Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network

Du, Guoqiang; Bu, Liangtao; Hou, Qi; Zhou, Jing; Lu, Beixin

doi:10.1371/journal.pone.0250795

Cited by 19 publications

(3 citation statements)

References 32 publications

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“…The hammer rebound index and the compressive strength relationship is therefore modified, which in turn means that conventional formulas are invalid for SCC [27]. Nevertheless, some research has been focused on the extent to which varying proportions of fine aggregate and flowability in the fresh state can affect the hammer rebound index of SCC [28], from which models have been developed that relate it to compressive strength [29].…”

Section: Introductionmentioning

confidence: 99%

Hammer rebound index as an overall-mechanical-quality indicator of self-compacting concrete containing recycled concrete aggregate

Revilla‐Cuesta

Ortega-López

Faleschini

et al. 2022

Construction and Building Materials

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Section: Introductionmentioning

confidence: 99%

Hammer rebound index as an overall-mechanical-quality indicator of self-compacting concrete containing recycled concrete aggregate

Revilla‐Cuesta

Ortega-López

Faleschini

et al. 2022

Construction and Building Materials

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“…Data standardization processing is the key step of neural network technology and can greatly improve the learning efficiency of ANNs. It can eliminate the difference in dimensions between different data 66 , 67 and control the data range within [0,1]. The standardized processing formula is shown in Eq.…”

Section: Methodsmentioning

confidence: 99%

Prediction of the shear capacity of ultrahigh-performance concrete beams using neural network and genetic algorithm

Hou

Hou²

2023

Sci Rep

Self Cite

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Currently, concrete structures have increasingly higher requirements for the shear capacity of beams, and ultrahigh-performance concrete (UHPC) beams are increasingly widely used. To facilitate the design of UHPC beams, this paper constructs a UHPC beam shear strength prediction model. First, static shear tests were conducted on 6 UHPC beam specimens with a length of 2 m and a cross-sectional size of 200 mm × 300 mm to explore the effects of the UHPC strength, shear span ratio, hoop ratio, and steel fiber content on the shear resistance and failure morphology of the UHPC beams. Based on the results of this study and a static load experiment of 102 UHPC beams in the literature, the construction includes the shear span ratio (λ), beam section width (b), beam section height (h), hoop ratio (ρSV), UHPC compressive strength (fc), steel fiber volume fraction (Vf), and the UHPC beam shear capacity (Vex) 7 parameter database. Based on the construction of the database, 1200 BPNN models were trained through trial and error. The models were evaluated using the correlation coefficient R, root mean square error RMSE, and a20-index indicators, and the optimal BPNN model (6-15-8-1) was determined based on the ranking of RMSE. After the optimal BPNN is optimized by a genetic algorithm, the prediction performance of the model is improved. The correlation coefficient between the predicted value and the experimental value is R2 = 0.98667, and RMSE = 7.38. This model can reliably predict the shear strength of UHPC beams and provide designers with a reference for the design of UHPC beams. Finally, after sensitivity analysis, the influence of each input parameter on the UHPC shear capacity is determined.

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“…Currently, the optimization of neural networks is primarily achieved to attain the optimal prediction model. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are common model optimization methods [16] which are both capable of ameliorating situations in which BP neural networks can easily fall into local optima, thus achieving optimal prediction effects [17,18].…”

Section: Introductionmentioning

confidence: 99%

Study on the Residence Time and Texture Prediction of Pea Protein Extrusion Based on Image Analysis

Wu,

Zhang,

Gao

et al. 2023

Foods

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This paper initially involves three main processing parameters: screw speed, feeding speed, and initial material moisture content, exploring the RTD of materials inside the extruder barrel under varying parameters and clarifying the impact of parameter variations on RTD. Finally, machine vision technology was utilized to link extruded product images to texture features, and a texture prediction model based on image features was established using a Back Propagation (BP) neural network. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to optimize the BP neural network. The results indicate that the feeding speed has a stronger impact than the screw speed on the extrusion process, and an increase in the initial material moisture content tends to shorten the RTD. Specifically, an increase in screw speed results in a denser product structure, while higher feeding speeds lead to reduced pore size in the microstructure. As the initial material moisture content increased from 55% to 70%, the average residence time MRT decreased from 265.21 s to 166.62 s. Additionally, elevated moisture content causes a more porous microstructure. After optimizing the texture prediction model of extruded products through the application of Particle Swarm Optimization and Genetic Algorithm models, it was discovered that the Genetic Algorithm was more effective in reducing errors (p < 0.05) than the Particle Swarm Optimization algorithm. It was found that the Particle Swarm Optimization model exhibited better prediction performance. The results of the prediction indicated a significant association between the image features of the product and hardness, resilience, and chewiness, as corroborated by correlation coefficients of 0.93913, 0.94040, and 0.94724, respectively.

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Prediction of the compressive strength of high-performance self-compacting concrete by an ultrasonic-rebound method based on a GA-BP neural network

Cited by 19 publications

References 32 publications

Hammer rebound index as an overall-mechanical-quality indicator of self-compacting concrete containing recycled concrete aggregate

Hammer rebound index as an overall-mechanical-quality indicator of self-compacting concrete containing recycled concrete aggregate

Prediction of the shear capacity of ultrahigh-performance concrete beams using neural network and genetic algorithm

Study on the Residence Time and Texture Prediction of Pea Protein Extrusion Based on Image Analysis

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