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Anti-epidemic Emergency Projects (AEEPs) have unique characteristics such as a short construction period, high-quality requirements, complex construction environment, many construction participants and many uncertain affecting factors. The purpose of this paper was to propose the establishment method for the Construction Community (CC) of AEEPs (CC-AEEPs) by analyzing the management features of AEEPs, to establish the method of the Social Network Analysis (SNA) for CC-AEEPs, and to apply this method and the framework to Wuhan Huoshenshan Hospital for their verification.

According to the CC theory, this paper explored the member composition and the establishment method for CC-AEEPs. The optimal management factors of CC-AEEPs were proposed by combining the management features of AEEPs and the SNA method for CC-AEEPs was further established. Finally, the applicability of the method was verified through a case study, and some countermeasures for the CC-AEEP social networks were proposed.

The establishment of CC-AEEPs is an important guarantee to complete AEEPs with top speed and high quality. Ten types of CC-AEEP members all played different but irreplaceable roles in cooperative construction, among which the Government, the Contractor, and the Supervisor had outstanding performances. The SNA method could effectively analyze the complexity and cooperative relationship among the members in four aspects. The case study of Huoshenshan Hospital validated the important role of CC-AEEP and its social network in the AEEP research providing beneficial enlightenment for the cooperative optimization path of the AEEP construction participants.

The new establishment method for CC-AEEPs was proposed from the perspective of “cooperation among human, society, and engineering” according to the theories of the engineering sociology and the CC. In this paper, the SNA method was applied to the research on the AEEP construction for the first time and the SNA method for CC-AEEPs was purposed. The optimal management factors of CC-AEEPs and the expansion path of the CC-AEEP social networks were proposed according to the whole-process tracking of AEEPs in Wuhan.

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

Currently, waste is regarded as a symptom of inefficiency. The generation of waste is a human activity, not a natural one. Currently, landfilling and incinerating wastes are common waste management techniques; but the use of these methods, in addition to wasting raw materials, causes damage to the environment, water, soil, and air. In the new concept of “Zero Waste” (ZW), waste is considered a valuable resource. A vital component of the methodology includes creating and managing items and procedures that limit the waste volume and toxicity and preserve and recover all resources rather than burning or burying them. With ZW, the end of one product becomes the beginning of another, unlike a linear system where waste is generated from product consumption. A scientific treatment technique, resource recovery, and reverse logistics may enable the waste from one product to become raw material for another, regardless of whether it is municipal, industrial, agricultural, biomedical, construction, or demolition. This chapter discusses the concept of zero landfills and zero waste and related initiatives and ideas; it also looks at potential obstacles to put the ZW concept into reality. Several methods are presented to investigate and evaluate efficient resource utilization for maximum recycling efficiency, economic improvement through resource minimization, and mandatory refuse collection. One of the most practical and used approaches is the Life Cycle Assessment (LCA) approach, which is based on green engineering and the cradle-to-cradle principle; the LCA technique is used in most current research, allowing for a complete investigation of possible environmental repercussions. This approach considers the entire life cycle of a product, including the origin of raw materials, manufacturing, transportation, usage, and final disposal, or recycling. Using a life cycle perspective, all stakeholders (product designers, service providers, political and legislative agencies, and consumers) may make environmentally sound and long-term decisions.

This paper attempts to investigate the effect of corruption on the real and accrual earnings management of target firms in the process of mergers and acquisitions.

The sample includes target firms from the European area that participate in mergers or acquisitions announced during 2010–2020. The preliminary empirical part estimates the level of earnings management during the period two years before the deal's announcement to identify whether the sample follows the manipulation behavior that the literature suggests for target firms. The primary empirical analysis focuses on the impact of corruption on real and accrual-based earnings management proxies, employing regression models and two alternative proxies for corruption. The existing literature points out that the combination of low levels of corruption and an integrated legal system reduces earnings manipulation.

The findings provide strong evidence for systematic downwards accounting manipulation practices, whereas the findings for real earnings management are not significant. The findings of the main empirical part show that corruption is positively associated with accrual-based manipulation and negatively related to real earnings management. In essence, in economies with a high level of transparency, managers adopt the manipulation of operating activities as a less detectable practice of earnings management instead of engaging in accounting procedures.

