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The purpose of this paper is to present a new special element model for thermal analysis of composites.

A hybrid finite element formulation taking the fundamental solution as kernel function is presented in this work for analyzing the thermal behavior and predicting the effective thermal conductivity of fiber‐reinforced composites. A representative volume cell containing single or multiple fibers (or inclusions) is considered to investigate the overall temperature distribution affected by the inclusions and the interactions among them, and to evaluate the effective thermal conductivity of the composites using the presented algorithm with special‐purpose inclusion elements. Numerical examples are presented to demonstrate the accuracy and applicability of the proposed method in analyzing fiber‐reinforced composites.

The independent intra‐element field and frame field, as well as the newly‐developed hybrid functional, make the algorithm versatile in terms of element construction, with the result that the related variational functional involves the element boundary integral only. All numerical results are compared with the solutions from ABAQUS and good agreement is observed for all cases, clearly demonstrating the potential applications of the proposed approach to large‐scale modeling of fiber‐reinforced composites. The usage of special inclusion element can significantly reduce model meshing effort and computing cost, and simultaneously avoid mesh regeneration when the fiber volume fraction is changed.

Due to the fact that the established special elements exactly satisfy the interaction of matrix and fiber within the element, only element boundary integrals are involved, thus the algorithm can significantly reduce modeling effort and computing cost with less elements, and simultaneously avoid mesh regeneration when the fiber volume fraction is changed.

Based on the special fundamental solution, a newly‐constructed inclusion element is applied to a number of test problems involving unit RVCs with multiple fibers to access the accuracy of the model. The effective thermal conductivity of the composites is evaluated for cases of single and multiple fibers using the average temperatures at certain points on a data‐collection surface. A new algorithm for evaluating effective properties with special elements is presented.

The purpose of this paper is to develop a hybrid‐Trefftz (HT) finite element model (FEM) for simulating heat conduction in nonlinear functionally graded materials (FGMs) which can effectively handle continuously varying properties within an element.

In the proposed model, a T‐complete set of homogeneous solutions is first derived and used to represent the intra‐element temperature fields. As a result, the graded properties of the FGMs are naturally reflected by using the newly developed Trefftz functions (T‐complete functions in some literature) to model the intra‐element fields. The derivation of the Trefftz functions is carried out by means of the well‐known Kirchhoff transformation in conjunction with various variable transformations.

The study shows that, in contrast to the conventional FEM, the HT‐FEM is an accurate numerical scheme for FGMs in terms of the number of unknowns and is insensitive to mesh distortion. The method also performs very well in terms of numerical accuracy and can converge to the analytical solution when the number of elements is increased.

The value of this paper is twofold: a T‐complete set of homogeneous solutions for nonlinear FMGs has been derived and used to represent the intra‐element temperature; and the corresponding variational functional and the associated algorithm has been constructed.

The purpose of this paper is to develop a heuristic method for topology optimization of a continuum with bi-modulus material which is frequently occurred in practical engineering.

The essentials of this model are as follows: First, the original bi-modulus is replaced with two isotropic materials to simplify structural analysis. Second, the stress filed is adopted to calculate the effective strain energy densities (SED) of elements. Third, a floating reference interval of SED is defined and updated by active constraint. Fourth, the elastic modulus of an element is updated according to its principal stresses. Final, the design variables are updated by comparing the local effective SEDs and the current reference interval of SED.

Numerical examples show that the ratio between the tension modulus and the compression modulus of the bi-modulus material in a structure has a significant effect on the final topology design, which is different from that in the same structure with isotropic material. In the optimal structure, it can be found that the material points with the higher modulus are reserved as much as possible. When the ratio is far more than unity, the material can be considered as tension-only material. If the ratio is far less than unity, the material can be considered as compression-only material. As a result, the topology optimization of continuum structures with tension-only or compression-only materials can also be solved by the proposed method.

The value of this paper is twofold: the bi-modulus material layout optimization in a continuum can be solved by the method proposed in this paper, and the layout difference between the structure with bi-modulus material and the same structure but with isotropic material shows that traditional topology optimization result could not be suitable for a real bi-modulus layout design project.

Rotating machineries are widely used in manufacturing, petroleum, chemical, aircraft, and other industries. To accurately identify the operating conditions of such rotating machineries, this paper aims to propose a fault diagnosis method based on sensitive dimensionless parameters and particle swarm optimization (PSO)–support vector machine (SVM) for reducing the unexpected downtime and economic losses.

