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Precast wall lifting during prefabricated building construction faces multiple non-lean problems, such as inaccurate lifting-time estimation, unreasonable resource allocation and improper process design. This study aims to identify the pathways for improving lifting performance to advance lean construction of prefabricated buildings.

This study developed a methodological framework that integrates the discrete event simulation method, the elimination, combination, rearrangement and simplification (ECRS) technique and intelligent optimization tool. Two schemes of precast wall lifting, namely, the enterprise's business as usual (BAU) and enterprise-leading (EL) schemes, were set to benchmark lifting performance. Furthermore, a best-practice (BP) scheme was modeled from the perspective of lifting activity ECRS and resource allocation for performance optimization.

A real project was selected to test the effect of the methodological framework. The results showed that compared with the EL scheme, the BP scheme reduced the total lifting time (TLT) by 6.3% and mitigated the TLT uncertainty (the gap between the maximum and minimum time values) by 20.6%. Under the BP scheme, increasing the resource inputs produces an insignificant effect in reducing TLT, i.e. increasing the number of component operators in the caulking subprocess from one to two only shortened the TLT by 3.6%, and no further time reduction was achieved as more component operators were added.

To solve non-lean problems associated with prefabricated building construction, this study provides a methodological framework that can separate a typical precast wall lifting process into fine-level activities. Besides, it also identifies the pathways (including the learning effect mitigation, labor and machinery resource adjustment and activities’ improvement) to reducing TLT and its uncertainty.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Early termination of public–private partnerships (PPPs) in China is caused by various risk factors, resulting in significant losses. This study aimed to clarify the key factors and identify the causal relationships among these factors.

Social network analysis (SNA) was used to analyze 37 risk factors that were summarized from 97 early terminated PPP cases and to identify the relationships among these key risk factors. Interpretive structural modeling (ISM) was conducted to explore the causal relationships. Data were collected from case documents, questionnaires and interviews.

A total of 17 key risk factors were identified and distributed in a hierarchical structure with six tiers. Among these key risk factors, the root causes affecting the early termination of PPP projects were government oversight in decision-making, local government transition, policy and law changes and force majeure. The direct cause was insufficient returns. Furthermore, local government and private sector defaults were essential mediating factors. Local government transition and the low willingness of the private sector were highlighted as potential key risks.

The cases and experts were all from China, and outcomes in other countries or cultures may differ from those of this study. Therefore, further studies are required.

This research provides knowledge regarding the key risk factors leading to the early termination of PPP projects and guidance on avoiding these factors and blocking the factors' transmission in the project lifecycle.

This study contributes to the knowledge of risk management by emphasizing the importance of local government transition, the low willingness of the private sector and project cooperation and operation, whose significance is ignored in the existing literature. The proposed ISM clarifies the role of risk factors in causing early termination and explains their transmission patterns.

It has been ten years since the policy was implemented, but the effect of the policy needs to be tested empirically. This paper aims to explore the mechanism of policy influence on regional innovation ability by measuring the effectiveness of policy by innovation ability indicators. Further, it reflects the problems in the process of the transformation and development of resource-based cities in recent years and points out the direction for the development of the cities in the future. In addition, this paper discusses the differences between regions and cities in China and seeks the path to narrow the gap.

This paper mainly uses the difference-in-difference method for the research. This study divided China’s resource-based cities and non-resource-based cities into experimental groups and control groups, and explored the effect of the transformation and development of resource-based cities and the changes of their innovation ability under the influence of the National Sustainable Development Plan for Resource-based Cities (NSDPRC). More carefully, this paper uses the fixed effects regression model, propensity score matching method, bootstrap method and other methods to improve the empirical results.

This paper finds that NSDPRC significantly improves the innovation ability of resource-based cities, although there is some lag in this effect. Research on the influence mechanism of policies shows that NSDPRC improves the marketization degree of resource-based cities and reduces the proportion of the secondary industry in such cities. Finally, the results of the heterogeneity analysis confirm that policies are more popular in western China and that resource-based cities in growth, maturity and decline are more vulnerable to policy influence. The development of policy effectiveness also requires the size of a city, and maintaining a healthy and reasonable scale is necessary for urban development.

First, the existing research on the development of resource-based cities is mainly from the perspective of economy and environment, but rarely from the perspective of innovation ability, and the index to measure urban development is relatively single. This paper will compensate for this deficiency. Second, different from the European and American countries that have basically completed the industrial transformation, the research on Chinese cities will provide a reference for the transformation of developing countries. Finally, from the perspective of resource endowment theory and innovation theory, this paper discusses the influence of SDPNRBC mechanism on the innovation ability improvement of resource-based cities, and further improves and enriches the theory.

The purpose of this study is to investigate the recovery and resilience tourism strategies and possible future development of four main Chinese tourism cities.

The authors collected data from the official accounts of tourism administrations of these cities, tourist attractions and opinions from media and newspapers in Sina Weibo platform. The authors adopted an inductive approach in observing relevant social media posts and applied content analysis to identify main China’s tourism prevention and recovery strategies.

During the mass pandemic infection period, top-down prevention and control measures were implemented by the Chinese central and local governments, with feasible and regional recovery policies and protocols being adapted according to local situations. Measures related to tourism industrial re-employment, improvement of international images and governmental financial supports to re-boost local tourism in Chinese cities were paid great attention. Digitalization, close-to-nature and cultural heritages became important factors in the future development of China’s tourism. Dark tourism, as a potential tourism recovery strategy, also obtained huge emergence, for the memory of people deceased in the pandemic and for the inheritance of national patriotism.

This study enriches the current literature in urban tourism recovery studies analyzing the specific case of Chinese tourism cities and fulfill some voids of previous research mostly focused on the first wave of the pandemic and the recovery strategies mainly of Western cities. It also provides valuable suggestions to tourism practitioners, destinations and urban cities in dealing with regional tourism recession and finding possible solutions for the scenario associated to the COVID-19 and other similar health crisis.