Reinforced concrete ultimate bond strength model using hybrid neural network-genetic algorithm

Rinchon, John Pepard Mendoza; Concha, Nolan C.; Calilung, Mary Grace V.

doi:10.1109/hnicem.2017.8269560

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…It also has a significant impact on the network's complexity and performance, and it was chosen because it provides ideal decision biases (b) . The tansig transfer function can understand the complex non-linear connection between the input and output parameters by producing values ranging from −1 to +1 [78]. Figure 3 depicts the design and architecture of the ANN model development.…”

Section: Backpropagation Neural Network (Bp-nn)mentioning

confidence: 99%

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Jesus

Senoro

Cruz

et al. 2021

Toxics

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Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson's correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

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Section: Backpropagation Neural Network (Bp-nn)mentioning

confidence: 99%

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Jesus

Senoro

Cruz

et al. 2021

Toxics

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“…Training Algorithm Levenberg -Marquardt [12] Transfer Function Hyperbolic Tangent Sigmoid [12] Number of Hidden Neurons 8…”

Section: Internal Characteristics Valuementioning

confidence: 99%

Management Competency Model: Predictive Neural Network Approach for Empirical Components of Construction Project Proficiency

Silva

Jesus

Villaverde

et al. 2021

Frontiers in Artificial Intelligence and Applications

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Management competencies are skills that incorporate the understanding, proficiencies and qualities essential for successful performance. Individuals in the top of the organizational hierarchy presents himself being an effective leader by immersing to a readily difficult activity in the project. Molding the important management competencies was found to be hard since the efficacy of a competent construction manager is dependent on countless administrative aspects. The current study intended to offer a construction management competency for human resource development in construction companies. Utilizing this competency model, the company could increase its performance capacity and productivity. This study developed a competency theory and a hybrid predictive model with specific foci on construction managers. The Management Competency Framework Assessment Instrument was developed, following an approach through factor-item analytic mode. Furthermore, this research constructs a predictive performance model using Artificial Neural Network through the factors associated to successful management performance. A sensitivity analysis was also implemented to assess the relative importance of individual factors to the effective construction performance.

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“…A convenient and precise way to model the complex interactions in such intricate systems is by means of artificial neural network [2][3][4][5][6]. There is no need to consider ideal assumptions to simplify the modelling approach as the neural network process raw data from actual experiments.…”

Section: Introductionmentioning

confidence: 99%

An Improved Prediction Model for Bond Strength of Deformed Bars in Rc Using Upv Test and Artificial Neural Network

Concha¹,

Oreta²

2020

GEOMATE

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The composite action of reinforcement in the surrounding concrete involve a complex and nonlinear mechanism. Inadequate understanding of the underlying interactions may lead to designs with insufficient amount of bond resistance of reinforcing bars in concrete structures. To investigate the effects of various parameters on the bond strength of steel bars in concrete, 54 cube samples with varying embedded reinforcements and strengths were prepared. The samples were cured for 28 days and tested using ultrasonic pulse velocity (UPV) test for sample homogeneity and single pull out test for bond strength. Data gathered in the experiment were used in the development of bond strength model as a function of compressive strength, concrete cover to rebar diameter ratio, embedment length, and UPV using artificial neural network (ANN). Of all the bond strength models considered from various literatures, the neural network model provided the most satisfactory prediction results in good agreement with the bond strength values obtained from the experiment. The UPV parameter was found to be one of the most significant predictors in the neural network model having a relative importance of 20.57%. This suggest that the robust prediction performance of the bond model was attributed to this essential component of the model. The proposed model of this study can be used as baseline information and rapid non-destructive assessment for zone wise strengthening in reinforced concrete.

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Reinforced concrete ultimate bond strength model using hybrid neural network-genetic algorithm

Cited by 12 publications

References 14 publications

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

A Hybrid Neural Network–Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Management Competency Model: Predictive Neural Network Approach for Empirical Components of Construction Project Proficiency

An Improved Prediction Model for Bond Strength of Deformed Bars in Rc Using Upv Test and Artificial Neural Network

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