Search results

The outbreak of COVID-19 pandemic has posed severe challenges to infrastructure construction in China. Particularly, the complex technology and high process uncertainty of deep foundation pit construction make its safety risk identification a challenging issue of general concern. To address these challenges, Building Information Modeling (BIM) can be used as an important tool to enhance communication and decision-making among stakeholders during the pandemic. The purpose of this study is to propose a knowledge management and BIM-integrated safety risk identification method for deep foundation pit construction to improve the management efficiency of project participants.

This paper proposes a risk identification method that integrates BIM and knowledge management for deep foundation pit construction. In the framework of knowledge management, the topological relationships between objects in BIM are extracted and visualized in the form of knowledge mapping. After that, formal expressions of codes are established to realize the structured processing of specification provisions and special construction requirements. A comprehensive plug-in for deep foundation pit construction is designed based on the BIM software.

The proposed method was verified by taking a sub-project in deep foundation pit project construction as an example. The result showed the new method can make full use of the existing specification and special engineering requirements knowledge. In addition, the developed visual BIM plug-in proves the feasibility and applicability of the proposed method, which can help to increase the risk identification efficiency and refinement.

The deep foundation pit safety risk identification is challenged by the confusion of deep foundation pit construction safety knowledge and the complexity of the BIM model. By establishing the standardized expression of normative knowledge and special construction requirements, the efficiency and refinement of risk identification are improved while ensuring the comprehensiveness of results. Moreover, the topology-based risk identification method focuses on the project objects and their relations in the way of network, eliminating the problem of low efficiency from the direct BIM-based risk identification method due to massive data.

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.

The problem statement is how to identify and analyze the corporate risks involved in the relationships with external agents involved in the open product innovation process (OPIP)? Seeking to extend this investigation, the purpose of this paper is to analyze the enterprise risks identified in corporate relations with external agents of the OPIP. This study proposes the systematization of the process of identification and analysis of the enterprise risks involved in the process of open product innovation.

The case explored in this study is the OPIP of Volkswagen do Brasil (VWB), one of the most important subsidiaries of the Volkswagen Group. Criteria were selected to both assessing corporate relations with external agents of the open innovation of VWB and analyzing the enterprise risks identified in these relations. Data collection included interviews with management-level professionals engaged in the OPIP activities and technical visits to a VWB’s industrial plant.

Results demonstrate that the enterprise risks mostly affecting the OPIP have a critical impact on the manufacturing process and initial sales of the new product.

The originality of the study focuses on the proposal of a systematization of how to identify and analyze the corporate risks involved in the process of open product innovation. The study focuses on the theoretical frontier on the open innovation and enterprise risk management (ERM) in the open innovation process.

This research investigated Australian property valuers' identification and consideration of physical climate change risks in valuation practice.

Thirty Australian valuer members of the Australian Property Institute from a variety of specialisations were interviewed. The semi-structured interviews explored climate change risks and the extent of risk investigation and consideration in valuation practice. The analysis utilised the Moser and Luers (2008) climate risk preparedness framework as a lens to evaluate current valuation practice in Australia.

The analysis reflects that while physical risks are easily identified and engaged with by valuers, correspondingly, there is a lack of understanding of and engagement with, climate change risks. This supports the need for better information sources and guidance to inform valuers of climate change risks and the development of specific mechanisms for the consideration of such risks to be included in valuation processes, practices and reports.

The research was limited by its sample size and qualitative approach. Therefore, the research is not a representative opinion of the Australian profession; however, the analysis provides the perspective of a range of valuers from across Australia with different valuation specialisations.

This research has established that valuers have the potential to be prepared to address climate change in their professional capacity, as described by Moser and Luers (2008). However, they are constrained by information communication, access and detail and subsequent market awareness of information on climate change risk exposure on properties. There is a need for further support, guidance, information and tools, as well as awareness-raising, to enable valuers to accurately identify and reflect all risks affecting a property in the process of valuation.

This research provides the first investigation into the consideration of climate change in valuation practice. Property stakeholders—owners, investors, financiers and occupiers—are escalating their climate change risk analysis and reporting for property portfolios and organisations. This research suggests that valuers also need to be aware of the changing dynamics of market reporting and decision-making related to climate change risks to ensure appropriate reflection in valuation practice.

This research has been conducted with a view to creating a framework to integrate risk management based on building information modeling (BIM) information.

In this research, all the information related to the construction of a residential project including 3D, 4D and 5D BIM models and the execution and control phases information was collected, and the risk list was determined for each activity accordingly.

The present study has suggested a framework for risk management in order to optimize project changes.

The lack of integration between 3D, 4D and 5D modeling besides execution information is a fundamental problem in many projects. The gap between these two groups of information will lead to improper management and late decisions, eventually imposing unforeseen delays and cost overruns. Risk management by the means of adopting a new approach has been addressed in recent studies using new methods, such as BIM and its associated technologies, some of which were mentioned in the review of theoretical literature in this research.

