Numerical approach to predict the flexural damage behavior of pervious concrete

Nguyen, Hoang Quan; Tran, Bao-Viet; Vu, Thai-Son

doi:10.1016/j.cscm.2022.e00946

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…In equation ( 4), ℓ represents a regularized length that describes the thickness of the smeared crack and is also an internal parameter affecting the critical stress for crack initiation. In equation ( 5), elastic strain is decomposed into extensive ε + and compressive ε − parts (for further details, refer to the formulas in Nguyen et al [23,24]). Applying the principle of maximum dissipation and energy minimization to equation ( 2) yields the set of coupled equations to be solved in the domain Ω associated with the structure, with boundary ∂Ω and outward normal n, to determine d (x) and u (x), for all positions denoted by the vector x in Ω:…”

Section: Fundamental Concept Of the Phase Field Methodsmentioning

confidence: 99%

Deep artificial neural network-powered phase field model for predicting damage characteristic in brittle composite under varying configurations

Nguyen,

Le,

Tran

et al. 2024

Mach. Learn.: Sci. Technol.

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This work introduces a novel artificial neural network-powered phase field model, offering rapid and precise predictions of fracture propagation in brittle materials. To improve the capabilities of the ANN model, we incorporate a loop of conditions into its core to regulate the Absolute Percentage Error for each observation point, that filters and consistently selects the most accurate outcome. This algorithm enables our model to better adapt to the highly sensitive validation data arising from varying configurations. The effectiveness of the approach is illustrated through three examples involving changes in the microgeometry and material properties of steel fiber-reinforced high-strength concrete structures. Indeed, the predicted outcomes from the improved ANN phase field model in terms of stress-strain relationship, and crack propagation path demonstrates an outperformance compared with that based on the Extreme Gradient Boosting method (XGB), a leading regression machine learning technique for tabular data. Additionally, the introduced model exhibits a remarkable speed advantage, being 180 times faster than traditional phase field simulations, and provides results at nearly any fiber location, demonstrating superiority over the phase field model. This study marks a significant advancement in the application of artificial intelligence for accurately predicting crack propagation paths in composite materials, particularly in cases involving the relative positioning of the fiber and initial crack location.

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Section: Fundamental Concept Of the Phase Field Methodsmentioning

confidence: 99%

Deep artificial neural network-powered phase field model for predicting damage characteristic in brittle composite under varying configurations

Nguyen,

Le,

Tran

et al. 2024

Mach. Learn.: Sci. Technol.

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show abstract

“…Furthermore, when changes occur in actual engineering conditions, extensive laboratory tests are required to re-evaluate the hydromechanical properties of materials, causing significant financial and labor investments. In light of this deficiency, a variety of alternative methods have been employed to evaluate the mechanical strength and infiltration properties of pervious concrete, including the random lattice discrete particle method 37 , discrete element method (DEM) [38][39][40] , finite element method (FEM) 41,42 , computational fluid dynamics (CFD) method 43 , and other self-developed numerical simulation methods [44][45][46] . The aim of these methods is to reduce the expenses associated with laboratory tests and expedite the material design process.…”

mentioning

confidence: 99%

Kriging-based surrogate data-enriching artificial neural network prediction of strength and permeability of permeable cement-stabilized base

Wang,

Xiao,

et al. 2024

Nat Commun

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Limited test data hinder the accurate prediction of mechanical strength and permeability of permeable cement-stabilized base materials (PCBM). Here we show a kriging-based surrogate model assisted artificial neural network (KS-ANN) framework that integrates laboratory testing, mathematical modeling, and machine learning. A statistical distribution model was established from limited test data to enrich the dataset through the combination of markov chain monte carlo simulation and kriging-based surrogate modeling. Subsequently, an artificial neural network (ANN) model was trained using the enriched dataset. The results demonstrate that the well-trained KS-ANN model effectively captures the actual data distribution characteristics. The accurate prediction of the mechanical strength and permeability of PCBM under the constraint of limited data validates the effectiveness of the proposed framework. As compared to traditional ANN models, the KS-ANN model improves the prediction accuracy of PCBM’s mechanical strength by 21%. Based on the accurate prediction of PCBM’s mechanical strength and permeability by the KS-ANN model, an optimization function was developed to determine the optimal cement content and compaction force range of PCBM, enabling it to concurrently satisfy the requirements of mechanical strength and permeability. This study provides a cost-effective and rapid solution for evaluating the performance and optimizing the design of PCBM and similar materials.

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Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods

Hùng

Seo

et al. 2022

KSCE J Civ Eng

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Numerical approach to predict the flexural damage behavior of pervious concrete

Cited by 5 publications

References 33 publications

Deep artificial neural network-powered phase field model for predicting damage characteristic in brittle composite under varying configurations

Deep artificial neural network-powered phase field model for predicting damage characteristic in brittle composite under varying configurations

Kriging-based surrogate data-enriching artificial neural network prediction of strength and permeability of permeable cement-stabilized base

Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods

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