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The induction of geological disasters is predominantly influenced by the dynamic evolution of the stress and plastic zones of the multilayer rock formations surrounding deep-rock roadways, and the behaviours and mechanisms of high in situ stress are key scientific issues related to deep-resource exploitation. The stress environment of deep resources is more complex owing to the influence of several geological factors, such as tectonic movements and landforms. Therefore, in practical engineering, the in situ stress field is in a complex anisotropic three-dimensional state, which may change the deformation and failure law of the surrounding rock. The purpose of this study is to investigate the tunnelling-induced stress and plastic evolution causing instability of multilayered surrounding rock by varying three-dimensional in situ stresses.

Based on data from the Yangquan Coal Mine, China, a finite difference model was established, and the elastic-plastic constitutive model and element deletion technology designed in the study were analysed. Gradual tunnelling along the roof and floor of the coal seam was used in the model, which predicted the impact tendency, and compared the results with the impact tendency report to verify the validity of the model. The evolutions of the stress field and plastic zone of the coal roadway in different stress fields were studied by modifying the maximum horizontal in situ stress, minimum horizontal in situ stress and lateral pressure coefficient.

The results shown that the in situ stress influenced the stress distribution and plastic zone of the surrounding rock. With an increase in the minimum horizontal in situ stress, the vertical in situ stress release area of the roof surrounding rock slowly decreased; the area of vertical in situ stress concentration area of the deep surrounding rock on roadway sides decreased, increased and decreased by turn; the area of roof now-shear failure area first increased and then decreased. With an increase in the lateral pressure coefficient, the area of the horizontal in situ stress release area of the surrounding rock increased; the area of vertical in situ stress release area of the roof and floor surrounding rock first decreased and then increased; the area of deep stress concentration area of roadway sides decreased; and the plastic area of the surrounding rock and the area of now-shear failure first decreased and then increased.

The results obtained in this study are based on actual cases and reveal the evolution law of the disturbing stress and plastic zone of multilayer surrounding rock caused by three-dimensional in situ stress during the excavation of deep rock roadways, which can provide a practical reference for the extraction of deep resources.

In recent decades, interest in digital transformation (DX) within the architecture, engineering, and construction (AEC) industry has significantly increased. Despite the existence of several literature reviews on DX research, there remains a notable lack of systematic quantitative and visual investigations into the structure and evolution of this field. This study aims to address this gap by uncovering the current state, key topics, keywords, and emerging areas in DX research specific to the AEC sector.

Employing a holistic review approach, this study undertook a thorough and systematic analysis of the literature concerning DX in the AEC industry. Utilizing a bibliometric analysis, 3,656 papers were retrieved from the Web of Science spanning the years 1990–2023. A scientometric analysis was then applied to these publications to discern patterns in publication years, geographical distribution, journals, authors, citations, and keywords.

The findings identify China, the USA, and England as the leading contributors in the field of DX in AEC sector. Prominent keywords include “building information modeling”, “design”, “system”, “framework”, “adoption”, “model”, “safety”, “internet of things”, and “innovation”. Emerging areas of interest are “deep learning”, “embodied energy”, and “machine learning”. A cluster analysis of keywords reveals key research themes such as “deep learning”, “smart buildings”, “virtual reality”, “augmented reality”, “smart contracts”, “sustainable development”, “building information modeling”, “big data”, and “3D printing”.

This study is among the earliest to provide a comprehensive scientometric mapping of the DX field. The findings presented here have significant implications for both industry practitioners and the scientific community, offering a thorough overview of the current state, prominent keywords, topics, and emerging areas within DX in the AEC industry. Additionally, this research serves as an invaluable reference and guideline for scholars interested in this subject.

Metro wheels running on different lines can undergo wear at different positions. This paper aims to investigate the effects of wheel wear at two typical positions, i.e. wheel flange and tread, on the dynamic performance of metro vehicles and analyzes the differences, with an aim of providing theoretical support on wheel reprofiling for different metro lines.

Wheel profile data were measured on two actual metro lines, denoted A and B. It was observed that wheel wear on Lines A and B was concentrated on flanges and treads, respectively. A metro vehicle dynamics model was built using multibody dynamics software SIMPACK. Then it was applied to analyze the differences in effects of wheel wear at different positions on vehicle dynamic performance (VDP) for various speeds (50, 60 and 70 km/h) and line conditions (straight line, R1000m, R600m and R300m curves). Critical speed and vibration acceleration were used as indicators of VDP during linear motion (on straight track), while VDP during curvilinear motion (on curved track) was evaluated in terms of wheel/rail lateral force, wheel/rail vertical force, derailment coefficient and wheel unloading rate.

First, compared to wheel profile with tread wear, wheel profile with flange wear showed better performance during linear motion. When the distance traveled reached 8 × 104 and 14 × 104 km, the vehicle’s critical speed was 12.2 and 21.6% higher, respectively. The corresponding vertical and lateral vibration accelerations were 59.7 and 74.8% lower. Second, compared to wheel profile with flange wear, that with tread wear showed better performance during curvilinear motion, with smaller wheel/rail lateral force, derailment coefficient and wheel unloading rate. When the vehicle speed was 50, 60 and 70 km/h, the maximum difference in the three indicators between the two wheel profiles was 40.2, 44.7 and 23.1%, respectively. For R1000m, R600m and R300m curves, the corresponding maximum difference was 45.7, 69.0 and 44.4%, respectively.