A relatively new hybrid intelligent fault classification approach is proposed by integrating multiple dimensionless parameters, the Fisher criterion and PSO–SVM. In terms of data pre-processing, a method based on wavelet packet decomposition (WPD), empirical mode decomposition (EMD) and dimensionless parameters is proposed for the extraction of the vibration signal features. The Fisher criterion is applied to reduce the redundant dimensionless parameters and search for the sensitive dimensionless parameters. Then, PSO is adapted to optimize the penalty parameter and kernel parameter for SVM. Finally, the sensitive dimensionless parameters are classified with the optimized model.

As two different time–frequency analysis methods, a method based on a combination of WPD and EMD used to extract multiple dimensionless parameters is presented. More vital diagnosis information can be obtained from the vibration signals than by only using a single time–frequency analysis method. Besides, a fault classification approach combining the sensitive dimensionless parameters and PSO-SVM classifier is proposed. The comparative experiment results show that the proposed method has a high classification accuracy and efficiency.

To the best of the authors’ knowledge, very few efforts have been performed for fault classification using multiple dimensionless parameters. In this paper, eighty dimensionless parameters have been studied intensively, which provides a new strategy in fault diagnosis field.

China is among one of the oldest civilizations in the world. The massive land mass of China also means that the Chinese people are subject to weather extremes as well as topographical variety in a country which cuts across alpine heights, treacherous deserts, lush valleys, dusty plains and lengthy rivers. With these weather extremes as the backdrop, it is crucial for the Chinese people to develop appropriate environmental control techniques for their dwellings as well as to ensure the structural integrity of their buildings. This paper discusses the protection, heating, anti‐seismic and dampness techniques developed and implemented in ancient China. It also documents the measures taken by the ancient Chinese to ensure the structural integrity of their buildings. The examples highlighted in this paper suggest that the building science principles adopted in ancient China remain relevant in the construction industry today.

The unique nature, complicated design, hazardous activities and complex work environment involved in the high-rise construction projects constitute significant risks worldwide. In the Indian context, construction safety management in high-rise construction projects is crucial due to the presence of significant occupational risks and hazards at the workplace. Occupational hazards lead to accidents that severely affect human health and result in substantial financial losses.

The study aims to present a hybrid risk assessment method (RAM) and the technique for order of preference by similarity to ideal solution (TOPSIS) method to detect and evaluate occupational risks in different construction activities through a questionnaire survey approach.

Aroundsix types of construction activities and corresponding ten risks are identified and evaluated during the study. Based on the calculation of risk scores, the findings imply that “roof work activities,” “finishing work,” “mechanical, electrical and plumbing work (MEP)” are hazardous construction activities, while, among the corresponding ten risks, “workers falling from height” is the most prominent risk among the majority of activities. Other risks include “risk due to fire and electric accidents” and “struck by falling objects,” which are the major risks in high-rise construction projects.

Theoriginality of the paper lies in its activity-based risk assessment and ranking of hazards in high-rise construction projects. By integrating theoretical insights with practical applications, the study attempts to enhance occupational safety and reduce accidents on construction sites, thereby significantly contributing to both academia and industry practices.

Environmental crises and resource shortage have been matters of concern worldwide particularly, as the beginning of the twenty-first century. Remanufacturing can be one of the best approaches to promoting the development of circular economy and realizing the efficient use of resources. However, research studies indicate that the general public still remains unaware of the merits of remanufactured products. Hence, investigating main motivations of consumers to purchase remanufactured products is certainly worth exploring. The purpose of this paper is to find out consumers’ key motivations to purchase remanufactured products. A new multi-attributes decision-making method based on prospect theory (PT) is conducted.

Firstly, according to the prior literature, 12 main factors that influence consumers to purchase a remanufactured product are selected. Then, the single valued triangular neutrosophic numbers and triangular neutrosophic weighted geometric averaging operator are applied to rank them. Secondly, a multi-attribute decision-making method with PT is used to evaluate the utilities of new and remanufactured products, respectively. Based on the utilities, consumers’ preferred choice between the new and remanufactured products can be elicited.

The proposed method has been applied to a real survey. The results demonstrate that the proposed method is not only capable of recognizing the consumers’ key motivations for purchasing a remanufactured product but also able to deal with the ranking of the new and remanufactured products.

