  
Syf. Umi Kalsum ◽  
Betti Ses Eka Polonia ◽  
Hurul 'Ain
Keyword(s):  
Compressive Strength ◽  
Energy Use ◽  
Raw Materials ◽  
Fine Aggregate ◽  
Glass Waste ◽  
Concrete Aggregates ◽  
Concrete Mix ◽  
Waste Solid ◽  
Mixed Material ◽  
Compressive Strength Of Concrete

Recycling is one way that is used to minimize the amount of waste that exists. Recycling is also a process to reduce the use of new raw materials, reduce energy use, reduce pollution, land degradation and greenhouse gas emissions. Materials that can be recycled consist of waste of glass, plastic, paper, metal, textiles and electronic goods. Glass has characteristics suitable as concrete aggregates, considering that glass is a material that does not absorb water. In addition, glass has high abrasion resistance. Meanwhile, the waste glass flux lowers the temperature to the temperature at which the formers will melt. Stabilizers in glass waste are made of calcium carbonate, which makes the glass waste solid and water-resistant. This glass waste is recycled by mixing it into the concrete mix. The recycling method is done by pounding the glass and putting it into the concrete mix stage. The purpose of mixing the glass waste is expected to increase the compressive strength of concrete. The use of glass waste as a mixed material affects the compressive strength of the concrete. The concrete with the most inferior to highest compressive strength is 4% variation concrete, 2% variation concrete, and traditional concrete. Optimal percentage addition of glass waste impacts on maximum concrete compressive strength is 2% mixture variation which obtained 11,88 Mpa & 11,32 Mpa.

Prediction of solid waste generation and finding the sustainable pathways in the city of Dhaka

2020 ◽  
Vol 31 (6) ◽  
pp. 1587-1601
Author(s):  
Md. Sazol Ahmmed ◽  
Md. Faisal Arif ◽  
Md. Mosharraf Hossain
Keyword(s):  
Solid Waste ◽  
Solid Waste Management ◽  
Suitable Model ◽  
Ann Model ◽  
Rapid Urbanization ◽  
Content Type ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
The One ◽  
The City

PurposeSolid waste (SW) is the result of rapid urbanization and industrialization, and is increasing day by day by the increasing number of population. This thesis paper emphasizes on the prediction of SW generation in the city of Dhaka and finding sustainable pathways for minimizing the gaps in the existing system.Design/methodology/approachIn this paper, the survey of different questionnaires of the Dhaka South City Corporation (DSCC) was conducted. The data of SW generation, for few years of each month, in the city of Dhaka were collected to develop a model named Artificial Neural Network (ANN). The ANN model was used for the accurate prediction of SW generation.FindingsAt first, by using the ANN model with the one hidden layer and changing the number of neurons of the layer different models were created and tested. Finally, according to R values (training, test, all) the structure with six neurons in the hidden layer was selected as the suitable model. Finally, six gaps were found in the existing system of solid waste management (SWM) in the DSCC. These gaps are the main barrier for the better SWM.Originality/valueThe authors propose that the best model for prediction is 12-6-3, and its training and testing results are given as 0.9972 and 0.80380, respectively. So the resulting prediction is so much close in comparison with actual data. In this paper, the opportunities of those gaps are provided for working properly and the DSCC will find the better result in the aspect of SW problem.

Classification of Breast Cancer Patients Using Neural Network Technique

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Putri Marhida Badarudin ◽  
◽  
Rozaida Ghazali ◽  
Abdullah Alahdal ◽  
N.A.M. Alduais ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Classification Model ◽  
Breast Cancer Patients ◽  
Ann Model ◽  
Average Accuracy ◽  
Artificial Neural Network Ann ◽  
The One ◽  
Detect Breast Cancer

This work develops an Artificial Neural Network (ANN) model for performing Breast Cancer (BC) classification tasks. The design of the model considers studying different ANN architectures from the literature and chooses the one with the best performance. This ANN model aims to classify BC cases more systematically and more quickly. It provides facilities in the field of medicine to detect breast cancer among women. The ANN classification model is able to achieve an average accuracy of 98.88 % with an average run time of 0.182 seconds. Using this model, the classification of BC can be carried out much more faster than manual diagnosis and with good enough accuracy.

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