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 APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO DETERMINE CONCRETE COMPRESSIVE STRENGTH BASED ON NON‐DESTRUCTIVE TESTS

2005 ◽  
Vol 11 (1) ◽  
pp. 23-32 ◽  
Author(s):  
Jerzy Hoła ◽  
Krzysztof Schabowicz
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Concrete Compressive Strength ◽  
Data Set ◽  
Strength Based ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural ◽  
Non Destructive

The paper deals with the neural identification of the compressive strength of concrete on the basis of non‐destructively determined parameters. Basic information on artificial neural networks and the types of artificial neural networks most suitable for the analysis of experimental results are given. A set of experimental data for the training and testing of neural networks is described. The data set covers a concrete compressive strength ranging from 24 to 105 MPa. The methodology of the neural identification of compressive strength is presented. Results of such identification are reported. The results show that artificial neural networks are highly suitable for assessing the compressive strength of concrete. The neural identification of the compressive strength of concrete has been verified in situ.

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 Prediction of Concrete Compressive Strength & Slump using Artificial Neural Networks (ANN)

2021 ◽  
Vol 9 (2) ◽  
pp. 84-89
Author(s):  
Yasir M. H. Badawi ◽  
Yousif Hummaida Ahmed
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Ann Model ◽  
Concrete Compressive Strength ◽  
Concrete Mix Design ◽  
Fine Aggregates ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Strength And Durability

Concrete is the most used building material in the world, due to its high compressive strength and durability. Those properties are measured and assessed in fresh and hardened states of concrete, with standard methods which are time and cost consuming. In the present study, the compressive strength and slump of concrete has been predicted using Artificial Neural Network (ANN), which is constructed using different input parameters involving concrete mix design (i.e. coarse & fine aggregates properties, cement content, water/cement (W/C) ratio, admixtures type and dosage …etc.).The predicted strength was compared with the experimentally obtained actual compressive strength and slump data collected in many years for different materials and mix designs in the Sudan. An ANN model has been developed by using MATLAB neural network toolbox. A good co-relationships with regression values of 0.915 and 0.931 for strength and slump respectively have been obtained between the predicted and experimental values. It is concluded that the ANN method can gain acceptable predictions for compressive strength and slump.  

scholarly journals Applying Artificial Intelligence to Improve On-Site Non-Destructive Concrete Compressive Strength Tests

Crystals ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1157
Author(s):  
Tu Quynh Loan Ngo ◽  
Yu-Ren Wang ◽  
Dai-Lun Chiang
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Prediction Models ◽  
Concrete Strength ◽  
Pulse Velocity ◽  
Statistical Regression ◽  
Concrete Compressive Strength ◽  
Major Drawback ◽  
Non Destructive

In the construction industry, non–destructive testing (NDT) methods are often used in the field to inspect the compressive strength of concrete. NDT methods do not cause damage to the existing structure and are relatively economical. Two popular NDT methods are the rebound hammer (RH) test and the ultrasonic pulse velocity (UPV) test. One major drawback of the RH test and UPV test is that the concrete compressive strength estimations are not very accurate when comparing them to the results obtained from the destructive tests. To improve concrete strength estimation, the researchers applied artificial intelligence prediction models to explore the relationships between the input values (results from the two NDT tests) and the output values (concrete strength). In-situ NDT data from a total of 98 samples were collected in collaboration with a material testing laboratory and the Professional Civil Engineer Association. In-situ NDT data were used to develop and validate the prediction models (both traditional statistical models and AI models). The analysis results showed that AI prediction models provide more accurate estimations when compared to statistical regression models. The research results show significant improvement when AI techniques (ANNs, SVM and ANFIS) are applied to estimate concrete compressive strength in RH and UPV tests.

scholarly journals Adapting Artificial Intelligence to Improve In Situ Concrete Compressive Strength Estimations in Rebound Hammer Tests

2020 ◽  
Vol 7 ◽  
Author(s):  
Yu Ren Wang ◽  
Yen Ling Lu ◽  
Dai Lun Chiang
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Prediction Models ◽  
Concrete Compressive Strength ◽  
Concrete Material ◽  
Rebound Hammer ◽  
Existing Structures ◽  
Core Samples ◽  
Using Data

Compressive strength is probably one the most crucial properties of concrete material. For existing structures, core samples are drilled and tested to obtain the concrete compressive strength. Many times, taking core samples is not feasible, and as a result, nondestructive methods to examine the concrete are required. The rebound hammer test is one of the most popular methods to estimate concrete compressive strength without causing damage to the existing structure. The test is inexpensive and can be easily conducted compared to other nondestructive testing methods. Also, concrete compressive strength estimations can be obtained almost instantly. However, previous results have shown that concrete compressive strength estimations obtained from rebound hammer tests are not very accurate. As a result, this research attempts to apply artificial intelligence prediction models to estimate concrete compressive strength using data from in situ rebound hammer tests. The results show that artificial intelligence methods can effectively improve in situ concrete compressive strength estimations in rebound hammer tests.

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.

Variasi Penambahan Sika Cim Dan Fiber Kawat Pada Beton Mutu Fc’ 30 Mpa

2018 ◽  
Vol 9 (2) ◽  
pp. 67-73
Author(s):  
M Zainul Arifin
Keyword(s):  
Tensile Strength ◽  
Compressive Strength ◽  
Curing Temperature ◽  
Test Results ◽  
Concrete Compressive Strength ◽  
Normal Concrete ◽  
Astm Method ◽  
Additive Type ◽  
Composition Variation ◽  
Compressive Strength Of Concrete

This research was conducted to determine the value of the highest compressive strength from the ratio of normal concrete to normal concrete plus additive types of Sika Cim with a composition variation of 0.25%, 0.50%, 0.75%, 1.00%, 1.25%, 1 , 50% and 1.75% of the weight of cement besides that in this study also aims to find the highest tensile strength from the ratio of normal concrete to normal concrete in the mixture of sika cim composition at the highest compressive strength above and after that added fiber wire with a size diameter of 1 mm in length 100 mm with a ratio of 1% of material weight. The concrete mix plan was calculated using the ASTM method, the matrial composition of the normal concrete mixture as follows, 314 kg / m3 cement, 789 kg / m3 sand, 1125 kg / m3 gravel and 189 liters / m3 of water at 10 cm slump, then normal concrete added variations of the composition of sika cim 0.25%, 0.50%, 0.75%, 1.00%, 1.25%, 1.5%, 1.75% by weight of cement and fiber, the tests carried out were compressive strength of concrete and tensile strength of concrete, normal maintenance is soaked in fresh water for 28 days at 30oC. From the test results it was found that the normal concrete compressive strength at the age of 28 days was fc1 30 Mpa, the variation in the addition of the sika cim additive type mineral was achieved in composition 0.75% of the cement weight of fc1 40.2 Mpa 30C. Besides that the tensile strength test results were 28 days old with the addition of 1% fiber wire mineral to the weight of the material at a curing temperature of 30oC of 7.5%.

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