scholarly journals Analyzing Uniaxial Compressive Strength of Concrete Using a Novel Satin Bowerbird Optimizer

2021 ◽  
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Search Algorithm ◽  
Large Dataset ◽  
Satin Bowerbird ◽  
Proposed Model ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural Network Ann ◽  
Non Destructive ◽  
New Algorithms

Surmounting the complexities in analyzing the mechanical parameters of concrete entails selecting an appropriate methodology. This study integrates artificial neural network (ANN) with a novel metaheuristic technique, namely satin bowerbird optimizer (SBO) for predicting uniaxial compressive strength (UCS) of concrete. For this purpose, the created hybrid is trained and tested using a relatively large dataset collected from the published literature. Three other new algorithms, namely Henry gas solubility optimization (HGSO), sunflower optimization (SFO), and vortex search algorithm (VSA) are also used as benchmarks. After attaining a proper population size for all algorithms, the Utilizing various accuracy indicators, it was shown that the proposed ANN-SBO not only can excellently analyze the UCS behavior, but also outperforms all three benchmark hybrids (i.e., ANN-HGSO, ANN-SFO, and ANN-VSA). In the prediction phase, the correlation indices of 0.87394, 0.87936, 0.95329, and 0.95663, as well as mean absolute percentage errors of 15.9719, 15.3845, 9.4970, and 8.0629%, calculated for the ANN-HGSO, ANN-SFO, ANN-VSA, and ANN-SBO, respectively, manifested the best prediction performance for the proposed model. Also, the ANN-VSA achieved reliable results as well. In short, the ANN-SBO can be used by engineers as an efficient non-destructive method for predicting the UCS of concrete.

scholarly journals Predicting Compressive Strength of Concrete Containing Recycled Aggregate Using Modified ANN with Different Optimization Algorithms

2021 ◽  
Vol 11 (2) ◽  
pp. 485
Author(s):  
Amirreza Kandiri ◽  
Farid Sartipi ◽  
Mahdi Kioumarsi
Keyword(s):  
Compressive Strength ◽  
Optimization Algorithms ◽  
Recycled Aggregate ◽  
Recycled Aggregates ◽  
Tree Model ◽  
Grasshopper Optimization Algorithm ◽  
Compressive Strength Of Concrete ◽  
Grasshopper Optimization ◽  
Artificial Neural Network Ann ◽  
Industry Needs

Using recycled aggregate in concrete is one of the best ways to reduce construction pollution and prevent the exploitation of natural resources to provide the needed aggregate. However, recycled aggregates affect the mechanical properties of concrete, but the existing information on the subject is less than what the industry needs. Compressive strength, on the other hand, is the most important mechanical property of concrete. Therefore, having predictive models to provide the required information can be helpful to convince the industry to increase the use of recycled aggregate in concrete. In this research, three different optimization algorithms including genetic algorithm (GA), salp swarm algorithm (SSA), and grasshopper optimization algorithm (GOA) are employed to be hybridized with artificial neural network (ANN) separately to predict the compressive strength of concrete containing recycled aggregate, and a M5P tree model is used to test the efficiency of the ANNs. The results of this study show the superior efficiency of the modified ANN with SSA when compared to other models. However, the statistical indicators of the hybrid ANNs with SSA, GA, and GOA are so close to each other.

Determination of a Suitable Moment for Formwork Removal from a Concrete Structure Using Rebound Hammer Test Methods

2021 ◽  
Vol 322 ◽  
pp. 23-27
Author(s):  
Petr Misák ◽  
Dalibor Kocáb ◽  
Petr Cikrle
Keyword(s):  
Compressive Strength ◽  
Characteristic Curve ◽  
Test Methods ◽  
Rebound Hammer ◽  
Practical Applications ◽  
Relevant Topic ◽  
Compressive Strength Of Concrete ◽  
The Moment ◽  
Non Destructive

Determining the compressive strength of concrete in the early stages of ageing has been an increasingly relevant topic in recent years, particularly with regard to the safe removal of formwork from a structure or its part. The compressive strength of concrete which designates safe removal of formwork without damaging the structure can be referred to as "stripping strength". It is undoubtedly beneficial to be able to determine the moment of safe formwork removal in a non-destructive manner, i.e. without compromising the structure. Modern rebound hammer test methods seem to be a suitable instrument with which it is possible to reduce the length of technological breaks associated with concrete ageing to a minimum, and consequently, reduce the total cost of the construction. However, the use of these methods presents a number of challenges. As many conducted experiments have shown, there is no single conversion relationship (regression model) between non-destructive rebound hammer test methods and compressive strength. It is therefore advisable to always create a unique conversion relationship for each individual concrete. In addition, it must be noted that conventional regression analysis methods operate with 50% reliability. In construction testing, however, the most common is the so-called characteristic value, which is defined as a 5% quantile. This value is therefore determined with 95% reliability. This paper describes the construction of a so-called "characteristic curve", which can be used to estimate the compressive strength of concrete in a structure using rebound hammer test methods with 95% reliability. Consequently, the values obtained from the characteristic curve can be easily used for practical applications.

