Greenhouse gas (GHG) emissions in the construction stage will be more relatively significant over time.Different construction methods influence GHG emissions in the construction phase. This study investigates the differences of GHG emissions between prefabrication and conventional construction methods. This study sets a calculation boundary and five emission sources for the semi-prefabricated construction process: embodied emissions of building materials, transportation of building materials, transportation of construction waste and soil, transportation of prefabricated components, operation of equipment, and construction techniques. A quantitative model is then established using a process-based method. A semi-prefabrication project and a conventional construction project in China are employed for preliminary examination of the differences in GHG emissions. Results show that the semi-prefabrication method produces less GHG emissions per square meter compared with the conventional construction, with the former producing 336 kg/m 2 and the latter generating 368 kg/m 2 . The largest proportion of total GHG emissions comes from the embodied emissions of building materials, accounting for approximately 85%. Four elements that positively contribute to reduced emissions are the embodied GHG emissions of building materials, transportation of building materials, resource consumption of equipment and techniques, and transportation of waste and soil, accounting for 86.5%, 18.3%, 10.3%, and 0.2%, respectively, of reduced emissions; one a negative effect on reduced emissions is the transportation of prefabricated components, which offsets 15.3% of the total emissions reduction. Thus, adopting prefabricated construction methods contribute to significant environmental benefits on GHG emissions in this initial study.

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In the current economic climate, budgets for the maintenance of public buildings are unlikely to meet the ever-increasing maintenance needs. Although it is unlikely that this problem can be overcome completely without an injection of further resources, it is possible for government maintenance authorities to improve the situation by ensuring that the best solution in terms of 'value for money' is achieved in the planned maintenance programme. A maintenance plan which is based on a rational assessment of priorities and up-to-date knowledge of the condition of the property stock will help to ensure the best use of available resources. Based on the multi-attribute maintenance prioritization model developed by Alan Spedding, Roy Holmes and Qiping Shen at the University of West of England, which is simple in practice and flexible from a management point of view, this paper presents the results of some further research into this area by modifying the original model using an analytical hierarchy process in deciding the weightings of the criteria set out in the prioritization model. This modified model is more quantitative and objective than the original model. The validation of the framework is also discussed.Planned Maintenance, Multi-attribute Prioritization, Priority Setting, Public Buildings, Analytic Hierarchy Process Ahp,

Over the past years, people's understanding towards Building Information Modeling (BIM) in the architecture, engineering, and construction (AEC) industry has improved significantly. BIM can be diversely recognized as a virtual design and construction environment, a communication vehicle among stakeholders, a lifelong information model, or an education platform that can be used in universities or colleges. BIM can also be used as a learning tool that can aid project teams in familiarizing themselves with a construction task, prior to commencement of the task on site. Yet, little effort has been made to measure the benefits of this kind. The aim of this research is to empirically measure the benefits of BIM as a learning tool in real-life construction tasks. The learning curves of two situations: construction tasks with and construction tasks without BIM are identified by following a series of analytical processes. The two learning curves are compared and the learning effects contributed by BIM are modeled as L effBIM . By inputting their own data, practitioners may use this generic model to measure learning effects contributed by BIM in their own projects. The model can be used to convince potential BIM users by showing empirical evidence of BIM's benefits. It is also hoped that the model can join the concerted efforts to promote BIM's value in the AEC industry.