scholarly journals Hybrid Artificial Intelligence Model Development for Roller-compacted Concrete Compressive Strength Estimation

2020 ◽  
Vol 33 (10) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Model Development ◽  
Concrete Compressive Strength ◽  
Roller Compacted Concrete ◽  
Hybrid Artificial Intelligence

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 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.

Applying Support Vector Machines in Rebound Hammer Test

2013 ◽  
Vol 853 ◽  
pp. 600-604 ◽  
Author(s):  
Yu Ren Wang ◽  
Wen Ten Kuo ◽  
Shian Shien Lu ◽  
Yi Fan Shih ◽  
Shih Shian Wei
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Support Vector Machines ◽  
Prediction Model ◽  
Prediction Models ◽  
Percentage Error ◽  
Support Vector ◽  
Concrete Compressive Strength ◽  
Rebound Hammer ◽  
Vector Machines

There are several nondestructive testing techniques available to test the compressive strength of the concrete and the Rebound Hammer Test is among one of the fast and economical methods. Nevertheless, it is found that the prediction results from Rebound Hammer Test are not satisfying (over 20% mean absolute percentage error). In view of this, this research intends to develop a concrete compressive strength prediction model for the SilverSchmidt test hammer, using data collected from 838 lab tests. The Q-values yield from the concrete test hammer SilverSchmidt is set as the input variable and the concrete compressive strength is set as the output variable for the prediction model. For the non-linear relationships, artificial intelligence technique, Support Vector Machines (SVMs), are adopted to develop the prediction models. The results show that the mean absolute percentage errors for SVMs prediction model, 6.76%, improves a lot when comparing to SilverSchmidt predictions. It is recommended that the artificial intelligence prediction models can be applied in the SilverSchmidt tests to improve the prediction accuracy.

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