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Prefabricated construction has been widely accepted as an alternative to conventional cast-in-situ construction, given its improved performance. However, prefabricated concrete building projects frequently encounter significant delays. It is, therefore, crucial to identify key factors affecting schedule and explore strategies to minimise the schedule delays for prefabricated concrete building projects. This paper adopts the decision-making trial and evaluation laboratory (DEMATEL) model and analytic network process (ANP) method to quantify the cause-and-effect relationships and prioritise the key delay factors in terms of their importance in the Chinese construction industry. The DEMATEL model evaluates the extent to which each factor impacts other factors. The quantified extents are then converted into a prioritisation matrix through ANP. The delay factors of prefabricated construction projects are selected and categorised based on a literature review and an expert interview. Questionnaires are then implemented to collect the data. The results reveal that the issue of inefficient structural connections for prefabricated components is found to be the most significant factor and most easily affected by other delay factors. This research also suggests prioritising major delay factors, such as 'lack of communication among participants' and 'low productivity', in the Chinese construction industry during scheduling control. Overall, this research contributes an assessment framework for decision making in the scheduling management of prefabricated construction. developed to meet the requirements of sustainability and housing demand in China [14]. China has also embarked on several initiatives to promote prefabrication [15]. However, in comparison to conventional on-site construction, there are also notable disadvantages that should be considered, such as structural changeability, transportation restrictions, and span limits [16]. In particular, prefabricated concrete construction requires increased and more detailed coordination at all stages of a project (because of more construction phases, more complicated construction techniques, and more stakeholders), which increases the difficulty in progress monitoring and planning for construction management tasks.Unlike the traditional construction methods, researchers and engineers are advised to pay particular attention to schedule risks of prefabrication construction projects before the construction stage [17]. For example, Li et al.[18] utilised a fuzzy analytical hierarchy process approach for ranking project risks associated with modular construction. The results revealed that the control schedule in the design stage is an important factor with respect to project duration owing to a relatively high weighting value. Also, few studies have investigated stakeholder-related risks and their cause-and-effect relationships prior to the construction stage. Therefore, Li et al.[17] employed a social network analysis to recognise and investigate the underlying network of stakehol...

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International audienc

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Studying the genetic diversity and population structure of natural forest populations is essential for evaluating their ability to survive under future environmental changes and establishing conservation strategies. Pinus koraiensis is a conifer species with high ecological and economic value in Northeast China. However, its natural forests have been greatly reduced in recent years, mostly due to over exploitation and over utilization. Here, we evaluated the genetic diversity and population structure of seven populations of P. koraiensis located throughout its native distribution. A total of 204 samples were genotyped with nine polymorphic nuclear SSR (simple sequence repeat) markers. The results showed high genetic diversity in all populations, with an average expected heterozygosity of 0.610, and the northern-most populations (Dailin (DL) and Fenglin (FL)) showed slightly higher diversity than the other five populations. The level of genetic differentiation among populations was very low (FST = 0.020). Analysis of molecular variance (AMOVA) showed that only 2.35% of the genetic variation existed among populations. Moreover, STRUCTURE analysis clearly separated the seven populations into two clusters. Populations DL and FL from the Xiaoxinganling Mountains comprised cluster I, while cluster II included the five populations from the Changbai Mountains and adjacent highlands. Our research on the genetic diversity and population structure of P. koraiensis in natural forests of China can provide a basis for the implementation of programs for the conservation and utilization of P. koraiensis genetic resources in the future.

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