scholarly journals Application of soft computing methodologies to predict the 28-day compressive strength of shotcrete: a comparative study of individual and hybrid models

2021 ◽  
Vol 36 (5) ◽  
pp. 33-48
Author(s):  
Mahtab Torkan ◽  
Hamid Kalhori ◽  
Mohammad Hossein Jalalian
Keyword(s):  
Compressive Strength ◽  
Soft Computing ◽  
Clustering Algorithm ◽  
Accurate Method ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Construction Technique ◽  
Computing Methodologies ◽  
Model Tree ◽  
M5p Model Tree

Shotcreting is a popular construction technique with wide-ranging applications in mining and civil engineering. Compressive strength is a primary mechanical property of shotcrete with particular importance for project safety, which highly depends on its mix design. But in practice, there is no reliable and accurate method to predict this strength. In this study, existing experimental data related to shotcretes with 59 different mix designs are used to develop a series of soft computing methodologies, including individual artificial neural network, support vector regression, and M5P model tree and their hybrids with the fuzzy c-means clustering algorithm so as to predict the 28-day compressive strength of shotcrete. Analysis of the results shows the superiority of the hybrid model over the individual models in predicting the compressive strength of shotcrete. Overall, data clustering prior to use of machine learning techniques leads to certain improvement in their performance and reliability and generalizability of their results. In particular, the M5P model tree exhibits excellent capability in anticipating the compressive strength of shotcrete.

Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm

2017 ◽  
Vol 142 ◽  
pp. 199-207 ◽  
Author(s):  
Ali Behnood ◽  
Venous Behnood ◽  
Mahsa Modiri Gharehveran ◽  
Kursat Esat Alyamac
Keyword(s):  
Compressive Strength ◽  
High Performance ◽  
Tree Algorithm ◽  
Model Tree ◽  
M5p Model Tree

scholarly journals Prediction of Static Modulus and Compressive Strength of Concrete from Dynamic Modulus Associated with Wave Velocity and Resonance Frequency Using Machine Learning Techniques

Materials ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 2886
Author(s):  
Jong Yil Park ◽  
Sung-Han Sim ◽  
Young Geun Yoon ◽  
Tae Keun Oh
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Elastic Modulus ◽  
Resonance Frequency ◽  
P Wave ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Regression Equations ◽  
S Wave ◽  
Wave Velocities

The static elastic modulus (Ec) and compressive strength (fc) are critical properties of concrete. When determining Ec and fc, concrete cores are collected and subjected to destructive tests. However, destructive tests require certain test permissions and large sample sizes. Hence, it is preferable to predict Ec using the dynamic elastic modulus (Ed), through nondestructive evaluations. A resonance frequency test performed according to ASTM C215-14 and a pressure wave (P-wave) measurement conducted according to ASTM C597M-16 are typically used to determine Ed. Recently, developments in transducers have enabled the measurement of a shear wave (S-wave) velocities in concrete. Although various equations have been proposed for estimating Ec and fc from Ed, their results deviate from experimental values. Thus, it is necessary to obtain a reliable Ed value for accurately predicting Ec and fc. In this study, Ed values were experimentally obtained from P-wave and S-wave velocities in the longitudinal and transverse modes; Ec and fc values were predicted using these Ed values through four machine learning (ML) methods: support vector machine, artificial neural networks, ensembles, and linear regression. Using ML, the prediction accuracy of Ec and fc was improved by 2.5–5% and 7–9%, respectively, compared with the accuracy obtained using classical or normal-regression equations. By combining ML methods, the accuracy of the predicted Ec and fc was improved by 0.5% and 1.5%, respectively, compared with the optimal single variable results.

scholarly journals Diabetes Mellitus Prediction using Ensemble Machine Learning Techniques

2020 ◽  
Vol 9 (2) ◽  
pp. 312-316
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Ensemble Classifier ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Healthcare Industry ◽  
Blurred Vision ◽  
The Way