The results of the study can provide a guidance and theoretical support on wheel reprofiling for different metro lines. On lines with large proportions of curved sections, metro vehicles are more prone to wheel flange wear and have poorer dynamic performance during curvilinear motion. Therefore, more attention should be paid to flange lubrication and maintenance for such lines. On lines with higher proportions of straight sections, metro vehicles are more prone to tread wear and have poorer performance on straight sections. So, tread maintenance and service requires more attention for such lines.

Existing research has focused primarily on the effects of wheel wear on VDP, but fails to consider the differences in the effects of wheel wear at different positions on VDP. In actual metro operation, the position of wheel wear can vary significantly between lines. Based on measured positions of wheel wear, this paper examines the differences in the effects of wheel wear at two typical positions, i.e. tread and flange, on VDP in detail.

Within the labor-intensive construction industry characterized by distinctly structural shortages in the labor force worldwide, efficient and effective migration of construction workers across regions is critical for the smooth operation of construction activities. This study aims to investigate how the interregional migration patterns of construction workers are impacted by the disparities in both employment opportunities and environment amenities between the origin and destination provinces.

Drawing on the push and pull theory and the archival data on 13,728 migrant construction workers in China, descriptive analyses are first performed to characterize the interregional migration patterns of the investigated construction workers. Combining regional data in the National Bureau of Statistics of China, this study uses hierarchical regression modeling techniques to empirically test the relative importance of the employment-related and environment-related factors in driving the interregional migration of construction workers after controlling for the effects of related economic and geographic factors.

The results provide evidence that the interregional migration of construction workers is principally driven by the disparities in employment opportunities while disparities in environment amenities (including climate comfort disparity, medical service disparity and educational service disparity) generally play much fewer substantive roles. With regard to the impacts of employment opportunities, the results provide evidence that compared with the disparity in job market size, the disparities in job income and industry development level are more significantly relevant factors, which positively pull and adversely push the interregional migration flows, respectively.

This study contributes to a deepened understanding of how workers specifically balance their employment and amenity needs to make temporary migration decisions in the “laggard” labor-intensive construction industry. This study also adds to the literature on population migration by characterizing the specific characteristics of construction workers and the temporary nature of the workers' migration activities. The findings hold important practical implications for construction organizations and policymakers for effectively managing the mobility of migrant construction workers.

The extant literature on migrant construction workers has primarily focused on the consequences of international migration and the generalization of empirical findings on population migration mechanisms in other domains to the construction industry is substantially limited by the specific characteristics of construction workers and the temporary nature of their migration activities. In addressing this gap, this study represents an exploratory effort to quantitatively characterize the interregional migration patterns of construction workers in the labor-intensive construction industry and examines the roles of employment opportunity and environmental amenity in driving interregional migration.

Despite the critical role of the policy environment in facilitating the advancement of building information modeling (BIM) as a systemic innovation to reshape traditional facility design, construction and operation processes, scant scholarly attention has been paid to systematically investigating how and why complex BIM policies are concretely and gradually implemented in different regional contexts from a dynamic policy diffusion perspective. This study aims to empirically investigate how different types of BIM policy instruments are dynamically implemented in heterogeneous regions over time and how the diffusion of BIM policies across different regions is comprehensively impacted by both internal efficiency needs and external legitimacy pressures.

This study employed a positivist research paradigm in which BIM policy data from 182 prefecture-level and above cities in China during 2011–2022 were analyzed with quantitative approaches for theory verification. Based on the content analysis of the evolutionary characteristics of the adopted BIM policy instruments in heterogeneous regions over time, the event history analysis (EHA) method was then used to further examine the mechanisms underlying the diffusion of BIM policies across different regions.

The content analysis results show that while environmental instruments (such as technological integration and goal planning) are the primary policy instruments currently adopted in China, recent years have also witnessed increasing adoptions of supply-side instruments (such as fiscal support and information support) and demand-side instruments (such as demonstration projects and tax incentives). After controlling for the impacts of regional fiscal and technical resources, the EHA results illustrate that BIM policy adoption positively relates to regional construction industry scale but negatively relates to regional industry productivity and that compared with public pressures from industry participants, vertical pressures from the central government and horizontal pressures from neighboring regions are more substantial drivers for policy adoption.

As an exploratory effort of using a dynamic policy diffusion perspective to systematically investigate how BIM policies are adopted in heterogeneous regional contexts to facilitate BIM advancement, this study not only characterizes the complexity and dynamics of BIM policies but also provides deepened understandings of the mechanisms underlying policy adoption in the conservative construction industry. The findings hold implications for how multifarious policy instruments can be more effectively and dynamically adopted to facilitate the advancement of BIM and related technologies as innovative solutions in the construction domain.

This study aims to explore how project-based firms, which generally organize most of their work around temporary projects in discontinuous and fragmented types of business contexts, proactively formulate and implement digital transformation strategies under institutional pressures in a predigital era.

