scholarly journals Prediction of Compressive Strengths with Partial Fine Aggregate of Plastic Using Artificial Neural Network and Revisions

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Cornelius Ngunjiri Ngandu
Keyword(s):  
Compressive Strength ◽  
Prediction Models ◽  
Plastic Waste ◽  
Partial Replacement ◽  
Percentage Error ◽  
Fine Aggregate ◽  
Ann Model ◽  
Waste Plastic ◽  
Green Concrete ◽  
Artificial Neural

In recent past years, plastic waste has been a environmental menace. Utilization of plastic waste as fine aggregate substitution could reduce the demand and negative impacts of sand mining while addressing waste plastic challenges.This study aims at evaluating compressive strengths prediction models for concrete with plastic—mainly recycled plastic—as partial replacement or addition of fine aggregates, by use of artificial neural networks (ANNs), developed in OCTAVE 5.2.0 and datasets from reviews. 44 datasets from 8 different sources were used, that included four input variables namely:- water: binder ratio; control compressive strength (MPa); % plastic replacement or additive by weight and plastic type; and the output variable was the compressive strength of concrete with partial plastic aggregates.Various models were run and the selected model, with 14 nodes in hidden layer and 320,000 iterations, indicated overall root mean square error (RMSE) , absolute factor of variance (R2), mean absolute error (MAE) and mean absolute percentage error (MAPE) values of 1.786 MPa, 0.997, 1.329 MPa and 4.44 %. Both experimental and predicted values showed a generally increasing % reduction of compressive strengths with increasing % plastic fine aggregate.The model showed reasonably low errors, reasonable accuracy and good generalization. ANN model could be used extensively in modeling of green concrete, with partial waste plastic fine aggregate. The study recommend ANNs models application as possible alternative for green concrete trial mix design. Sustainable techniques such as low-cost superplasticizers from recycled material and cost-effective technologies to adequately sizing and shaping plastic for fine aggregate application should be encouraged, so as to enhance strength of concrete with partial plastic aggregates.

scholarly journals Feasibility Study on Use of Plastic Waste as Fine Aggregate in Concrete Mixes

2020 ◽  
Vol 1 (1) ◽  
pp. 26
Author(s):  
Sudarshan Dattatraya Kore
Keyword(s):  
Mechanical Properties ◽  
Waste Management ◽  
Solid Waste ◽  
Solid Waste Management ◽  
Plastic Waste ◽  
Partial Replacement ◽  
Fine Aggregate ◽  
Environment Friendly ◽  
Waste Plastic ◽  
Recycled Plastics

Plastic is used in many forms in day-to-day life. Since Plastic is non-biodegradable, landfills do not provide an environment friendly solution. Hence, there is strong need to utilize waste plastic. This creates a large quantity of garbage every day which is unhealthy and pollutes the environment. In present scenario solid waste management is a challenge in our country. The production of solid waste is increasing day to day and causes serious concerns to the environment. In this study, the recycled plastics are used in the concrete as a partial replacement of fine aggregate in concrete. The main purpose of this study is to investigate the mechanical properties of concrete such as workability, compressive, flexural and split tensile strengths of concrete mixes with partial replacement of conventional fine aggregate with aggregate produced from plastic waste. The use of plastic aggregate as replacement for fine aggregate enhances workability and fresh bulk density of concrete mixes. The mechanical properties of concrete such as compressive, flexural, and tensile strengths of concrete reduced marginally up to 10% replacement levels.

scholarly journals Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Palika Chopra ◽  
Rajendra Kumar Sharma ◽  
Maneek Kumar
Keyword(s):  
Compressive Strength ◽  
Genetic Programming ◽  
Prediction Models ◽  
Model Development ◽  
Strength Prediction ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs) and Genetic Programming (GP). The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM) for the prediction of concrete compressive strength is the best prediction tool.

scholarly journals Feasibility Study of Recycled Plastic Waste as Fine Aggregate in Concrete

2020 ◽  
Vol 184 ◽  
pp. 01084
Author(s):  
K. Sai Gopi ◽  
Dr. T. Srinivas ◽  
S. P. Raju V
Keyword(s):  
Compressive Strength ◽  
Construction Industry ◽  
Environmental Concern ◽  
Plastic Waste ◽  
Partial Replacement ◽  
Fine Aggregate ◽  
River Sand ◽  
Sustainable Concrete ◽  
Waste Polyethylene ◽  
Day By Day

