Comparison of artificial neural network and coupled simulated annealing based least square support vector regression models for prediction of compressive strength of high-performance concrete

Rad, Mostafa Ayubi; Ayubirad, Mohammad Sadegh

doi:10.24200/sci.2017.2412

Cited by 14 publications

(10 citation statements)

References 25 publications

(32 reference statements)

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“…Similarly, in A-GTAW process the lowest energy level gives the optimized value for variables based on an energy function is created and minimized. The mechanism of SA algorithm is defined as follows 23 :…”

Section: Modeling and Optimization Of The Processmentioning

confidence: 99%

Modeling and optimization of A-GTAW process using Box–Behnken design and hybrid BPNN-PSO approach

Moghaddam

Kolahan

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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Gas tungsten arc welding (GTAW) is the most extensively used process capable of fabricating a wide range of alloys based on its distinctive merits has been introduced. However, some demerits have been reported among which shallow penetration is the most crucial ones. In order to deal with the poor penetration of the process, different procedures have been proposed among which activated gas tungsten arc welding (A-GTAW) is the most extensively used one. In this study effect of percentage of activating fluxes (TiO2 and SiO2) combination (F) and the most important process variables (welding current (C), welding speed (S)) on the most important process measures (weld bead width (WBW), depth of penetration (DOP), and consequently aspect ratio (ASR)) in welding of AISI316L austenite stainless steel parts have been investigated. Box-behnken design (BBD) has been used to design the experimental matrix required for date gathering, modeling, and statistical analysis purposes. A neural network with a back propagation algorithm (BPNN) in artificial neural network (ANN) modeling approach has been employed to relate the process input variables and output characteristics. The proper BPNN architecture (number of hidden layers and neurons/nodes in each hidden layer) has been determined using particle swarm optimization (PSO) algorithm. Moreover, process optimization in such a way that maximum DOP, minimum WBW, and desired ASR achieved has been carried out using PSO algorithm. Next, the performance of PSO algorithm has been checked using simulated annealing (SA) algorithm. Finally, to evaluate the performance of the proposed method a set of confirmation experimental test has been conducted. Results of experimental tests revealed that the proposed method is quite efficient in modeling and optimization (with less than 4% error) of A-GTAW process.

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Section: Modeling and Optimization Of The Processmentioning

confidence: 99%

Modeling and optimization of A-GTAW process using Box–Behnken design and hybrid BPNN-PSO approach

Moghaddam

Kolahan

2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Additionally, in a study, Ayubi‐Rad modeled the mechanical CS feature of concrete using SVR (support vector regression) based on the couple‐simulated annealing (CSA) to realize the nonlinear relationship among the concrete samples' components as the fine aggregates, water, FLAs, the blast furnace slags, the superplasticizer, the coarse aggregates, and age of samples. Consequently, using the correlation rate of 92% and 6.17 MPa recorded as RMSE index, results showed the developed models had a substantial ability to predict the CS rates of samples 38 …”

Section: Introductionmentioning

confidence: 99%

“…Consequently, using the correlation rate of 92% and 6.17 MPa recorded as RMSE index, results showed the developed models had a substantial ability to predict the CS rates of samples. 38 Also, the combination of main predictive models with optimization algorithms can boost the accuracy of estimation that has not been seen in studies. For this reason, the present study has aimed to investigate the joining two matheuristic optimization algorithms, namely sine cosine algorithm (SCA) and improved grey wolf optimization (IGWO), with a SVM, as hybrid frameworks, to appraise the hardness properties of HPC compounds by modifying its key variables.…”

Section: Introductionmentioning

confidence: 99%

Developing a support vector regression model via optimization algorithms to appraise the hardness properties of high‐performance concrete

Yan

2022

Structural Concrete

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High-performance concrete (HPC) as a highly sophisticated aggregate in constructional projects has made modeling given mechanical properties a very complex problem. Declaring by many studies, mechanical features of HPC are not only characterized by the maximum size of coarse aggregate and water amount since influencing by the other components. Using fly-ash and silica fume as the key constituents can simultaneously increase the hardness aspects and the environmental effects. Considering the compressive strength and slump flow of concrete should be investigated before performing any practical practices. Artificial intelligence approaches with precise and low-cost methods can replace the costly experimental ways. Therefore, the present paper has aimed to link a prediction model with optimization algorithms to accurately appraise the hardness properties of HPC samples rarely found in literature like this way. In this regard, a machine learning approach of Support Vector Regression using two kernels of Gaussian and radial basis function is coupled with matheuristic algorithms to optimize the modeling process of compressive strength and slump flow of HPC samples. The internal settings of SVR would be tuned at optimal rate by optimizers to function efficiently. To investigate the performance of hybrid frameworks developed in this research, several indicators evaluated the results of hybrid models. Therefore, the R 2 of the models was calculated averagely at 0.91 with a maximum difference rate of 11% for the testing phase. While the RMSE index assessed the models with higher values of 16.56 mm for slump and 12.86 MPa for compressive strength. Generally, using smart approaches with high-accuracy performance has been proposed to be used instead of physical procedures increasing the productivity of concrete compressive strength in terms of time, energy, and cost criteria.

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“…ese algorithms are extensively used in ANN training for accurate error estimation. ese include the genetic algorithm (GA) [26,27], particle swarm optimization (PSO) [28], or simulated annealing (SA) [29]. e genetic algorithm can find global and local optimums and may be even trapped in local optimums, but it has rapid convergence [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…e genetic algorithm can find global and local optimums and may be even trapped in local optimums, but it has rapid convergence [26,27]. A growing number of studies have recently adopted ANN [27,28,30] and SVR [29] as a model for regression network estimation.…”

Section: Introductionmentioning

confidence: 99%

Fusion‐Learning‐Based Optimization: A Modified Metaheuristic Method for Lightweight High‐Performance Concrete Design

2022

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In order to build high-quality concrete, it is imperative to know the raw materials in advance. It is possible to accurately predict the quality of concrete and the amount of raw materials used using machine learning-enhanced methods. An automated process based on machine learning strategies is proposed in this paper for predicting the compressive strength of concrete. Fusion-learning-based optimization is used in the proposed approach to generate a strong learner by pooling support vector regression models. The SVR technique proposes an optimization method for finding the kernel radial basis function (RBF) parameters based on improving the innovative gunner algorithm (AIG). As a result of AIG's diverse solutions, local optima are effectively avoided. Therefore, the novelty of our research is that, in solving the uncertainty of predicted outputs based on integrated models, we use fusion-learning-based optimization to improve regression discrimination. We also collected a standard dataset to analyze the proposed algorithm, and subsequently, the dataset was designed from concrete laboratory tests on 244 samples, seven features, and three outputs. Different regression intensities are determined by correlation analysis of responses. Regression fusion is sufficiently accurate to estimate the number of desired outcomes examined based on the appropriate input data sample. The best quality concrete can be achieved with an error rate of less than 5%.

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Comparison of artificial neural network and coupled simulated annealing based least square support vector regression models for prediction of compressive strength of high-performance concrete

Cited by 14 publications

References 25 publications

Modeling and optimization of A-GTAW process using Box–Behnken design and hybrid BPNN-PSO approach

Modeling and optimization of A-GTAW process using Box–Behnken design and hybrid BPNN-PSO approach

Developing a support vector regression model via optimization algorithms to appraise the hardness properties of high‐performance concrete

Fusion‐Learning‐Based Optimization: A Modified Metaheuristic Method for Lightweight High‐Performance Concrete Design

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