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Considering the substantial benefits derived from the use of Building Information Modeling (BIM) in construction projects, governments and its related sectors have introduced mandatory policies requiring the use of BIM. However, little is known about the impact of mandatory policies on BIM-based project performance. Therefore, the purpose of this paper is to provide a systematical understanding on the impact of policy interventions on the implementation practice of innovative technologies.

This paper utilizes the propensity score matching and difference in differences (PSM-DID) method to investigate the impact of policy interventions on BIM-based project performance. Using the panel data collected from 2015 to 2021 in the Hong Kong construction industry, this paper explores the impact of the first mandatory BIM policy on the BIM-based project performance of three key stakeholders.

The subjective BIM performance and BIM return on investment (ROI) have significantly improved after implementing the mandatory BIM policy. The promotion effect of mandatory BIM policy on BIM-based project performance gradually increases over time. Moreover, the promotion effect of mandatory BIM policy on BIM performance shows significant heterogeneity for different stakeholders and organizations of different sizes.

This study examined the impact of policy interventions on BIM-based project performance. The research findings can provide a holistic understanding of the potential implications of innovative mandatory policy in performance improvement and offer some constructive suggestions to policymakers and industry practitioners to promote the penetration of BIM in the construction industry.

The purpose of this paper is to provide theoretical basis and data support for researchers to choose appropriate international partners, provide a basis for Chinese research funding agencies, such as National Natural Science Foundation of China (NSFC) to formulate international research collaboration (IRC) strategies and policies and provide recommendations for the improvement of the internationalization level of China's basic scientific research.

Based on existing research, this study took output of “Major International (Regional) Joint Research Project” (MIJRP) funded by NSFC and participated by Chinese scholars in the meantime as the analysis object, proposed hypotheses and constructed the indicators of IRC and research output (RO). In addition, the mathematical statistics was used to compare the RO of China's IRC and nonIRC, and the statistical analysis model was used to measure the influence on RO of collaboration country's research capacity, research collaboration between China and US, scope of international research collaboration and reprint author country.

The RO of China's IRC is higher than that of nonIRC; research capacity of collaboration country has no inevitable effect on the RO of China's IRC; the RO of China's IRC participated by Americans is higher than that without American scholars; expanding the scope of China's IRC to some degree can increase RO; the RO of China's IRC led by foreigners is higher than that led by Chinese. In particular, China–US IRC and foreign scholars acting as the reprint author are two major factors for the RO of China's IRC.

Most of the traditional research on IRC are based on the co-author papers, and this study tried to analyze the characteristics and regularities on IRC from a new view of international collaboration projects, which can be a supplement to the traditional international collaboration research on co-author papers.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

This paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.

A chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.

The involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.

This research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.