The contributions of this paper are threefold. Firstly, this paper attempts to explore the main motivations that encourage consumers to purchase a remanufactured product, instead of a new one. Secondly, the proposed method is established based on PT, taking consumers’ psychological behavior into consideration, which can obtain a relatively reasonable result. Thirdly, it can enrich the remanufacturing literature, serving as a guideline for consumers when purchasing a remanufactured product. In addition, this paper can help the remanufacturers make pricing and production decisions where necessary.

The Chinese civilization is an important part of the history of mankind. The purpose of this paper is to show that there are project management lessons to be learned from Chinese history, including that relating to the management of the building process in ancient China.

Through a review of the literature, this paper discusses the key management and economic practices in the building process of ancient China and highlights these practices from an important document, the Yingzao Fashi or (“Treatise on Architectural Methods”), that was compared with the modern‐day project management framework.

This paper explains the official systems instituted for public projects; the management of labour, design and planning of construction works; quantity surveying practices; the use, control and recycling of building materials; and inspection of building elements in ancient China.

The study suggests that lessons in the principles of construction project management in ancient China bear many similarities with the nine areas of modern‐day project management body of knowledge relating to integration, scope, time, cost, quality, human resource, communications, risk, and procurement management. An area for future research would be to compare the Yingzao Fashi with modern‐day codes of practice for building works to determine which of its “ancient” provisions relating to quality management are still relevant today.

It was found that much emphasis was placed by the ancient Chinese on the quality aspects of prominent building projects. This is one facet from which modern‐day project managers and clients can draw lessons.

The diagnosis and prediction methods used for estimating the health conditions of the bearing are of great significance in modern petrochemical industries. This paper aims to discuss the accuracy and stability of improved empirical mode decomposition (EMD) algorithm in bearing fault diagnosis.

This paper adopts the improved adaptive complementary ensemble empirical mode decomposition (ICEEMD) to process the nonlinear and nonstationary signals. Two data sets including a multistage centrifugal fan data set from the laboratory and a motor bearing data set from the Case Western Reserve University are used to perform experiments. Furthermore, the proposed fault diagnosis method, combined with intelligent methods, is evaluated by using two data sets. The proposed method achieved accuracies of 99.62% and 99.17%. Through the experiment of two data, it can be seen that the proposed algorithm has excellent performance in the accuracy and stability of diagnosis.

According to the review papers, as one of the effective decomposition methods to deal with nonlinear nonstationary signals, the method based on EMD has been widely used in bearing fault diagnosis. However, EMD is often used to figure out the nonlinear nonstationarity of fault data, but the traditional EMD is prone to modal confusion, and the white noise in signal reconstruction is difficult to eliminate.

In this paper only the top three optimal intrinsic mode functions (IMFs) are selected, but IMFs with less correlation cannot completely deny their value. Considering the actual working conditions of petrochemical units, the feasibility of this method in compound fault diagnosis needs to be studied.

Different from traditional methods, ICEEMD not only does not need human intervention and setting but also improves the extraction efficiency of feature information. Then, it is combined with a data-driven approach to complete the data preprocessing, and further carries out the fault identification and classification with the optimized convolutional neural network.

Nanocrystalline LaB₆, NdB₆, Nd-doped LaB₆ and La-doped NdB₆ have been studied using the density functional theory (DFT) to study their electronic, optical and mechanical characteristics. The purpose of this paper is to address this issue.

Nanocrystalline LaB₆, NdB₆, Nd-doped LaB₆ and La-doped NdB₆ have been studied using the DFT to study their electronic, optical and mechanical characteristics. The calculated lattice constants of LaB₆, NdB₆, Nd-doped LaB₆ and La-doped NdB₆ were 4.157, 4.118, 4.267 and 4.449, respectively. The lattice constant of La₇Nd₁B₆ was increased when Nd is doped into LaB₆. B p comprised the uppermost valence bands (VBs), whereas B s comprised the lowermost conduction bands (CBs). The authors’ results showed that La doping reduced the work function of NdB₆ and increased its thermionic emission characteristics.

The authors’ results showed that La doping reduced the work function of NdB₆ and increased its thermionic emission characteristics.

The work function of LaB₆ was 2.7 eV, which is higher than that of La₁Nd₇B₆ (2.64 eV).