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Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

The purpose of this paper is to examine whether, how and when new venture creation progress (NVCP) affects work-to-family conflict (WFC) by introducing coping behavior strategies as mediators, entrepreneurs’ prior experience and family involvement in business as moderators.

This study performs multivariate regression analysis based on a sample of 260 nascent entrepreneurs from the Chinese Panel Study of Entrepreneurial Dynamics.

This study reveals that an entrepreneur’s WFC tends to increase along with the growth of the new venture. Specifically, NVCP impels entrepreneurs to adopt reactive role behavior strategy and meet both entrepreneurial and family demands; meanwhile, NVCP propels entrepreneurs to adopt prioritizing entrepreneurship behavior strategy for the increasing work demands, thus leading to more WFC; the mediation effect of prioritizing entrepreneurship behaviors is stronger than that of reactive role behaviors, which leads to an overall positive main effect. Moreover, the preceding mediating paths are moderated by entrepreneurs’ prior experience and family involvement.

First, the authors have investigated how NVCP influenced WFC. However, the authors did not extend the research to the possible effect of WFC on entrepreneurial performance. Second, in the work-family-conflict literature, unmarried and those without children are often excluded since their private life demands differ significantly from parents’ demands. Although the authors control for marital status in the model, the number of children is still left uncontrolled. Furthermore, the authors only used the first two waves of data, leading to a potential selection bias. In addition, the Chinese context may have influenced the generalizability of the results in a complex manner.

This paper indicates that reactive role behavior strategy will decrease WFC, while prioritizing entrepreneurship behavior strategy will increase WFC. Therefore, the authors suggest entrepreneurs adopt more reactive strategy to reduce WFC. Besides, both prior experience and family involvement strengthen the relationship between NVCP and prioritizing entrepreneurship behavior strategy, thereby leading to more WFC. Therefore, entrepreneurs with prior experience and family involvement should pay more attention to their roles in family. Furthermore, entrepreneurs with family involvement can try to segment the entrepreneurship-family boundary psychologically. For example, entrepreneurs can avoid business talking with families but show concerns for them at rest time.

WFC has been found negatively related to individual health and well-being. And entrepreneurs experienced even more WFC than employees in established organizations. Therefore, it is of great importance to focus on the topic of reducing entrepreneurs’ WFC. This research indicates that entrepreneurs can experience less WFC by choosing reactive role behavior strategy. Prior experience and family involvement can induce them to be more attached to new venture creation. This research provides practical suggestions and reminders for entrepreneurs.

This mediated moderation model elaborates whether, how and when NVCP affects WFC, thereby contributing to the knowledge of entrepreneurship-family interface and enlightening nascent entrepreneurs about balancing their start-up responsibilities with their family life.

This paper aims to establish a model to evaluate the benefits of building information modeling (BIM) from the owners' perspective. The model analyzes the correlation between five secondary benefit indicators and their subsystems: product, financial, organizational, management and strategic. The final key factors of BIM benefits provide a decision-making basis for owners to raise the efficiency of BIM application.

Firstly, the authors combed 31 BIM-related literature and interviewed experts to identify 15 preliminary benefit indicators. The authors established a BIM benefit evaluation system based on relevant concepts, including two primary indicators, five secondary indicators and 15 tertiary indicators. Secondly, the authors analyzed the indicators by the extension theory of matter element analysis and a questionnaire survey of expert opinion. Finally, the new method was applied to a case study of a large shopping center in east China for empirical verification.

A BIM benefit evaluation model, including a three-tiered hierarchy of primary, secondary and tertiary indicators, was constructed through literature review and expert opinions. The model determined the critical factors of BIM benefits, enhanced understanding of owner benefits and improved BIM application under the owners' leadership.

At present, most studies focus on specific project stages or benefit indicators. This study developed an integrated BIM benefit evaluation system that targets owners. The findings could foster the development of China's construction industry, promote owner-led BIM application and advocate adopting the benefit evaluation method.