The purpose is to identify and evaluate the safety risk factors in the coal mine construction process.

The text mining technique was applied in the stage of safety risk factor identification. The association rules method was used to obtain associations with safety risk factors. Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) were utilized to evaluate safety risk factors.

The results show that 18 safety risk factors are divided into 6 levels. There are 12 risk transmission paths in total. Meanwhile, unsafe behavior and equipment malfunction failure are the direct causes of accidents, and inadequate management system is the basic factor that determines the safety risk status.

Due to the limitation of the computational matrix workload, this article only categorizes numerous lexical items into 18 factors. Then, the workshop relied on a limited number of experts; thus, the findings may be potentially biased. Next, the accident report lacks a universal standard for compilation, and the use of text mining technique may be further optimized. Finally, since the data are all from China, subsequent cross-country studies should be considered.

The results can help China coal mine project managers to have a clear understanding of safety risks, efficiently carry out risk hazard identification work and take timely measures to cut off the path of transmission with risks identified in this study. This helps reduce the economic losses of coal mining enterprises, thus improving the safety standards of the entire coal mining industry and the national standards for coal mine safety policy formulation.

Coal mine construction projects are characterized by complexity and difficulties in construction. Current research on the identification and assessment of safety risk factors in coal mine construction is insufficient. This study combines objective and systematic research approaches. The findings contribute to the safety risk management of China coal mine construction projects by providing a basis for the development of safety measures.

The current research aimed to investigate the external enablement role of Digital Infrastructures (DI) in the interplay of entrepreneurial cognitions and innovation.

Data from the Global Entrepreneurship Monitor (GEM) and Digital Economy and Society Index (DESI) were used for analyses. This yielded a sample of 8,601 Generation Z entrepreneurs operating in 25 European countries.

Applying hierarchical moderated regressions showed that socio-cognitive components of an entrepreneurial mindset (self-efficacy, risk propensity, opportunity identification) affect innovation among Generation Z entrepreneurs. More importantly, DI plays an external enablement role in the interplay of cognitions and innovation among Generation Z entrepreneurs.

This study contributes to the socio-cognitive theory of entrepreneurship by integrating an external enablement perspective into the study of cognitions and entrepreneurial outcomes (here, innovation). It contributes to the digital technology perspective of entrepreneurship by connecting the conversation about the socio-cognitive perspective of entrepreneurship regarding the role of cognitions in innovation to the conversation in information systems (IS) regarding technology affordances and constraints. This study extends the application of the external enabler framework to the post-entry stage of entrepreneurial activity and integrates a generational perspective into it.

Managing supply chain risk is a crucial element in ensuring the long-term sustainability of any organization or industry. As such, identification of risks and deploying their mitigation strategies should be the focal point to sustain in the long run. The risks that are faced by food processing supply chains are gaining prominence, given more consumers requiring higher quality products while ensuring traceability. In essence, this research focuses on the supply chain risks and mitigation strategies in the spice industry of an emerging economy, Sri Lanka.

This paper integrates two popular multi-criteria decision-making (MCDM) techniques, such as the analytical hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the supply chain risks and to derive their mitigation strategies for the spice industry.

Findings show that “inability to meet quality requirements” has been established as the most significant risk in the Sri Lankan spice industry. On the other hand, “vertical integration” (backward integration) has been discovered as the key mitigation strategy to ameliorate the effects of supply chain risks in this sector.

This study is exploratory, and more empirical data and statistical analyses are needed to further validate the outcomes of the study.

Despite being one of the largest trade exporters in Sri Lanka, the spice industry gets scant attention to the identification and mitigation of the risks. The authors explored the supply chain risks in the spice industry and then prioritized the suitable mitigation strategies using an integrated AHP-TOPSIS method.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

A comprehensive understanding of the determining factors and implications of the frameworks for appreciating the relationships between climate risks and cultural heritage remains deficient. To address the gap, the review analysed literature on the management of climate risk in cultural heritage. The review examines the strengths and weaknesses of climate risk management (CRM) frameworks and attendant implications for the conservation of cultural heritage.

The study adopted a two-phased systematic review procedure. In the first phase, the authors reviewed related publications published between 2017 and 2021 in Scopus and Google Scholar. Key reports published by organisations such as the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and International Council on Monuments and Sites (ICOMOS) were identified and included in Phase Two to further understand approaches to CRM in cultural heritage.

Results established the changes in trend and interactions between factors influencing the adoption of CRM frameworks, including methods and tools for CRM. There is also increasing interest in adopting quantitative and qualitative methods using highly technical equipment and software to assess climate risks to cultural heritage assets. However, climate risk information is largely collected at the national and regional levels rather than at the cultural heritage asset.

The review establishes increasing implementation of CRM frameworks across national boundaries at place level using high-level technical skills and knowledge, which are rare amongst local organisations and professionals involved in cultural heritage management.

The review established the need for multi-sectoral, bottom-up and place-based approaches to improve the identification of climate risks and decision-making processes for climate change adaptation.