Search results

The purpose of this paper is to investigate the effects of different surface structures, dimensional parameters and cavitation models on the lubrication characteristics of water-lubricated journal bearings.

In this paper, the coupling iteration method of ANSYS and MATLAB is established to calculate the journal orbits of water-lubricated bearing, and the differences between the journal orbits of the smoothed and the textured water-lubricated bearings are compared and analyzed, and the effects of different bearing materials, L/D ratios and clearance ratios on the lubrication performance of water-lubricated bearings are investigated. The effects of different cavitation models on the static equilibrium position and whirling trajectory of water-lubricated bearings are compared.

The results show that when the surface texture is distributed in the upper bearing or the bearing elastic modulus decreases, the bearing stability increases. Considering shear cavitation and noncondensing gas, the rotor journal orbits amplitude decreases at high speed with low clearance ratio. A water film test rig for water-lubricated bearings is built to measure the full-circle water film pressure of water-lubricated journal bearings, and the experimental results are compared with the simulation results, which are in good agreement.

The findings provide a theoretical basis for optimizing the structure of water-lubricated bearings.

As the foundation of social and economic development, infrastructure development projects are characterized by large initial investment, high technical requirements and thus generally delivered through complex contractor–subcontractor collaboration chains. This study aims to characterize the complexity of collaborative networks between contractors and subcontractors for infrastructure development through comparing the structural characteristics and the formation mechanisms of contractor–subcontractor collaborative networks for the following two different types of infrastructure: public works (PWCN) owned and operated by government agencies, and public utilities (PUCN) owned and operated by nongovernment agencies.

Based on the method of stochastic actor-oriented models and the longitudinal dataset of National Quality Award Projects in China during 2001–2020, this study compares how the structural characteristics of project-based collaborative networks between contractors and subcontractors for the two types of projects are different and how related micro-mechanisms, including both structure-based endogenous network effects and attribute-based exogenous homophily effects (institutional, organizational and geographical homophily), collectively underpin the formation of the networks.

The empirical results provide evidence that while the two networks are both characterized by relatively low levels of network density, PWCN is more globally connected around a minority of superconnected contractors as compared with PUCN. The results further reveal that compared with PUCN, the formation of PWCN is more significantly related to the structure-based anti in-isolates effect, suggesting that PWCN is more open for new entrant subcontractors. With regard to the attribute-based homophily effects, the results provide evidence that while both significantly and positively related to the effects of organizational (same company group) and geographical homophily (same location), the formation of PWCN and PUCN is oppositely driven by the institutional homophily effect (same ownership type).

As an exploratory effort of using network perspective to investigate the formation mechanisms of contractor–subcontractor relationships in the infrastructure development domain, this study contributes to a network and self-organizing system view of how contractors select subcontractors in different types of infrastructure projects. The study also provides insights into how contractor–subcontractor collaborative relationships can be better manipulated to promote the development of complex infrastructure in different contexts.

The purpose of this paper is to develop a bi-objective optimization framework to select prefabricated construction service composition. An improved algorithm-genetic simulated annealing algorithm (GSA) is employed to demonstrate the application of the framework.

The weighted aggregate multi-dimensional collaborative relationship is used to quantitatively evaluate the synergistic effect. The quality of service is measured using the same method. The research proposed a service combination selection framework of prefabricated construction that comprehensively considers the quality of service and synergistic effect. The framework is demonstrated by using a GSA that can accept poor solutions with a certain probability. Furthermore, GSA is compared with the genetic algorithm (GA), simulated annealing algorithm (SA) and particle swarm optimization algorithm (PSO) to validate the performance.

The results indicated that GSA has the largest optimal fitness value and synergistic effect compared with other algorithms, and the convergence time and convergence iteration of the improved algorithm are generally at a low level.

The contribution of this study is that the proposed framework enables project managers to clarify the interactions of the prefabricated construction process and provides guidance for project collaborative management. In addition, GSA helps to improve the probability of successful collaboration between potential partners, therefore enhancing client satisfaction.

Prefabricated buildings (PBs) have proven to effectively mitigate carbon emissions in the construction industry. Existing studies have analyzed the environmental performance of PBs considering the shift in construction methods, ignoring the emissions abatement effects of the low-carbon practices adopted by participants in the prefabricated building supply chain (PBSC). Thus, it is challenging to exploit the environmental advantages of PBs. To further reveal the carbon reduction potential of PBs and assist participants in making low-carbon practice strategy decisions, this paper constructs a system dynamics (SD) model to explore the performance of PBSC in low-carbon practices.

This study adopts the SD approach to integrate the complex dynamic relationship between variables and explicitly considers the environmental and economic impacts of PBSC to explore the carbon emission reduction effects of low-carbon practices by enterprises under environmental policies from the supply chain perspective.