The healthcare industry is inflicted with the plethora of patient data which is being supplemented each day manifold. Researchers have been continually using this data to help the healthcare industry improve upon the way major diseases could be handled. They are even working upon the way the patients could be informed timely of the symptoms that could avoid the major hazards related to them. Diabetes is one such disease that is growing at an alarming rate today. In fact, it can inflict numerous severe damages; blurred vision, myopia, burning extremities, kidney and heart failure. It occurs when sugar levels reach a certain threshold, or the human body cannot contain enough insulin to regulate the threshold. Therefore, patients affected by Diabetes must be informed so that proper treatments can be taken to control Diabetes. For this reason, early prediction and classification of Diabetes are significant. This work makes use of Machine Learning algorithms to improve the accuracy of prediction of the Diabetes. A dataset obtained as an output of K-Mean Clustering Algorithm was fed to an ensemble model with principal component analysis and K-means clustering. Our ensemble method produced only eight incorrectly classified instances, which was lowest compared to other methods. The experiments also showed that ensemble classifier models performed better than the base classifiers alone. Its result was compared with the same Dataset being applied on specific methods like random forest, Support Vector Machine, Decision Tree, Multilayer perceptron, and Naïve Bayes classification methods. All methods were run using 10k fold cross-validation.

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.

scholarly journals An Effective Heart Disease Prediction Model based on Machine Learning Techniques

2020 ◽  
Author(s):  
Rony Chowdhury Ripan ◽  
Iqbal H. Sarker ◽  
Md. Hasan Furhad ◽  
Md Musfique Anwar ◽  
Mohammed Moshiul Hoque
Keyword(s):  
Heart Disease ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor ◽  
Healthcare Data ◽  
Learning Techniques

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

Prediction of bearing capacity of H shaped skirted footings on sand using soft computing techniques

2020 ◽  
Vol 2 (103) ◽  
pp. 62-74
Author(s):  
T. Gnananandarao ◽  
V.N. Khatri ◽  
R.K. Dutta
Keyword(s):  
Bearing Capacity ◽  
Soft Computing ◽  
Field Data ◽  
Friction Angle ◽  
Ultimate Bearing Capacity ◽  
Statistical Parameters ◽  
Model Tree ◽  
Soft Computing Techniques ◽  
M5p Model Tree ◽  
Model Equations

Purpose: The present study aims to apply soft computing techniques, Artificial Neural Network (ANN) and M5P model tree, to predict the ultimate bearing capacity of the H plan shaped skirted footing on the sand Design/methodology/approach: A total of 162 laboratory test data for the regular plan shaped (square, circular, rectangular, and strip (up to L/B = 2.5) skirted footing were collected from the literature to develop the soft computing-based models. These models were later modified for the H Plan shaped skirted footing with the introduction of the multiplication factor. The input variables chosen for the regular plan shaped footings were skirt depth to width of the footing ratio (Ds/B), friction angle of the sand (􀉭), the ratio of the interface friction angle-to-friction angle of sand (δ/􀉭), and length-to-width (L/B) ratio of the footing. The output is the bearing capacity ratio (BCR, a ratio of the bearing capacity of the skirted footing to the bearing capacity of un-skirted footing). Findings: Sensitivity analysis was carried out to see the impact of the individual variable on the BCR). The sensitivity results reveal that the skirt depth to width of the footing ratio is the primary variable affecting the BCR. Finally, the performance of the developed soft computing models was assessed using six statistical parameters. The results from the statistical parameters reveal that model developed using ANN was performing superior to the one prepared using M5P model tree technique for the prediction of the ultimate bearing capacity of H plan shaped skirted footing on sand. Research limitations/implications: The model equations are developed with experimental laboratory data. Hence, these equations need further improvement by using field data. However, until now there no field data have been available to include in the present data set. Practical implications: These proposed model equations can be used to predict the bearing capacity of the H-shaped footing with the help of Ds/B, 􀉭, δ/􀉭 and L/B without performing the laboratory experiments. Originality/value: There is no such model equation that was developed so far for the H-shaped skirted footings. Hence, an attempt was made in this article to predict the bearing capacity of the H-shaped footing by using available experimental data with the help of soft computing techniques.

RETRACTED: Estimating unconfined compressive strength of cockle shell–cement–sand mixtures using soft computing methodologies

2015 ◽  
Vol 98 ◽  
pp. 49-58 ◽  
Author(s):  
Shervin Motamedi ◽  
Shahaboddin Shamshirband ◽  
Roslan Hashim ◽  
Dalibor Petković ◽  
Chandrabhushan Roy
Keyword(s):  
Compressive Strength ◽  
Soft Computing ◽  
Unconfined Compressive Strength ◽  
Computing Methodologies ◽  
Cockle Shell

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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