A balanced load mapping method based on radial basis functions and fuzzy sets

Biancolini, Marco Evangelos; Chiappa, Andrea; Giorgetti, F.; Groth, Corrado; Cella, Ubaldo; Salvini, P

doi:10.1002/nme.5850

Cited by 30 publications

(9 citation statements)

References 46 publications

(52 reference statements)

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“…This method is similar to IDW, but the fluctuation of predicated value. There is also a smoothening parameter to control the smoothness of the resulting surface (Biancolini et al 2018), and the optimal kernel parameter in the study area is (0.41).…”

Section: Radial Basis Function (Rbf)mentioning

confidence: 99%

Statistical analysis of the best GIS interpolation method for bearing capacity estimation in An-Najaf City, Iraq

et al. 2021

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The presence of an economical solution to predict soil behaviour is essential for new construction areas. This paper aims to investigate the ultimate interpolation method for predicting the soil bearing capacity of An-Najaf city-Iraq based on field investigation information. Firstly, the engineering bearing capacity was calculated based on the in-site N-SPT values using dynamic loading for 464 boreholes with depths of 0–2 m, using the Meyerhof formula. The data then were classified and imported to the GIS program to apply the interpolation methods. Four deterministic and two geostatistical interpolation methods were applied to produce six bearing capacity maps. The statistical analyses were performed using two methods: the common cross-validation method by the coefficient of determination (R2) and root mean square error (RMSE), where the results showed that ordinary kriging (OK) is the ultimate method with the least RMSE and highest R2. These results were confusing so, the backward elimination regression (BER) procedure was applied to gain the definite result. The results of BER show that among all the deterministic methods, the IDW is the optimal and most significant interpolation method. The result of geostatistical methods shows that EBK is the best method in our case than the OK method. BER also applied to all six methods and shows that IDW is the ultimate significant method. The results indicate no general ultimate interpolation method for all cases and datasets type; therefore, the statistical analyses must be performed for each case and dataset.

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Section: Radial Basis Function (Rbf)mentioning

confidence: 99%

Statistical analysis of the best GIS interpolation method for bearing capacity estimation in An-Najaf City, Iraq

et al. 2021

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“…With transient FSI simulations it is necessary to store both modal coordinates and relative derivatives. The update of modal coordinates is performed according to Equation (20), using a modal load assumed to be constant within each time step, the shape update is performed at the end of each time step adopting Equation (11).…”

Section: Setup Of Modal Fsi Analysismentioning

confidence: 99%

“…There is, for this reason, the need for numerical algorithms able to properly interpolate back and forth loads and displacements, with a proper equilibrium between accuracy and computational costs while respecting the requirements in terms of load balance when dealing with forces, precision and smoothness when dealing with displacements. Over the years, several mapping methods have been developed and are available in literature [9][10][11], such as the standard mortar method (SMM) [12] and the force reaction method [13]. Another drawback given by data transfer for partitioned approaches, when using commercial CSM and CFD solvers, is also given by the technical problem of exchanging information since, when using a closed source program offering limited degree of customization or scripting, transferring data by means of external files is often the only available option that can turn into a major bottleneck in terms of computational cost.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Functions Vector Fields Interpolation for Complex Fluid Structure Interaction Problems

Groth

Porziani

Biancolini

2021

Fluids

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Fluid structure interaction (FSI) is a complex phenomenon that in several applications cannot be neglected. Given its complexity and multi-disciplinarity the solution of FSI problems is difficult and time consuming, requiring not only the solution of the structural and fluid domains, but also the use of expensive numerical methods to couple the two physics and to properly update the numerical grid. Advanced mesh morphing can be used to embed into the fluid grid the vector fields resulting from structural calculations. The main advantage is that such embedding and the related computational costs occur only at initialization of the computation. A proper combination of embedded vector fields can be used to tackle steady and transient FSI problems by structural modes superposition, for the case of linear structures, or to impose a full non-linear displacement time history. Radial basis functions interpolation, a powerful and precise meshless tool, is used in this work to combine the vector fields and propagate their effect to the full fluid domain of interest. A review of industrial high fidelity FSI problems tackled by means of the proposed method and RBF is given for steady, transient, and non-linear transient FSI problems.

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“…26,27 Thus, the fuzzy set theory is put forward to deal with the fuzzy uncertainty, which is characterized by the membership function (MF) constructed from available expert opinions. 28,29 Under the framework of the fuzzy set theory, failure possibility (FP) is popular in quantifying the safety degree, which serves the lower bound of the fuzzy safety confidence level.…”

Section: Introductionmentioning

confidence: 99%

An efficient method for estimating time‐dependent failure possibility by combining adaptive Kriging with adaptive truncated fuzzy simulation

Wang

Lü

Feng

et al. 2021

Numerical Meth Engineering

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Time-dependent failure possibility (TDFP) can measure the structural safety level for a time interval of interest under fuzzy uncertainty, but its calculational cost is unaffordable by using fuzzy simulation (FS) due to a required large size of FS candidate sampling pool (CSP). Although time-dependent adaptive Kriging model (T-AK) combined with FS (T-AK-FS) was presented to reduce the number of calling performance function, a large FS CSP still makes training T-AK time-consuming. To improve its efficiency, an adaptive truncated FS (ATFS) with T-AK (T-AK-ATFS) is proposed by CSP size reduction approach. By T-AK-ATFS, the largest safety hypercube in fuzzy standard space is adaptively searched, in which the samples are in safety states and can be removed from the FS CSP. Moreover, T-AK is adaptively trained to search the largest safety hypercube and estimate TDFP simultaneously. In adaptively searching process, the FS CSP is divided into several sub-CSPs, on which training T-AK is more time-saving.

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A balanced load mapping method based on radial basis functions and fuzzy sets

Cited by 30 publications

References 46 publications

Statistical analysis of the best GIS interpolation method for bearing capacity estimation in An-Najaf City, Iraq

Statistical analysis of the best GIS interpolation method for bearing capacity estimation in An-Najaf City, Iraq

Radial Basis Functions Vector Fields Interpolation for Complex Fluid Structure Interaction Problems

An efficient method for estimating time‐dependent failure possibility by combining adaptive Kriging with adaptive truncated fuzzy simulation

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