scholarly journals Machine learning techniques to predict the compressive strength of concrete

2020 ◽  
Vol 36 ◽  
Author(s):  
P. Silva ◽  
G. Moita ◽  
V. Arruda
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Compressive Strength Of Concrete

scholarly journals Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Palika Chopra ◽  
Rajendra Kumar Sharma ◽  
Maneek Kumar ◽  
Tanuj Chopra
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Software Environment ◽  
R Software ◽  
Learning Techniques ◽  
Compressive Strength Of Concrete ◽  
Indispensable Tool

A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT) model, random forest (RF) model, and neural network (NN) model have been used and compared with the help of coefficient of determination (R2) and root-mean-square error (RMSE), and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

scholarly journals Machine Learning Techniques in Concrete Mix Design

Materials ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1256 ◽  
Author(s):  
Patryk Ziolkowski ◽  
Maciej Niedostatkiewicz
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
State Of The Art ◽  
Mix Design ◽  
Machine Learning Techniques ◽  
Concrete Mix Design ◽  
Learning Techniques ◽  
Concrete Mix

Concrete mix design is a complex and multistage process in which we try to find the best composition of ingredients to create good performing concrete. In contemporary literature, as well as in state-of-the-art corporate practice, there are some methods of concrete mix design, from which the most popular are methods derived from The Three Equation Method. One of the most important features of concrete is compressive strength, which determines the concrete class. Predictable compressive strength of concrete is essential for concrete structure utilisation and is the main feature of its safety and durability. Recently, machine learning is gaining significant attention and future predictions for this technology are even more promising. Data mining on large sets of data attracts attention since machine learning algorithms have achieved a level in which they can recognise patterns which are difficult to recognise by human cognitive skills. In our paper, we would like to utilise state-of-the-art achievements in machine learning techniques for concrete mix design. In our research, we prepared an extensive database of concrete recipes with the according destructive laboratory tests, which we used to feed the selected optimal architecture of an artificial neural network. We have translated the architecture of the artificial neural network into a mathematical equation that can be used in practical applications.

scholarly journals Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

2019 ◽  
Vol 9 (18) ◽  
pp. 3715 ◽  
Author(s):  
Hai Xu ◽  
Jian Zhou ◽  
Panagiotis G. Asteris ◽  
Danial Jahed Armaghani ◽  
Mahmood Md Tahir
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Predictive Models ◽  
Penetration Rate ◽  
Tunnel Boring Machine ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Tunnel Boring ◽  
Learning Techniques

Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.

Prediction of cement-based mortars compressive strength using machine learning techniques

2021 ◽  
Author(s):  
Panagiotis G. Asteris ◽  
Mohammadreza Koopialipoor ◽  
Danial J. Armaghani ◽  
Evgenios A. Kotsonis ◽  
Paulo B. Lourenço
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Modelling and Prediction of Concrete Compressive Strength Using Machine Learning

2021 ◽  
pp. 526-532
Author(s):  
K Sumanth Reddy ◽  
Gaddam Pranith ◽  
Karre Varun ◽  
Thipparthy Surya Sai Teja
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Rational Relation ◽  
Compressive Strength Of Concrete ◽  
And Performance ◽  
Materials Used

The compressive strength of concrete plays an important role in determining the durability and performance of concrete. Due to rapid growth in material engineering finalizing an appropriate proportion for the mix of concrete to obtain the desired compressive strength of concrete has become cumbersome and a laborious task further the problem becomes more complex to obtain a rational relation between the concrete materials used to the strength obtained. The development in computational methods can be used to obtain a rational relation between the materials used and the compressive strength using machine learning techniques which reduces the influence of outliers and all unwanted variables influence in the determination of compressive strength. In this paper basic machine learning technics Multilayer perceptron neural network (MLP), Support Vector Machines (SVM), linear regressions (LR) and Classification and Regression Tree (CART), have been used to develop a model for determining the compressive strength for two different set of data (ingredients). Among all technics used the SVM provides a better results in comparison to other, but comprehensively the SVM cannot be a universal model because many recent literatures have proved that such models need more data and also the dynamicity of the attributes involved play an important role in determining the efficacy of the model.

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