Results show that with the advance of prefabrication level, the carbon emissions from production and transportation processes increase, and the total carbon emissions of PBSC show an upward trend. Low-carbon practices of rational transportation route planning and carbon-reduction energy investment can effectively reduce carbon emissions with negative economic impacts on transportation enterprises. The application of sustainable materials in low-carbon practices is both economically and environmentally friendly. In addition, carbon tax does not always promote the implementation of low-carbon practices, and the improvement of enterprises' environmental awareness can further strengthen the effect of low-carbon practices.

This study dynamically assesses the carbon reduction effects of low-carbon practices in PBSC, informing the low-carbon decision-making of participants in building construction projects and guiding the government to formulate environmental policies.

To help decision-makers choose appropriate infrastructure project delivery systems (IPDS) and keep up with the construction industry’s rapid growth, this study aims to develop a goal optimization technique.This looks into team integration, large production and optimum sustainability. The suggested approach for meeting several infrastructure project objectives is flexible and expandable. This research overcomes the significant discrepancy between the construction industry’s progress and the rate at which project delivery methods evolve.

This study examined pertinent literature to choose an appropriate project delivery method and gave information on several elements that affect that decision. After optimization using a genetic algorithm (GA), a Pareto front of solutions has been found. The three construction goals of sustainability, mass production and team integration are all met by the chosen best solution. The four most popular possibilities for studying the suggested approach are five primary categories, each of which has 22 variables, and the weight of each variable was established using Simo’s procedure. This is optimized, demonstrating the accuracy of the optimization model.

Sustainability, mass production and team integration are the major goals of selecting the finest IPDS. The Pareto-optimal solutions discovered through analysis demonstrated that the created GA is reliable and generates solid outcomes. In fact, it enables decisions that were based on a single criterion to be overturned. The process has therefore demonstrated its efficacy in identifying the ideal answer. First integrated project delivery (IPD), second design-build (DB), third design-bid-build (DBB) and last construction manager at risk (CMR) are the best options. The weight of the aims function has found these rankings to be satisfactory.

The findings demonstrate that the suggested strategy can lead to optimization, providing the government with a wide range of options for attaining certain project objectives. The ability of this study to evaluate the combined effects of three objectives in choosing the best IPDS, the production of optimal selection solutions (IPDS), which can help with better decision-making when many objectives are present, and the flexibility and extendibility of the suggested approach for achieving priorities in infrastructure projects are what make it unique. This approach was able to select IPDS to meet developments in the construction project.

To confirm the validity of the GA, the factor of error was calculated, which is equal to 1.7599e-08.

We examine the association between conditional conservatism in initial public offering (IPO) underpricing and post-issue stock market survival in the U.S.

We adopt an archival approach by collecting data for 1,761 U.S. IPO issuers for the period 1990–2017. Regression analyses are conducted to evaluate the association between conditional conservatism in initial public offerings with underpricing and post-issue stock market survival. We identify firms that went public in the period 1990–2012. These firms are then followed for five years after the IPO to assess their stock market survival.

We find that pre-issue conditional conservatism is significantly associated with less IPO underpricing. We also detect that IPO firms with higher levels of conditional conservative reporting are more likely to survive in the post-IPO stock market in the three-, four-, and five-year periods after the IPO. Our main findings are robust after controlling for other factors in our models, such as IPO cycles, venture capitalists, research and development investment, and pre-IPO accounting performance.

We extend research by demonstrating that conditional conservative reporting practices help firms reduce their indirect costs of raising their initial public capital. Additionally, our research introduces new evidence on the association between pre-IPO conditional conservatism and after-issue stock market survival. Our findings empirically support the International Accounting Standards Board’s (IASB) decision to reintroduce the concept of prudence into the conceptual framework, by showing how conservative reporting can reduce information asymmetry in IPO firms.

The present study aims to identify significant barriers to adopting prefabricated construction (PFC) in developing economies using a study in Sri Lanka and develop an integrated strategy framework to mitigate and overcome the obstacles.

The research process included a comprehensive literature review, a pilot study, a questionnaire survey for data collection, statistical analysis and a qualitative content analysis.

Ranking method revealed that all 23 barriers were significant. Top significant barriers include challenges in prefabricated component transportation, high capital investment costs and lack of awareness of the benefits of PFC among owners/developers. Factor analysis clustered six barrier categories (BCs) that fit the barrier factors, explaining 71.22% of the cumulative variance. Fuzzy synthetic evaluation revealed that all BCs significantly influence PFC adoption in Sri Lanka. Finally, the proposed mitigation strategies were mapped with barriers to complete the integrated framework.

The study outcomes are relevant to construction industry stakeholders of Sri Lanka, who are keen to enhance construction efficiencies. The implications can also benefit construction industry stakeholders and policymakers to formulate policies and regulations and identify mitigation solutions.

The study provides deeper insights into the challenges to adopting prefabrication in South Asian countries such as Sri Lanka. Furthermore, the integrated framework is a novel contribution that can be used to derive actions to mitigate barriers in developing economies.