Nowadays, Environmental concern towards plastic waste rises because of its low degradability and creating problems like chunking sewer lines, drainages, waterways, filling landfills, health problems, etc. The best approach is recycling and reuses plastic waste. Increase in the production of plastic day by day but, very little was recycled. On the other hand, huge demand for concrete in the construction industry. Utilization of recycled plastic waste in the production of sustainable concrete by partial replacement of fine aggregate. This study has been investigated the utilization of two types of recycled plastic waste Polyethylene Terephthalate (PET) and Polypropylene (PP) as fine aggregate in concrete. M30 grade of concrete has been used by partial replacement of fine aggregate (River Sand) with recycled plastic waste in the percentage of 5, 10, 15, 20, and 25. The workability and compressive strength results are checked to find the acceptable percentage of incorporation of PET and PP in concrete. From the results, it is observed that the workability is decreased as the percentage of recycled plastic waste is increased. The Optimum Percentage of replacement of PET is 10%. PP has shown a marginal reduction in compressive strength for 5% replacement.

Concrete Compressive Strength Prediction Using Rebound Method with Artificial Neural Network

2012 ◽  
Vol 443-444 ◽  
pp. 34-39
Author(s):  
Jian Ming Liu ◽  
Hui Jian Li ◽  
Chang Jun He
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Back Propagation ◽  
Mean Value ◽  
Percentage Error ◽  
Fine Aggregate ◽  
Coarse Aggregates ◽  
Artificial Neural ◽  
Cementing Material ◽  
Bpnn Model

Concrete is a mixture of the cementing material, aggregate and water in a certain proportion and is the most main materials of the civil engineering materials. It is difficult to make modeling for a highly complex material. The concrete rebound value with wide randomness is a dependent variable, while the compressive strength value with narrow randomness is an independent variable. This paper aimed to show possible applicability of artificial neural networks (ANN) to predict the compressive strength. Back propagation neural networks (BPNN) model is constructed trained and tested using the available test data of 108 different concrete specimens. The data of input parameters used in BPNN model were covered the ratio of water to cement, fine aggregate ratio, coarse aggregates, mean value of test area of rebound method measurement. The mean absolute percentage error was less then 10.19% for compressive strength. The results showed that ANNs was good at as a feasible tool for predicting compressive strength.

scholarly journals Feasibility Study on Use of Plastic Waste as Fine Aggregate in Concrete Mixes

2019 ◽  
Vol 1 (1) ◽  
pp. 26
Author(s):  
Sudarshan Dattatraya Kore
Keyword(s):  
Mechanical Properties ◽  
Waste Management ◽  
Solid Waste ◽  
Solid Waste Management ◽  
Plastic Waste ◽  
Partial Replacement ◽  
Fine Aggregate ◽  
Environment Friendly ◽  
Waste Plastic ◽  
Recycled Plastics

Plastic is used in many forms in day-to-day life. Since Plastic is non-biodegradable, landfills do not provide an environment friendly solution. Hence, there is strong need to utilize waste plastic. This creates a large quantity of garbage every day which is unhealthy and pollutes the environment. In present scenario solid waste management is a challenge in our country. The production of solid waste is increasing day to day and causes serious concerns to the environment. In this study, the recycled plastics are used in the concrete as a partial replacement of fine aggregate in concrete. The main purpose of this study is to investigate the mechanical properties of concrete such as workability, compressive, flexural and split tensile strengths of concrete mixes with partial replacement of conventional fine aggregate with aggregate produced from plastic waste. The use of plastic aggregate as replacement for fine aggregate enhances workability and fresh bulk density of concrete mixes. The mechanical properties of concrete such as compressive, flexural, and tensile strengths of concrete reduced marginally up to 10% replacement levels.Plastic is used in many forms in day-to-day life. Since Plastic is non-biodegradable, landfills do not provide an environment friendly solution. Hence, there is strong need to utilize waste plastic. This creates a large quantity of garbage every day which is unhealthy and pollutes the environment. In present scenario solid waste management is a challenge in our country. The production of solid waste is increasing day to day and causes serious concerns to the environment. In this study, the recycled plastics are used in the concrete as a partial replacement of fine aggregate in concrete. The main purpose of this study is to investigate the mechanical properties of concrete such as workability, compressive, flexural and split tensile strengths of concrete mixes with partial replacement of conventional fine aggregate with aggregate produced from plastic waste. The use of plastic aggregate as replacement for fine aggregate enhances workability and fresh bulk density of concrete mixes. The mechanical properties of concrete such as compressive, flexural, and tensile strengths of concrete reduced marginally up to 10% replacement levels.

scholarly journals Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

Materials ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 7531
Author(s):  
Afnan Nafees ◽  
Muhammad Faisal Javed ◽  
Sherbaz Khan ◽  
Kashif Nazir ◽  
Furqan Farooq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Silica Fume ◽  
Splitting Tensile Strength ◽  
Partial Replacement ◽  
Fine Aggregate ◽  
Learning Models ◽  
Green Concrete ◽  
Machine Learning Models