scholarly journals Using Support Vector Machine to Improve Ultrasonic Pulse Velocity Test for Concrete

2018 ◽  
Vol 207 ◽  
pp. 01001
Author(s):  
Tu Quynh Loan Ngo ◽  
Yu-Ren Wang
Keyword(s):  
Compressive Strength ◽  
Ultrasonic Pulse Velocity ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Testing Methods ◽  
Destructive Testing ◽  
Compressive Strength Of Concrete ◽  
Non Destructive

In the construction industry, to evaluate the compressive strength of concrete, destructive and non-destructive testing methods are used. Non-destructive testing methods are preferable due to the fact that those methods do not destroy concrete samples. However, they usually give larger percentage of error than using destructive tests. Among the non-destructive testing methods, the ultrasonic pulse velocity test is the popular one because it is economic and very simple in operation. Using the ultrasonic pulse velocity test gives 20% MAPE more than using destructive tests. This paper aims to improve the ultrasonic pulse velocity test results in estimating the compressive strength of concrete using the help of artificial intelligent. To establish a better prediction model for the ultrasonic pulse velocity test, data collected from 312 cylinder of concrete samples are used to develop and validate the model. The research results provide valuable information when using the ultrasonic pulse velocity tests to the inputs data in addition with support vector machine by learning algorithms, and the actual compressive strengths are set as the target output data to train the model. The results show that both MAPEs for the linear and nonlinear regression models are 11.17% and 17.66% respectively. The MAPE for the support vector machine models is 11.02%. These research results can provide valuable information when using the ultrasonic pulse velocity test to estimate the compressive strength of concrete.

scholarly journals A New Evaluation Method for the Uniaxial Compressive Strength ahead of the Tunnel Face Based on the Driving Data and Specification Parameters of TBM

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yuexiu Wu ◽  
Chishuai Ma ◽  
Xianjun Tan ◽  
Dianseng Yang ◽  
Hongming Tian ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Uniaxial Compressive Strength ◽  
Evaluation Method ◽  
Hard Rock ◽  
Rock Type ◽  
Soft Rock ◽  
Tunnel Face ◽  
Diversion Tunnel ◽  
Proposed Model ◽  
High Prediction

Uniaxial compressive strength (UCS) is a very important fundamental mechanical parameter for TBM construction. In this work, a predictive model of UCS was proposed according to the TBM parameters including torque, penetration, cutter number, and cutter diameter. The parameter of the new proposed model was established by fourteen existed TBM tunnels’ construction data. To describe the relationships of UCS with PLSI of the Murree tertiary hard rocks, regression analyses have been conducted and a fitting equation with high-prediction performance was developed. Validation from the data of Neelum–Jhelum (NJ) TBM diversion tunnel were carried out. The absolute errors between predictive UCS and experimental UCS were presented. Through comparison, it can be concluded that the proposed calculation equation of UCS has a high accuracy for a certain rock type with UCS from 50 MPa to 200 MPa. For special hard rock or soft rock, a new calculation equation between UCS and TBM parameters should be studied furthermore.

scholarly journals Prediction of Compressive Strength Using Support Vector Regression

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 51-56
Author(s):  
Goutham J Sai ◽  
Vijay Pal Singh
Keyword(s):  
Compressive Strength ◽  
Support Vector Regression ◽  
Design Stage ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Study Support ◽  
Compressive Strength Of Concrete ◽  
Non Destructive

At the design stage of a structure, the members of adequate dimension and strength is provided. But with passage of time, the strength of the members reduces gradually due to exposure to environmental conditions and unexpected loadings other than for which the structure is designed. Non Destructive Testing (NDT) method provides a convenient and rapid method of determination of existing strength of concrete without subjecting the member to any damage.  In the present study, Support Vector Regression (SVR) in Python has been used for the prediction of compressive strength of concrete. Three different NDT techniques have been used as input for the SVR model. A good co-relation between predicted strength and strength determined after crushing the concrete cubes has been achieved. It has also been observed that accuracy in the predicted strength is more in case of inputs from more than one NDT technique is used.

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