Determine OWA operator weights using kernel density estimation

Some subjective methods should divide input values into local clusters before determining the ordered weighted averaging (OWA) operator weights based on the data distribution characteristics of input values. However, the process of clustering input values is complex. In this paper, a novel probability density based OWA (PDOWA) operator is put forward based on the data distribution characteristics of input values. To capture the local cluster structures of input values, the kernel density estimation (KDE) is used to estimate the probability density function (PDF), which fits to the input values. The derived PDF contains the density information of input values, which reflects the importance of input values. Therefore, the input values with high probability densities (PDs) should be assigned with large weights, while the ones with low PDs should be assigned with small weights. Afterwards, the desirable properties of the proposed PDOWA operator are investigated. Finally, the proposed PDOWA operator is applied to handle the multicriteria decision making problem concerning the evaluation of smart phones and it is compared with some existing OWA operators. The comparative analysis shows that the proposed PDOWA operator is simpler and more efficient than the existing OWA operator

Biaxial creep test study on the influence of structural anisotropy on rheological behaviour of hard rock

Rheological characteristics are one of most important properties needed to be considered for the designing and construction for the long term stability and serviceability of underground structures in the rock mass. Up to date, although extensive studies on the rheological properties of rocks are available in the literature, most of existing studies reported the strain-time data for the axial deformation through compression rheological method and did not mention the lateral deformation, and mainly focused on the soft rocks at shallow depth. Thus, very limited attention has been paid to the rheological properties of deep and hard rock, neglecting the effects of structural anisotropy on the rheological properties. This paper presents a comprehensive in-depth study on the rheological behaviours of super-deep hard rock considering the effects of structural anisotropy by using the uniaxial and biaxial creep tests. The results revealed that significant creep behaviour can be observed in the hard rock specimens under high stress in the in-situ conditions, and the strain-time behaviour of hard rock exhibited brittle failure. The strain-time curves of hard rock exhibited two obvious phases of instantaneous creep and steady state creep without the phase of accelerated creep. Moreover, it was observed that the rheological behaviours, including the instantaneous modulus, transient creep duration, axial and lateral creep deformations, steady state creep rate, volumetric strain and contraction ratio are strongly affected by the structural anisotropy. Based on the experimental data, empirical models of the parameters governing creep behaviour have been established

A Novel Strength Model for Cement Marine Clay Based on the Mechanical-Chemical Coupling Behavior

Crucial mechanical-chemical (MC) interactions occur during the cement hydration process in cement marine clay; however, the role of such an important element of the resulting strength has been subject to less investigation, particularly from the theoretical perspective. To overcome this scientific gap, an efficient strength-based model accounting for the coupled MC processes is proposed here. Based on the analysis of the cement hydration mechanism, the porosity was chosen as the main factor to characterize the influence of the MC interactions on the overall response. To verify the accuracy of the MC model, the unconfined compressive strength (UCS) experiment was conducted for the cement marine clay samples, and the corresponding simulation model was constructed using COMSOL multiphysics®. In addition, a comparison between the predicted results by the existing three strength models and the proposed MC model was performed. Subsequently, the sensitivity analysis and identification of mechanical parameters were carefully carried out. The obtained results show that the UCS strength for Taizhou clay ranges from 10.21 kPa to 354.2 kPa as the cement content increases from 10% to 20%, and the curing time varies from 3 days to 28 days. The mechanical parameters in the MC model can be obtained according to the porosity level. A reasonably good agreement between the UCS strength results of simulations and the experimentally observed data is reported. Additionally, the predicted UCS strength results by the MC model demonstrate the best correspondence with the measured values, indicating the high efficacy of the established model

Multiattribute Group Decision-Making Based on Linguistic Pythagorean Fuzzy Interaction Partitioned Bonferroni Mean Aggregation Operators