In recent decades, infrastructure has continued to develop as an important basis for social development and people's lives. Resource management of these large-scale projects has been immensely concerned because dozens of construction enterprises (CEs) often work together. In this situation, resource collaboration among enterprises has become a key measure to ensure project implementation. Thus, this study aims to propose a systematic multi-agent resource collaborative decision-making optimization model for large projects from a matching perspective.

The main contribution of this work was an advancement of the current research by: (1) generalizing the resource matching decision-making problem and quantifying the relationship between CEs. (2) Based on the matching domain, the resource input costs and benefits of each enterprise in the associated group were comprehensively analyzed to build the mathematical model, which also incorporated prospect theory to map more realistic decisions. (3) According to the influencing factors of resource decision-making, such as cost, benefit and attitude of decision-makers, determined the optimal resource input in different situations.

Numerical experiments were used to verify the effectiveness of the multi-agent resource matching decision (MARMD) method in this study. The results indicated that this model could provide guidance for optimal decision-making for each participating enterprise in the resource association group under different situations. And the results showed the psychological preference of decision-makers has an important influence on decision performance.

While the MARMD method has been proposed in this research, MARMD still has many limitations. A more detailed matching relationship between different resource types in CEs is still not fully analyzed, and relevant studies about more accurate parameters of decision-makers’ psychological preferences should be conducted in this area in the future.

Compared with traditional projects, large-scale engineering construction has the characteristics of huge resource consumption and more participants. While decision-makers can determine the matching relationship between related enterprises, this is ambiguous and the wider range will vary with more participants or complex environment. The MARMD method provided in this paper is an effective methodological tool with clearer decision-making positioning and stronger actual operability, which could provide references for large-scale project resource management.

Large-scale engineering is complex infrastructure projects that ensure national security, increase economic development, improve people's lives and promote social progress. During the implementation of large-scale projects, CEs realize value-added through resource exchange and integration. Studying the optimal collaborative decision of multi-agent resources from a matching perspective can realize the improvement of resource transformation efficiency and promote the development of large-scale engineering projects.

The current research on engineering resources decision-making lacks a matching relationship, which leads to unclear decision objectives, ambiguous decision processes and poor operability decision methods. To solve these issues, a novel approach was proposed to reveal the decision mechanism of multi-agent resource optimization in large-scale projects. This paper could bring inspiration to the research of large-scale project resource management.

This study evaluates the knowledge structure of building information modeling (BIM) in green buildings. Buildings are one of the main contributors to carbon emissions, and implementing BIM in green buildings is seen as an indispensable approach to mitigate environmental and climate change issues.

Through a bibliometric analysis, 297 publications retrieved from the Web of Science (WoS) were analyzed to explore their intellectual structure.

Bibliographic coupling analysis produced four clusters on current and emerging trends, while co-word analysis produced four clusters on future BIM and green building trends. Current and emerging trends revolve around BIM adoption in green and existing buildings, life cycle analysis (LCA) and sustainable rating tools. Future trends related to BIM and performance analysis and optimization, the BIM framework for green building design and construction, overcoming barriers and maximizing benefits in BIM adoption.

The implications of this study are relevant to all BIM and green building stakeholders, including developers, engineers, architects, occupants, tenants and the whole community.

This study examines the crucial integration of BIM and green building within the more extensive construction and building field scope.

Online shoppers feel insecure due to the various unethical practices of e-tailers. It is, therefore, crucial for online retailers to alleviate customer concerns. Extant literature indicates that country-of-origin (COO) cues influence consumer perception. A relatively underexplored phenomenon in an emerging market context, the COO image of the online retailer, i.e. a foreign-origin online retailer (FOOR) or an Indian-origin online retailer (IOOR), needs validation. The current study investigates customer expectations of online retailers' ethical behaviour against the backdrop of online retailer-based signals in emerging markets.

The researchers floated an online questionnaire using a seven-point Likert scale. The authors sought recipient responses in Google Forms shared via e-mails and social media connections. The authors analysed 1,018 useable responses using partial least square structural equation modelling (PLS-SEM) in Smart PLS 3.

The empirical study examined the influence of the consumer perception of ethics of online retailers (CPEOR) and COO on consumer purchase intention. It validated the proposed research model. The research findings inform that the CPEOR and the COO influence purchase intention through the mediation effects of trust and satisfaction. Results indicate that privacy, security, non-deception, fulfilment, customer service, FOOR and IOOR strongly predict consumer trust. In contrast, privacy, non-deception, fulfilment, customer service and FOOR strongly predict consumer satisfaction. However, security and IOOR did not influence consumer satisfaction.

The study results have theoretical and practical implications for academic researchers and online retailing managers. Future studies can validate the model in different geo-demographic scenarios and e-commerce settings.

The study enriches the extant literature on CPEOR in the Indian context. This study is pioneering work examining consumer purchase intention by adding the COO construct to the CPEOR model.