The development of mobile technology has changed the traditional financial industry and banking sector. While traditional banks have adopted artificial intelligence (AI) techniques to deepen the development of mobile banking applications (apps), the current literature lacks research on the use of AI-based constructs to explore users' mobile banking app adoption intentions. To fill this gap, based on stimulus-organism-response (SOR) theory, two AI feature constructs as stimuli are considered, namely, perceived intelligence and anthropomorphism. This study then develops a research model to investigate how intelligence and anthropomorphism affect task-technology fit (TTF), perceived cost, perceived risk and trust (organism), which in turn influence users' AI mobile banking app adoption (response).

This study used a convenience nonprobability sampling approach; a total of 451 responses were collected to examine the model. The partial least squares technique was utilized for data analysis.

The results show that intelligence and anthropomorphism increase users' willingness to adopt mobile banking apps through TTF and trust. However, higher levels of anthropomorphism enhance users' perceived cost. In addition, both intelligence and anthropomorphism have insignificant effects on perceived risk. The results provide theoretical contributions for AI-based mobile banking app adoption and offer practical guidance for bank planning to use AI to retain users.

Based on SOR theory, this study reveals that as features, AI-enabled intelligence and anthropomorphism help us further understand users' perceptions regarding cost, risk, TTF and trust in the context of AI-enabled app adoption intentions.

Currently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience.

A database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives.

The case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers.

The framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.

This study aims to develop a Stackelberg Game Model for seeking the optimal subsidy plans with varying levels of government financial capability (GFC). Furthermore, the scenario-based analysis is conducted and will enable governments to identify a comprehensive subsidy plan as follows: improve project performance and optimise social welfare.

A Stackelberg Game Model is developed to optimise the effectiveness of subsidies on the performance of public-private partnerships (PPPs).

According to the scenarios that are generated from the model, governments that are confronting with limited public budgets could reduce the intensity of performance incentives and increase the participation-oriented subsidy. Whilst a participation-oriented subsidy can stimulate private organisations’ willingness to participate in infrastructure PPPs, a performance-oriented subsidy is capable of facilitating the projects’ performances. Intuitively, the performance-oriented subsidy enables the private entities of PPPs to improve their efforts on the projects to realise higher profits. However, the participation-oriented subsidy is unable to affect the level of their effort spent on the projects. To satisfy both parties’ expectations in a PPP, the performance-oriented subsidy needs to be prioritised for a purpose of enabling higher quality outputs.

The game model developed in this study contributes to the literature by offering new insight into the underlying mechanism of governments and private entities, in terms of their decision-making for subsidy planning and contributions (i.e. resource allocation and spending) during the life-cycle of PPPs. This research enriches the government subsidy model by revealing the effects of the GFC and clarifies the impacts of two different schemes of subsidy on the performance of PPPs.

The government has been conventionally viewed as being omnipotent to provide PPPs with a wide range of subsidies. However, the subsidies are not unlimited, due to GFC. In addressing this void, this study has modelled the impacts of government subsidy plans with a consideration of GFC-related constraints. The combined effects of the participation- and performance-oriented subsidies on the project performance of PPPs have been examined.

The quality of produced textile fibers plays a very important role in the textile industry, and detection and assessment schemes are the key problems. Therefore, the purpose of this paper is to propose a relatively simple and effective technique to detect and assess the quality of produced textile fibers.

In order to achieve automatic visual inspection of fabric defects, first, images of the textile fabric are pre-processed by using Block-Matching and 3-D (BM3D) filtering. And then, features of textile fibers image are respectively extracted, including color, texture and frequency spectrum features. The color features are extracted by using hue–saturation–intensity model, which is more consistent with the human vision perception model; texture features are extracted by using scale-invariant feature transform scheme, which is a quite good method to detect and describe the local image features, and the obtained features are robust to local geometric distortion; frequency spectrum features of textiles are less sensitive to noise and intensity variations than spatial features. Finally, for evaluating the quality of the fabric in real time, two quantitatively metric parameters, peak signal-to-noise ratio and structural similarity, are used to objectively assess the quality of textile fabric image.

Compared to the quality between production and pre-processing of textile fiber images, the BM3D filtering method is a very efficient technology to improve the quality of textile fiber images. Compared to the different features of textile fibers, like color, texture and frequency spectrum, the proposed detection and assessment method based on textile fabric image feature can easily detect and assess the quality of textiles. Moreover, the objective metrics can further improve the intelligence and performance of detection and assessment schemes, and it is very simple to detect and assess the quality of textiles in the textile industry.

