Jinfan Zhang

Knowledge plays a vital role in achieving sustainable development goals (SDGs) in construction industry. According to the view of organization learning, heterogeneous teams have heterogeneous core knowledge. The main aim of this study is to explore the differences in demand for sustainability learning knowledge (SLK) among the mega engineering construction project (MECP) teams, which has important implications for improving the performance of team learning and achieving SDGs in MECPs. This study followed a research design that included identifying SLK and the stakeholder teams that needed SLK and investigating the respondents’ perception of the level of demands for SLK through semistructured interviews and survey questionnaires executed in China. The statistical test results indicated that there were some significant differences in demands for some SLK among the MECP teams and MECPs. All types of MECP teams had a common high demand for SLK on environment, safety, and health in China. This study contributes to research on sustainable construction in MECPs in two main ways: (1) it reveals novel insights related to the integration of SL into the MECPs sustainable construction practices for developing countries and (2) it offers the possibility for practitioners to identify the most crucial knowledge and focus their efforts and resources efficiently.

Green buildings are an important initiative to address energy and environmental issues in the construction field. The high-quality development of green buildings is affected by many factors, and it is necessary to identify the critical factors affecting the high-quality development of green buildings and analyze them systematically. The adopted literature analysis method and expert consultation method, the DIM (DEMATEL-ISM-MICMAC) model was established to explore critical factors influencing green buildings’ high-quality development and their internal hierarchical structure, interrelationships, and mechanisms. Then, targeted suggestions were put forward to promote green buildings’ high-quality development. The results showed that: (1) The critical factors influencing green buildings’ high-quality development could be divided into five levels, three groups, and four areas. The economic development level, living standard of residents, education level, incentive policies, and compulsory laws and regulations were in the deep factor group, fundamentally affecting green buildings’ high-quality development. (2) In terms of drive and centrality, the economic development level, living standard of residents, education level, and incentive policies were at the forefront, playing a vital role in the high-quality development of green buildings.

The construction megaproject team (CMPT) emphasises integration as a megaproject multiteam system (MTS) to facilitate knowledge learning. This paper synthesises the CMPT structure characteristics and team learning processes into a conceptual framework named the CMPT-MTS learning framework to advance future CMPT learning research. The constructs included are identified from published research. A case example is the island and tunnel project of the Hong Kong–Zhuhai–Macao Bridge, which provides practical grounding for framework refinement. The conceptual framework deemed to follow a cyclical pattern identifies the CMPT-MTS structure variables and team learning processes affecting learning outcomes and contextual variables hypothesised to influence the intra- and inter-team interaction. We discuss how the conceptual framework might identify the CMPT learning research gaps for future research. The framework’s contribution to the body of knowledge expands our lens to understand team learning practices in the complex CMPT by considering CMPT as an MTS.

Reversible lanes constitute an important solutions for sustainable transportation, with the aim to solve the practical problem of reversible lane optimization of urban road networks constrained by adjustment time. Considering the relationship between the number of lanes and the capacity of sections, a mixed-integer bilevel programming model of reversible lane optimization constrained by adjustment time is constructed in order to minimize the total travel time of the system. The results show that the model can effectively obtain the optimal strategy for any number of reversible sections subject to adjustment time constraints. With the increase of the number of reversible sections that can be optimized within the adjustment time, the cumulative reduced system time increases monotonically and the road network optimization effect improves, but as a whole, the optimization effect of the newly added reversible sections in each stage shows a decreasing trend. When the number of reversible sections that can be optimized within the adjustment time reaches a certain number, increasing the number of reversible sections will have a limited further effect on the overall system. For the reversible lane optimization problem of urban road networks, only efficient reversible sections need to be optimized to achieve a good optimization effect.