Inverse identification of hyperelastic constitutive parameters of skeletal muscles via optimization of AI techniques

Yang, Li; Sang, Jianbing; Wei, Xinyu; Yu, Wenying; Tian, Weichang; Liu, G R

doi:10.1080/10255842.2021.1906235

Cited by 13 publications

(2 citation statements)

References 40 publications

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“…The KNN model utilizes the nearest neighbor set for analysis and is often used for handling parameters with unknown probabilities that are difficult to estimate. This includes classifiers for classifying individual data points into their nearest neighbor categories and regression for predictive processes [48,52].…”

Section: Optimizing Park Service Levels: the Gwo-knn Modelmentioning

confidence: 99%

Demand-Led Optimization of Urban Park Services

Tong,

Qian,

et al. 2023

Forests

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As the demand for cultural and recreational services grows, the mismatch between the supply and demand of park services significantly affects residents’ well-being. Optimizing the spatial layout of park services is a focal point of urban park and green space research. Taking Hangzhou, Zhejiang Province, as a case study, this research analyzes the spatial patterns and balance of park service supply and demand. Utilizing the Grey Wolf Optimization Model optimized by the K-Nearest Neighbor Model (GWO-KNN), this study proposes construction objectives for optimizing park services. The results indicate the following: (1) significant differences exist in the park service demands of residents in different residential environments; (2) there is a noticeable spatial disparity in park service supply among various residential areas with an overall positive correlation between park service supply levels and resident demands, yet an imbalance exists; (3) this study categorizes spatial types into low-service coordination, high-service coordination, low-service imbalance, and high-service imbalance; (4) the GWO-KNN Model is applied with optimization objectives being the innovative aspect of this study. Strategies for each park category are proposed: emphasizing suburban park construction by utilizing surrounding green resources and adding diverse facilities; introducing facilities friendly to vulnerable groups to meet the needs of diverse populations; enhancing the complementary advantages between “new” and “old” cities by moderately increasing park sizes and improving cultural and facility development levels; optimizing spatial structure with limited land resources to construct an urban park network system. This study aims to provide theoretical and technical support for optimizing urban park and green space systems.

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Section: Optimizing Park Service Levels: the Gwo-knn Modelmentioning

confidence: 99%

Demand-Led Optimization of Urban Park Services

Tong,

Qian,

et al. 2023

Forests

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show abstract

“…At the present stage, the common known approaches for determining hyperelastic material parameters, namely the strain energy density function coefficient, mainly include experiments [ 19 , 20 , 21 ], numerical calculation [ 22 , 23 ] and artificial intelligence methods [ 24 , 25 ]. In particular, artificial intelligence methods can predict the related parameters, which cannot be obtained directly or are difficult to obtain through experiment and simulation, and have received widespread attention in recent years.…”

Section: Introductionmentioning

confidence: 99%

Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network

Hou

Zhang

et al. 2022

Materials

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In this study, we present a systematic scheme to identify the material parameters in constitutive model of hyperelastic materials such as rubber. This approach is proposed based on the combined use of general regression neural network, experimental data and finite element analysis. In detail, the finite element analysis is carried out to provide the learning samples of GRNN model, while the results observed from the uniaxial tensile test is set as the target value of GRNN model. A problem involving parameters identification of silicone rubber material is described for validation. The results show that the proposed GRNN-based approach has the characteristics of high universality and good precision, and can be extended to parameters identification of complex rubber-like hyperelastic material constitutive.

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A parameter identification scheme of the visco-hyperelastic constitutive model of rubber-like materials based on general regression neural network

Chen

Wang

et al. 2023

Arch Appl Mech

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Inverse identification of hyperelastic constitutive parameters of skeletal muscles via optimization of AI techniques

Cited by 13 publications

References 40 publications

Demand-Led Optimization of Urban Park Services

Demand-Led Optimization of Urban Park Services

Parameters Identification of Rubber-like Hyperelastic Material Based on General Regression Neural Network

A parameter identification scheme of the visco-hyperelastic constitutive model of rubber-like materials based on general regression neural network

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