An intelligent detection and assessment method based on textile fabric image feature is proposed, which can efficiently detect and assess the quality of textiles, thereby improving the efficiency of textile production lines.

The performance of public–private partnerships (PPPs) can be determined by a variety of factors, i.e. influencing factors (IFs). This study is undertaken for a purpose of identifying how such factors determine the project's performance (i.e. factor transmission patterns), particularly from the key stakeholders' perspectives.

A hybrid approach, which comprises a Social Network Analysis, ISM (i.e. Interpretive Structural Modeling) and an improved DEMATEL (i.e. Decision-Making Trail and Evaluation Laboratory), was developed to analyze the causal relationships between the identified IFs as well as the transmission patterns of their impacts on PPPs. Data were collected from interviews and questionnaire surveys.

The transmission patterns of the identified IFs cascade from project environment and features and stakeholders' relationship to the project company capabilities and project process. It is identified that the public authority has a higher level than that of the private entity in PPPs.

It lacks longitudinal studies to investigate the dynamics of PPP stakeholder relationships and social networks. Future research needs to explore the transmission patterns of sub-factors affecting PPP performance and extend the applicability of the developed hybrid approach.

This research provides practitioners with a robust tool that is useful for and insights into enhancing the management of lifecycle performance. It ensures the public authorities and private entities embarking on PPPs will make an informed decision about the monitoring of the life cycle performance.

This study contributes to knowledge of managerial mechanisms that can be adopted to manage factors determining the performance of PPPs. It enables an understanding of stakeholders' roles in driving the life cycle performance of PPPs.

An optimal control model is built considering the private sector's opportunistic effort diversion and reciprocal effort improvement, while a numerical study is conducted to draw some managerial implications.

In infrastructure PPP projects, private sectors may opportunistically divert part of their effort from the current projects to other projects to allocate their limited human resources. Nevertheless, this effort diversion can be inhibited by dynamic incentives since the private sectors reciprocally exert greater effort into the current projects when receiving the dynamic incentives. This article investigates how the government specifies the output standard that the private sector should meet and offers dynamic incentives to mitigate the private sector's opportunistic effort diversion.

The output standard for the private sector to acquire the dynamic incentives should be specified as the output level corresponding to the private sector's optimal long-run stationary equilibrium (OLSE) effort level, which decreases with its reciprocal preference level but increases with its effort-diverting level. The optimal dynamic incentives comprise an initial incentive and a periodic OLSE incentive, which declines with the reciprocal preference level but improves with the effort-diverting level. Besides, the numerical study reveals that the government should distinguish whether the bidders have high effort-diverting levels and, if so, should focus on their reciprocal preference levels and decline the bidders with low reciprocal preference to avoid utility loss.

This article provides a theoretical model combining opportunistic behavior with reciprocal preference through an optimal control lens, thus embedding the problem of incentive design into a broader socioeconomic framework.

The aims of this study are to conduct a systematic review of public–private partnership (PPP) studies published from 2009 to 2019, to compare the results with Ke et al. (2009) who reviewed the PPP literature published from 1998 to 2008, and to trace the evolution of the PPP knowledge in the past two decades. This study also presents the possible directions that the PPP research may go towards in the future, arguably.

This study carried out a top journal-based search to identify the quality PPP articles published from 2009 to 2019. A total of 12 top-tier construction journals were systematically searched in the database of web of science (WOS), from which 279 PPP articles were identified for review.

The number of the identified articles, the titles of the journals, institutions, the most cited papers, and prevalent research methods were analyzed and compared. The existing PPP studies in construction journals were classified into seven streams. Through analysis of the PPP research status and gaps, five future research directions were revealed.

This study contributes to the current body of knowledge by revealing the research trend of PPP from 2009 to 2019. It presents the change of PPP development trend in the past decade through comparison with Ke et al. (2009). It also reveals the major research streams and points out the directions that the PPP research may go towards in the future. Moreover, this study is helpful to the practice as well. It can enhance the practitioners' understanding of the PPP development in the past decade. In addition, it identified the research institutions contributing the most in the area of PPP, which may serve as valuable reference for practitioners to locate the best institutions for consultancy or collaboration.