An exploratory case study was conducted in a large-scale construction company in China using multiple data collection methods, including semistructured interviews, documentation collection and observation.

An integrated framework is developed to conceptualize three key dimensions of digital transformation strategies of project-based firms: strategic adaptation for organization-environment fit through balancing the internal efficiency needs with the external legitimacy pressures; proactive business transformation through comprehensively managing the roles of digital technologies in optimizing defined business processes and fostering new business models; and delicate organizational transformation to integrate temporary project-level operation processes with ongoing firm-level business processes.

This study represents an exploratory effort to empirically investigate how project-based firms strategically organize complex digital transformation imperatives in their discontinuous and fragmented business contexts. The findings contribute to deepened understandings of how complex organizational and environmental contexts can be comprehensively managed for systemic business and organizational transformations to leverage the value of emerging digital technologies for project-based organizations.

Due to the lack of industrywide data, now there is only perceptual knowledge, through survey data, that construction firms may face great challenges in geographical diversification. The article aims to provide an objective description on the state of interregional market entry practices in the Chinese construction industry. How these practices are influenced by related set of regional and industry-related factors is further studied.

Description analysis of the practices is first performed based on a first-hand industrial data set including 1,020 projects and 404 construction firms. Combining provincial panel data in the National Bureau of Statistics of China, related regional and industry-related factors on these practices are further analyzed through hierarchical regression models.

(1) Interregional market entry practices are not quite prevalent during the past two decades, only involving 271 projects and 111 firms which accounts for 26.57% and 27.47% of the analyzed subjects respectively; (2) Large, state-owned and experienced firms are more frequently involved in the practices; (3) A number of regional factors such as local market protection and industry scale also significantly impact the practices.

The empirical analysis of this study was conducted using the specific dataset from the National Quality Award Projects where firms tend to be national, which may limit the generalizability of the findings to the whole industry contexts.

While most of the research on market entry practices in the project-based construction industry is undertaken at project or firm level based on survey data, this research represents an exploratory effort of using objective data to provide a macro overview of the practices at the industry level. The findings could contribute to a deepened understanding of how these practices are impacted by related regional- and industry-level factors.

The purpose of this paper was to map the safety management research of construction industry by scientometric analysis, which can predict important highlights and future research directions of safety management research in the construction industry. As an important issue in the construction industry, safety management issues have been researched from different perspectives. Although previous studies make knowledge contributions to the safety management research of construction industry, there are still huge obstacles to distinguish the comprehensive knowledge map of safety management research in the construction industry.

This study applies three scientometric analysis methods, collaboration network analysis, co-occurrence network analysis and cocitation network analysis, to the safety management research of construction industry. 5,406 articles were retrieved from the core collection database of the Web of Science. CiteSpace was used for constructing a comprehensive analysis framework to analyze and visualize the safety management research of construction industry. According to integrating the analysis results, a knowledge map for the safety management research of construction industry can be constructed.

The analysis results revealed the academic communities, key research topics and knowledge body of safety management research in the construction industry. The evolution paths of safety management research in the construction industry were divided into three development stages: “construction safety management”, “multi-objective safety management” and “comprehensive safety management”. Five research directions were predicted on the future safety management research of construction industry, including (1) comprehensive assessment indicators system; (2) intelligent safety management; (3) cross-organization collaboration of safety management; (4) multilevel safety behavior perception and (5) comparative analysis of safety climate.

The findings can reveal the overall status of safety management research in the construction industry and represent a high-quality knowledge body of safety management research in the construction industry that accurately reflects the comprehensive knowledge map on the safety management research of construction industry. The findings also predict important highlights and future research directions of safety management research in the construction industry, which will help researchers in the safety management research of construction industry for future collaboration and work.

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.

Sentiment analysis is the subfield of data mining, which is profusely used for studying the opinions of the users by analyzing their suggestions on the Web platform. It plays an important role in the daily decision-making process, and every decision has a great impact on daily life. Various techniques including machine learning algorithms have been proposed for sentiment analysis, but still, they are inefficient for extracting the sentiment features from the given text. Although the improvement in sentiment analysis approaches, there are several problems, which make the analysis inefficient and inaccurate. This paper aims to develop the sentiment analysis scheme on movie reviews by proposing a novel classifier.

For the analysis, the movie reviews are collected and subjected to pre-processing. From the pre-processed review, a total of nine sentiment related features are extracted and provided to the proposed exponential-salp swarm algorithm based actor-critic neural network (ESSA-ACNN) classifier for the sentiment classification. The ESSA algorithm is developed by integrating the exponentially weighted moving average (EWMA) and SSA for selecting the optimal weight of ACNN. Finally, the proposed classifier classifies the reviews into positive or negative class.

The performance of the ESSA-ACNN classifier is analyzed by considering the reviews present in the movie review database. From, the simulation results, it is evident that the proposed ESSA-ACNN classifier has improved performance than the existing works by having the performance of 0.7417, 0.8807 and 0.8119, for sensitivity, specificity and accuracy, respectively.

The proposed classifier can be applicable for real-world problems, such as business, political activities and so on.