This study contributes to the literature highlighting the diversification of these firms' manipulation strategies according to the national level's corruption status.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.

Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

Mixed methods research is useful to enhance theoretical and practical research contributions. However, single methods have predominated much logistics and supply chain management (LSCM) research. This paper presents a review of mixed methods research across ten years in LSCM to determine their usage, identify benefits and inhibitors, and provide suggestions for LSCM researchers to realise the benefits from using mixed methods.

This paper adopts a mixed methods approach through a quantitative analysis of methods used in six leading LSCM journals, an e-mail survey of mixed methods article authors during the review period, and four published case studies that used mixed methods.

Only 144 (ten percent) of all empirical articles were published using mixed methods during the review period. A range of benefits and inhibitors regarding mixed methods adoption were found. Suggestions for LSCM authors include research training in mixed methods use and developing a project-specific research design due to the specificity and complexity associated with mixed methods research.

LSCM is at a critical juncture, shaped by new contexts, themes and challenges, and would benefit from different research approaches and methods. This paper contributes to the LSCM domain through analysing the current state, benefits and inhibitors of mixed methods research in LSCM journals to provide a renewed call to action and guidelines for mixed methods LSCM research, and suggesting research design adaptation to enable agile and resilient research when investigating rapidly changing and complex phenomena.

This research aims to systematically review studies on significant risks for Critical Infrastructure Projects (CIPs) from selected top-tier academic journals from 2011 to 2023.

In this research, a three-step systematic literature review methodology was employed to analyse 55 selected articles on Critical Infrastructure Risks (CIRs) from well-regarded and relevant academic journals published from 2011 to 2023.

The findings highlight a growing research focus on CIRs from 2011 to 2023. A total of 128 risks were identified and grouped into ten distinct categories: construction, cultural, environmental, financial, legal, management, market, political, safety and technical risks. In addition, literature reviews combined with questionnaire surveys were more frequently used to identify CIRs than any other method. Moreover, oil and gas projects were the subjects most often explored in the reviewed papers. Furthermore, it was observed that publications from Iran, the USA and China dominated CIRs research, making significant contributions, accounting for 49.65% of the analysed articles.

This research specifically focuses on five types of CIPs (i.e. roadways, bridges, water supply systems, dams and oil and gas projects). Other CIPs like cyber-physical systems or electric power systems, were not considered in this research.

Governments and contracting firms can benefit from the findings of this study by understanding the significant risks associated with the execution of CIPs, irrespective of the nation, industry or type of project. The results of this investigation can offer construction professionals valuable insights to formulate and implement risk response plans in the early stages of a project.

As a novel literature review related to CIRs, it lays the groundwork for future research and deepens the understanding of the multi-faceted effects of these risks, as well as sets practical response strategies.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

Although several studies have aimed to present models to predict conflict outcomes, fewer methods have been developed to analyze conflict manageability and provide management strategies based on prediction models. This research pitches into the manageability analysis of conflicts occur during the implementation of a proposed change in construction projects. In this way, a framework has been developed by defining two parameters: the predictability index and the preventability index.

Within this framework, the predictability index determines how many outcomes of the prediction model can be used for conflict management based on the degree of clarity. The preventability index demonstrates how preventive measures for conflict management can be identified. Eventually, three preventive measures can be determined: (1) identifying weaknesses of decision-making patterns and organizational culture, (2) identifying events that may be prevented using soft skills and (3) identifying differences among similar change-implementation scenarios and evaluating causes of the differences. To demonstrate the capabilities of proposed framework, a practical example has been analyzed.

The results show that the behavior of the project parties can be psychologically analyzed, and psychological conflicts can be distinguished from technical conflicts. Moreover, identifying the weaknesses of parties' decision-making patterns and their organizational culture is the most effective measure to prevent the conflicts.

This research contributes to the construction body of knowledge by quantifying the predictability and preventability of conflicts between the project parties in a construction project based on: (1) the certainty level of the conflict occurrence and (2) the level of alignment between predicted outcomes of the conflict occurrence and the issued change request and/or change order.