Developing a hybrid artificial neural network-genetic algorithm model to predict resilient modulus of polypropylene/polyester fiber-reinforced asphalt concrete

Vadood, Morteza; Johari, Majid Safar; Rahai, Alireza

doi:10.1080/00405000.2014.985882

Cited by 24 publications

(7 citation statements)

References 20 publications

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“…According to these results, the best performance was obtained from the DL model that used the 80% of all data for training. Vadood et al 46 devised a hybrid model to estimate the resilient modulus hybrid fiber–reinforced asphalt concrete. They said that the proposed model estimated the results of the experiment with 96% accuracy.…”

Section: Prediction Results Of the Selected Modelsmentioning

confidence: 99%

Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models

Kına

Türk

Tanyıldızı

2022

Structural Concrete

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In this work, to estimate the compressive, splitting tensile, and flexural strength of self‐compacting concrete (SCC) having single fiber and binary, ternary, and quaternary fiber hybridization, the deep‐learning (DL) and support vector regression (SVR) models were devised. The fiber content and coarse aggregate/total aggregate ratio (CA/TA) were the variables for 24 designed mixtures. Four different fibers, which were a macro steel fiber, two types of micro steel fibers with different aspect ratio, and polyvinyl alcohol (PVA) fiber, were used in SCC mixtures. The specimens of each mixture were tested to measure the engineering properties for 7, 28, and 90 days. The amount of cement, fly ash, fine aggregate, CA, high‐range water‐reducing admixture, water, macro steel fiber, PVA fiber, two types of micro steel fibers, and curing time were selected as input layers while the output layers were strength results. The experimental results were compared with the estimation results. The engineering properties were estimated using the SVR model with 95.25%, 87.81%, and 93.89% accuracy, respectively. Furthermore, the DL model estimated compressive strength, tensile strength, and flexural strength with 99.27%, 98.59%, and 99.15% accuracy, respectively. It was found that the DL estimated the engineering properties of hybrid fiber–reinforced SCC with higher accuracy than SVR.

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Section: Prediction Results Of the Selected Modelsmentioning

confidence: 99%

Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models

Kına

Türk

Tanyıldızı

2022

Structural Concrete

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“…Analysing the current research results from these two perspectives, the intelligent design and simulation modeling of cable components are not integrated to achieve better practical application, which is not in line with the ideal situation of cable automatic optimization design [3].…”

Section: Introductionmentioning

confidence: 94%

Hybrid Optimization Algorithm for Cable Design of Electromechanical Products Based on A* and Genetic Algorithm

2019

IJOMAM

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In order to solve the problem that traditional mechanical and electrical cable design relies heavily on experience and lacks optimization in structure design, aiming at the particularity of cable component design, after comparing several intelligent optimization algorithms synthetically, commonly used A* algorithm and genetic algorithm, which are suitable for cable design, are selected and improved in order to realize the intelligent optimization design of cable component. Firstly, the main technologies needed to be considered in actual cabling design are analysed; the cabling environment requirements of intelligent optimization algorithm are compared, and the automatic generation method of cabling environment is obtained based on three-dimensional software; A* algorithm considering cabling technology is designed and improved to plan the cabling path. The application of genetic algorithm in the design of cable branching structure is analysed, and the partial operation of genetic algorithm is improved to optimize the performance of the algorithm; and then A* algorithm is introduced into the fitness function of genetic algorithm to realize the global optimization design of branching structure and path in cable design. The research results confirm that the hybrid algorithm of A* algorithm and genetic algorithm has certain advantages in the design of cable for mechanical and electrical products. It improves the traditional design algorithm and greatly improves the efficiency of the design of cable for mechanical and electrical products.

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“…Boscato et al, (2020) [25] utilized members made of Glass Fiber Reinforced Polymer (GFRP) were used to analyze numerical and experimental data based on Gaussian Processes Regression. Vadood et al, (2014) [26] investigated regression and artificial neural networks which were used to analyze and estimate the resilience modulus of modified HMA samples constructed with polypropylene and polyester fibers (hybrid and single modes). Cook et al, (2020) [27] analyzed the support vector machine (SVM), multilayer perceptron artificial neural network (MLP-ANN), M5Prime model tree approach (M5P), and RF models were utilized to evaluate the performance of the hybrid RF-FFA model to those of regularly used solo ML models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Binder Content in Glass Fiber Reinforced Asphalt Mix Using Machine Learning Techniques

et al. 2022

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Several researchers have been reported the results of adding a variety of fibers to asphalt concrete described as fiber-reinforced asphalt concrete FRAC. This research paper finds the most suitable prediction model for Marshall Stability and the optimistic bitumen content useful in glass fiber reinforced asphalt mix by performing Marshall Stability tests and further analyzing the data in consonance with published research. Four machine learning approaches were used to find the best prediction model: Artificial Neural Network, Support-Vector-Machine, Gaussian-Process, and Random-Forest. Seven statistical metrics were used to evaluate the performance of the applied models i.e., Coefficient-of-correlation (CC), Mean-Absolute-Error (MAE), Root mean squared error (RMSE), Relative absolute error (RAE), Root relative squared error (RRSE), Scattering index (SI), and (BIAS). Test results of testing stage indicated that the Support Vector Machine (SVM_PUK) model performs the best in validation amongst all applied models with CC values as 0.8776 MAE as 1.2294, RMSE as 1.9653, RAE as 38.33%, RRSE as 55.22%, SI as 1.0648 and BIAS as 0.5005. The Taylor diagram of the testing dataset also confirms that the model based on SVM outperforms the other models. Results of sensitivity analysis show that the bitumen content of about 5% has a significant effect on the Marshall Stability.

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Developing a hybrid artificial neural network-genetic algorithm model to predict resilient modulus of polypropylene/polyester fiber-reinforced asphalt concrete

Cited by 24 publications

References 20 publications

Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models

Estimation of strengths of hybrid FR‐SCC by using deep‐learning and support vector regression models

Hybrid Optimization Algorithm for Cable Design of Electromechanical Products Based on A* and Genetic Algorithm

Prediction of Binder Content in Glass Fiber Reinforced Asphalt Mix Using Machine Learning Techniques

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