scholarly journals Corrigendum to “Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods”

2020 ◽  
Vol 2020 ◽  
pp. 1-1
Author(s):  
M. Timur Cihan
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Concrete Compressive Strength ◽  
Learning Methods ◽  
Machine Learning Methods

High Performance Concrete (HPC) Compressive Strength Prediction With Advanced Machine Learning Methods

2020 ◽  
pp. 118-140
Author(s):  
Melda Yucel ◽  
Ersin Namlı
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Prediction Model ◽  
High Performance Concrete ◽  
Prediction Method ◽  
Structural Features ◽  
Strength Prediction ◽  
Concrete Compressive Strength ◽  
Learning Methods ◽  
Machine Learning Methods

In this chapter, prediction applications of concrete compressive strength values were realized via generation of various hybrid models, which are based on decision trees as main prediction method, by using different artificial intelligence and machine learning techniques. In respect to this aim, a literature research was presented. Used machine learning methods were explained together with their developments and structural features. Various applications were performed to predict concrete compressive strength, and then feature selection was applied to prediction model in order to determine primarily important parameters for compressive strength prediction model. Success of both models was evaluated with respect to correct and precision prediction of values with different error metrics and calculations.

scholarly journals Prediction of Concrete Compressive Strength and Slump by Machine Learning Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
M. Timur Cihan
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Regression Method ◽  
Concrete Compressive Strength ◽  
Learning Methods ◽  
Acceptable Error ◽  
Machine Learning Methods

Machine learning methods have been successfully applied to many engineering disciplines. Prediction of the concrete compressive strength (fc) and slump (S) is important in terms of the desirability of concrete and its sustainability. The goals of this study were (i) to determine the most successful normalization technique for the datasets, (ii) to select the prime regression method to predict the fc and S outputs, (iii) to obtain the best subset with the ReliefF feature selection method, and (iv) to compare the regression results for the original and selected subsets. Experimental results demonstrate that the decimal scaling and min-max normalization techniques are the most successful methods for predicting the compressive strength and slump outputs, respectively. According to the evaluation metrics, such as the correlation coefficient, root mean squared error, and mean absolute error, the fuzzy logic method makes better predictions than any other regression method. Moreover, when the input variable was reduced from seven to four by the ReliefF feature selection method, the predicted accuracy was within the acceptable error rate.

scholarly journals A comparison of machine learning methods for predicting the compressive strength of field-placed concrete

2019 ◽  
Vol 228 ◽  
pp. 116661 ◽  
Author(s):  
M.A. DeRousseau ◽  
E. Laftchiev ◽  
J.R. Kasprzyk ◽  
B. Rajagopalan ◽  
W.V. Srubar
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Comparison of Machine Learning Approaches with Traditional Methods for Predicting the Compressive Strength of Rice Husk Ash Concrete

Crystals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 779
Author(s):  
Muhammad Nasir Amin ◽  
Ammar Iqtidar ◽  
Kaffayatullah Khan ◽  
Muhammad Faisal Javed ◽  
Faisal I. Shalabi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Rice Husk ◽  
Rice Husk Ash ◽  
Harmful Effect ◽  
Partial Replacement ◽  
Learning Methods ◽  
Traditional Methods ◽  
Inference System ◽  
Machine Learning Methods

Efforts are being devoted to reducing the harmful effect of the construction industry around the globe, including the use of rice husk ash as a partial replacement of cement. However, no method is available to date to predict the compressive strength (CS) of rice husk ash blended concrete (RHAC). In this study, advanced machine learning techniques (artificial neural network, artificial neuro-fuzzy inference system) were used to predict the CS of RHAC. Based on the published literature, six inputs, i.e., age of specimen, percentage of rice husk ash, percentage of superplasticizer, aggregates, water, and amount of cement, were selected. Results obtained from machine learning methods were compared with traditional methods such as linear and non-linear regressions. It was observed that the performance of machine learning methods was superior to traditional methods for determining the CS of RHAC. This study will prove beneficial in minimizing the cost and time of executing laboratory experiments for designing the optimum content portions of RHAC.

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