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This paper introduces a novel method to improve building safety management by leveraging building information modeling (BIM) and adaptive information retrieval techniques. The integration aims to overcome the limitations of traditional safety management methods in connecting construction processes with risk management efficiently.

The proposed method involves developing industry foundation classes (IFC) ontologies and integrating them with a safety document ontology to form a comprehensive BIM-based safety context framework. Custom reasoning rules and an inference engine are constructed to enable automatic context-aware safety information retrieval. The methodology is demonstrated through an adaptive information retrieval system using job hazard analysis (JHA) documents.

The implementation of the BIM-based adaptive information retrieval system shows significant improvements in identifying and managing construction risks. By mapping job-specific risks to corresponding safety measures, the system enhances risk detection and management tailored to particular construction tasks. The results indicate a marked improvement in the precision and accuracy of safety assessments and recommendations, aligning them closely with planned construction activities and conditions.

This paper offers an innovative approach to construction safety management through the development of a BIM-facilitated context-aware information retrieval system. This approach provides a more intelligent and automated framework for identifying and managing risks in construction projects. By focusing on specific job steps and related risks, the system enhances the effectiveness and accuracy of safety measures, contributing to better overall building safety management.

The wavelet neural network (WNN) has the drawbacks of slow convergence speed and easy falling into local optima in data prediction. Although the artificial bee colony (ABC) algorithm has strong global optimization ability and fast convergence speed, it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.

This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model. Based on the example of the Jinan Yuhan underground tunnel project, the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed, and the analysis results are compared with the actual detection amount.

The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data, with a maximum relative error of only 4.73%. On this basis, the results show that the statistical features of ABC-WNN are the lowest, with the errors at 0.566 and 0.573, compared with the single back propagation (BP) neural network model and WNN model. Therefore, it can be derived that the ABC-WNN model has higher prediction accuracy, better computational stability and faster convergence speed for deformation.

This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels. This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multi-arch tunnels and small clearance tunnels. It can provide a new and effective way for deformation prediction in similar projects.

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

This paper aims to introduce a novel modularity-based framework, Com_Tracker, designed to detect and track community structures in dynamic social networks without recomputing them from scratch at each snapshot. Despite extensive research in this area, existing approaches either require repetitive computations or fail to capture key community behavioral events, both of which limit the ability to generate timely and actionable insights. Efficiently tracking community structures is crucial for real-time decision-making in rapidly evolving networks, while capturing behavioral events is necessary for understanding deeper community dynamics. This study addresses these limitations by proposing a more efficient and adaptive solution. It aims to answer the following questions: How can we efficiently track community structures without recomputation? How can we detect significant community events over time?

Com_Tracker models dynamic social networks as a sequence of snapshots. First, it detects the community structure of the initial snapshot using a static community detection algorithm. Then, for each subsequent time step, Com_Tracker updates the community structure based on the previous snapshot, allowing it to track communities and detect their changes over time. The locus-based adjacency encoding scheme is adopted, and Pearson’s correlation guides the construction of neighboring solutions.

Experiments conducted on various networks demonstrate that Com_Tracker effectively detects community structures and tracks their evolution in dynamic social networks. The results highlight its potential for real-time tracking and provide promising performance outcomes.

Com_Tracker offers valuable insights into community evolution, helping practitioners across fields such as resource management, public security, marketing and public health. By understanding how communities evolve, decision-makers can better allocate resources, enhance targeted strategies and predict future community behaviors, improving overall responsiveness to changes in network dynamics.

Com_Tracker addresses critical gaps in existing research by combining the strengths of modularity maximization with efficient tracking of community changes. Unlike previous methods that either recompute structures or fail to capture behavioral events, Com_Tracker provides an incremental, adaptive framework capable of detecting both community evolution and behavioral changes, enhancing real-world applicability in dynamic environments.

As technology aspect of agriculture becomes more and more important with the time to increase agricultural productivity in a sustainable and smart way, agriculture practices become more interdisciplinary. Furthermore, agricultural practices are affected by urban and rural planning enabling urban and rural farming. Architecture, engineering and construction (AEC) industry can support food security through the integration of agricultural practices and technologies into the built environment, its interior design, and greenhouses supporting urban and rural farming. Based on the literature review, this chapter aims to investigate ways for enhancing AEC industry’s and its professionals’ contribution to food security and sustainable agricultural practices. This chapter highlights roles of the AEC industry in enhancing food security and sustainable agricultural practices. This chapter emphasizes the importance of undergraduate and graduate curriculums of future AEC industry professionals (e.g., architects, interior architects, civil engineers) to equip them with the skills and knowledge of sustainable agricultural practices and technologies integrated greenhouses, built environment and indoor environment, and interior design. For this reason, agricultural policies need to cover food security-related interdisciplinary education and training (e.g., renewable energy-based agriculture integrated built environment) of AEC industry professionals. Agricultural policies need to be designed with the contribution of and considering AEC industry professionals as they are among the main stakeholders of food security and renewable energy-based agriculture-integrated built environment. Furthermore, this chapter highlights how AEC industry, in compliance with United Nations Sustainable Development Goals and countries sustainable and resilient development plans, can contribute to food security and sustainability. This chapter can be beneficial to all stakeholders of the sustainable agricultural practices.

Drawing on the conservation of resources (COR) theory, we propose a mediated moderation showing how proactive personality (PP) and job crafting toward interests (JC-interests) influence the relationship between interest incongruence and cyberloafing.

We used a three-wave survey and collected data from 429 full-time employees working in different industries in China.