The partitioned Bonferroni mean (PBM) operator can efficiently aggregate inputs, which are divided into parts based on their interrelationships. To date, it has not been used to aggregate linguistic Pythagorean fuzzy numbers (LPFNs). In this paper, we extend the PBM operator and partitioned geometric Bonferroni mean (PGBM) operator to the linguistic Pythagorean fuzzy sets (LPFSs) and use them to develop a novel multiattribute group decision-making model under the linguistic Pythagorean fuzzy environment. We first define some novel operational laws for LPFNs, which take into consideration the interactions between the membership degree (MD) and nonmembership degree (NMD) from two different LPFNs. Based on these novel operational laws, we put forward the interaction PBM (LPFIPBM) operator, the weighted interaction PBM (LPFWIPBM) operator, the interaction PGBM (LPFIPGBM) operator, and the weighted interaction PGBM (LPFWIPGBM) operator. Then, we study some properties of these proposed operators and discuss their special cases. Based on the proposed LPFWIPBM and LPFWIPGBM operators, a novel multiattribute group decision-making model is developed to process the linguistic Pythagorean fuzzy information. Finally, some illustrative examples are introduced to compare our proposed methods with the existing ones

The Influence of Geological Conditions in the Hangzhou Bay Area on the Deformation Behavior of Deep Excavations

The deformation behavior of deep excavations is affected by many factors, among which the geological conditions are greatly affected. Hangzhou Bay is affected by marine siltation and river alluvium, and the geological conditions within the urban area of Hangzhou are quite different. In this paper, the geological and deformation data of 79 deep excavation cases in the Hangzhou urban area were collected, and the statistical analysis showed that the deformation control of excavations in the silt area was poor. The average maximum lateral wall displacement of deep excavations of the Hangzhou urban area was 0.41%H (H was the depth of the excavation), the average value of the alluvial area was 0.22%H, and the average value of the silted area was 0.55%H. The influence of geological conditions, wall type, and construction period on the deformation of excavations was compared, and the deformation behavior of excavations in the silted area was clearly affected by various factors

Relating Extra Connectivity and Extra Conditional Diagnosability in Regular Networks

The h-extra node-connectivity of a graph G is the size of a minimal node-set, whose removal will disconnect G, but each remaining component has no fewer h + 1 nodes. Based on h-extra node-connectivity, the h-extra conditional fault-diagnosability of networks has been proposed for a better, more realistic measure of networks\u27 fault-tolerability. It is the maximal x such that G is h-extra conditionally x-fault-diagnosable. This paper will establish a relationship between the h-extra node-connectivity and h-extra conditional fault-diagnosability for a regular graph G, under the classic PMC diagnostic model. We will apply the newly found relationship to a variety of well-known regular networks, to directly obtain their h-extra conditional fault-diagnosability. The significance of the paper\u27s work is that it relates the notions of h-extra node-connectivity and h-extra conditional fault-diagnosability, so that a regular network\u27s h-extra conditional fault-diagnosability may be known once its h-extra node-connectivity is known

Use of Modified Lignocellulosic Butanol Residue in Phenol-Resorcinol-Formaldehyde Polymers

Lignocellulosic butanol residue (BR), obtained as the by-product of lignocellulosic butanol production, was used for the preparation of lignin-based phenol-resorcinol-formaldehyde resins (LPRFRs) by condensation polymerization. The lignin was first phenolated under sodium hydroxide catalysis at 90 to 92 °C at various phenolation times (1.0 to 4.0 h). The structural differences between BR and phenolated BR (PBR) were studied using Fourier transform infrared (FT-IR) spectroscopy, ultraviolet (UV) spectroscopy, thermogravimetric analysis (TGA), and gel permeation chromatography (GPC). The BR phenolated for 3.0 h had high phenol hydroxyl content, low molecular weight, and good thermal stability. The LPRFRs with 30 wt.% BR had the lowest free formaldehyde and phenol. With the substitution of BR for phenol, the hydrophilicity of LPRFRs increased. In addition, the mechanical, fragility, thermal properties, and morphology of lignin-phenol-resorcinol-formaldehyde foams (LPRFFs) were also investigated. The LPRFFs had excellent comprehensive properties when 30 wt.% PBR was substituted for phenol. These experimental findings could provide a new avenue for further study and application of bio-phenol-resorcinol foams