Silica fume (SF) is a mineral additive that is widely used in the construction industry when producing sustainable concrete. The integration of SF in concrete as a partial replacement for cement has several evident benefits, including reduced CO2 emissions, cost-effective concrete, increased durability, and mechanical qualities. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Multilayer perceptron neural networks (MLPNN), adaptive neural fuzzy detection systems (ANFIS), and genetic programming are all used (GEP). From accessible literature data, a broad and accurate database of 283 compressive strengths and 149 split tensile strengths was created. The six most significant input parameters were cement, fine aggregate, coarse aggregate, water, superplasticizer, and silica fume. Different statistical measures were used to evaluate models, including mean absolute error, root mean square error, root mean squared log error and the coefficient of determination. Both machine learning models, MLPNN and ANFIS, produced acceptable results with high prediction accuracy. Statistical analysis revealed that the ANFIS model outperformed the MLPNN model in terms of compressive and tensile strength prediction. The GEP models outperformed all other models. The predicted values for compressive strength and splitting tensile strength for GEP models were consistent with experimental values, with an R2 value of 0.97 for compressive strength and 0.93 for splitting tensile strength. Furthermore, sensitivity tests revealed that cement and water are the determining parameters in the growth of compressive strength but have the least effect on splitting tensile strength. Cross-validation was used to avoid overfitting and to confirm the output of the generalized modelling technique. GEP develops an empirical expression for each outcome to forecast future databases’ features to promote the usage of green concrete.

scholarly journals Experimental Study on Properties of Concrete Using Shredded Plastic Waste and M-Sand as Partial Replacement of Fine Aggregate

2021 ◽  
pp. 427-432
Author(s):  
Sindhu Vaardini U ◽  
Pon Soundarya M
Keyword(s):  
Mechanical Properties ◽  
Compressive Strength ◽  
Flexural Strength ◽  
Plastic Waste ◽  
Partial Replacement ◽  
Fine Aggregate ◽  
Conventional Concrete ◽  
Manufactured Sand ◽  
And Behavior ◽  
Land Water

Disposal of large quantity of plastic causes land, water, and air pollution etc.., so a study is conducted to recycle the plastic in concrete. This work investigates about the replacement of natural aggregate with non-biodegradable plastic aggregate made up of mixed shredded plastic waste in concrete. Several tests are conducted such as compressive strength of cube, compressive strength of cylinder, flexural strength test of prism to identify the properties and behavior of concrete using shredded plastic aggregate. Replacement of fine aggregate weight by 0%, 5%, 10%, 15%, 20% with shredded plastic fine (PF) aggregate and manufactured sand (M-Sand). Totally 30 cubes, 30 cylinders and 30 prisms are casted to identify the compressive strength, cylindrical compressive strength, and flexural strength respectively. Casted specimens are tested at 7, 14 and 28 days. The identified results from concrete using shredded plastic aggregate are compared with conventional concrete. Result shows that initially there is increase in mechanical properties then there is reduction in mechanical properties due to addition of shredded plastic aggregate added concrete. This reduction in strength is mainly due to poor bond strength between cement and shredded plastic aggregate.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

scholarly journals Use of Nondestructive Testing of Ultrasound and Artificial Neural Networks to Estimate Compressive Strength of Concrete

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 44
Author(s):  
Fernando A. N. Silva ◽  
João M. P. Q. Delgado ◽  
Rosely S. Cavalcanti ◽  
António C. Azevedo ◽  
Ana S. Guimarães ◽  
...  
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Cross Sections ◽  
Influential Factors ◽  
Average Error ◽  
Ann Model ◽  
Cement Ratio ◽  
Water Cement Ratio ◽  
Artificial Neural

The work presents the results of an experimental campaign carried out on concrete elements in order to investigate the potential of using artificial neural networks (ANNs) to estimate the compressive strength based on relevant parameters, such as the water–cement ratio, aggregate–cement ratio, age of testing, and percentage cement/metakaolin ratios (5% and 10%). We prepared 162 cylindrical concrete specimens with dimensions of 10 cm in diameter and 20 cm in height and 27 prismatic specimens with cross sections measuring 25 and 50 cm in length, with 9 different concrete mixture proportions. A longitudinal transducer with a frequency of 54 kHz was used to measure the ultrasonic velocities. An ANN model was developed, different ANN configurations were tested and compared to identify the best ANN model. Using this model, it was possible to assess the contribution of each input variable to the compressive strength of the tested concretes. The results indicate an excellent performance of the ANN model developed to predict compressive strength from the input parameters studied, with an average error less than 5%. Together, the water–cement ratio and the percentage of metakaolin were shown to be the most influential factors for the compressive strength value predicted by the developed ANN model.

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