We found that interest incongruence was positively related to cyberloafing. Furthermore, this positive relationship was more significant when employees were low in PP or engaged in low levels of JC-interests. In addition, the moderating effect of PP was mediated by JC-interests.

These findings are helpful for organizations in figuring out how to mitigate the detrimental effects of interest incongruence by providing more support to proactive employees and implementing various JC interventions.

This study suggests that PP and JC-interests (resource gain strategy) could mitigate the positive effect of interest incongruence on employees’ cyberloafing.

This paper aims to examine the role of local/provincial government in influencing corporate social and environmental reporting (CSER) in China, and more specifically, how the underlying economic and political factors associated with local government have influenced the quality of CSER.

The authors used 234 environmentally sensitive companies listed on the Shanghai and Shenzhen Stock Exchanges during 2013 and 2015 as the research sample to test the relationship between CSER and local government’s political connection and economic prioritisation and the potential mediating effect of local economic prioritisation.

The analysis provides evidence that local/provincial government’s political geographical connectedness with the central government has directly and positively influenced the level of CSER, while local prioritisation of economic development has a direct but negative effect on CSER in China. In addition, local/provincial prioritisation of economic development has mediated the relationship between local–central political geographical connectedness and CSER.

While local/provincial governments are heavily influenced by the coercive pressure from the central government, they also act in their own political and economic interests in overseeing CSER at the local level. This study raises the question about the effectiveness of the top-down approach to improving CSER in China and suggests that the central government may need to focus more on coordinating and harmonising different local/provincial governments’ interests to enable achieving a common sustainability goal.

The authors provide evidence revealing how the economic and political contexts of local government have played a significant role in shaping CSER in China. More specifically, this paper addresses a gap in the literature by highlighting the importance of local government oversight power for CSER development and how such oversight is determined by local prioritisation of economic development and political geographical connectedness of local and central governments.

Understanding young adults’ organic food purchasing behavior in the fresh food e-commerce platforms (FFEP) is crucial for expanding the global environmental product market. The study aims to investigate how specific characteristics of platforms and organic food information impact young adults’ perceived value, leading to their subsequent purchase intention.

Around 535 valid responses were collected through an online survey and then analyzed applying a two-stage structural equation model (SEM) and artificial neural network (ANN) approach.

Results of this research show that platform characteristics (including system quality and evaluation system) and product information characteristics (including organic label, ingredient information and traceability information) significantly affect young adults’ perceived utilitarian and hedonic value. The platform’s service quality has a strong effect on their perceptions of hedonic value, while the delivery system strongly influences their utilitarian value. Moreover, the perceived value, as a crucial mediator, plays a significant role in moderating the influence of platform and product information characteristics on the purchase intentions of young consumers regarding organic food.

Previous research has overlooked the credence attributes of organic food and particularities of online purchasing, focusing instead on general platform and product characteristics. This study addresses this gap by proposing a more appropriate model that integrates the characteristics of both the platform and product information. This offers theoretical and managerial implications for effectively stimulating organic food consumption among young adults in online environments.

The chapter provides a comprehensive analysis of the interplay between organizational socioecology, green technological innovation, and environmental regulations. It emphasizes the significance of organizational strategies in enhancing performance, particularly in contexts where environmental sustainability is a priority. The research delves into the theory of organizational socioecology, suggesting a convergence with sociological perspectives in organizational research. This approach underscores the interdependence between organizations and society, especially in the realm of environmental responsibility and climate change. A key aspect of the study is the exploration of green technological innovation in product and service development, aiming to reduce environmental impact. The dynamics of adopting green innovation are influenced by numerous factors, including government policies, market conditions, and organizational characteristics. The chapter examines the impact of environmental regulations on organizational behavior and innovation, discussing how these regulations can drive organizations towards green innovation, thus balancing the need for economic growth with environmental sustainability. Furthermore, the chapter addresses the role of government subsidies and incentives in encouraging organizations to adopt green technologies and practices. The effectiveness of these mechanisms in fostering a more sustainable and innovative organizational landscape is analyzed. Additionally, the article provides a comparative analysis of various theories and models related to organizational innovation and sustainability, integrating insights from different disciplinary perspectives. By combining empirical data with theoretical frameworks, the article assesses the effectiveness of organizational strategies in enhancing green innovation and meeting environmental regulations. It offers practical implications for organizations striving to align their practices with sustainability goals, contributing valuable insights for researchers, policymakers, and practitioners in the field of sustainability and organizational change.

Integrating balance theory and social identify theory, this paper proposes a multilevel model to explain how abusive supervision climate of team impacts the relationship among team members as well as subordinates’ behavior towards their teammates, especially organizational citizenship behavior (OCB).

A survey was conducted to collect two-wave and multi-source data from 398 employees nested in 106 teams from Chinese high-technology companies. Hierarchical linear modeling was conducted to examine the theoretical model.

The results indicate that there is an inverted U-shape association between abusive supervision climate and subordinates’ OCB towards coworker; team member exchange (TMX) mediates their inverted U-shaped link. Furthermore, we confirm that coworker support plays a vitally moderating role upon the curvilinear link of abusive supervision climate (ASC)–TMX; specifically, when employees perceive low coworker support, negative relations between ASC and TMX will be stronger.

This study identifies team members’ advantageous and adverse relational response to shared threat of ASC and examines coworker support as a moderator of ASC, which provides valuable insights into when and why employees tend to cooperate with their teammates to jointly confront their leader’s abuse and highlights the importance of coworkers, thus enabling organizations to deeply understand the wider influences of ASC on interpersonal relationship between team members.