Over the past years, people’s understanding of building information modeling (BIM) in the architecture, engineering, and construction (AEC) industry has improved significantly. Building information modeling 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 and colleges. Building information modeling can also be used as a learning tool that can aid project teams in familiarizing themselves with a construction task before 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 ...

Forecasting construction waste generation is the yardstick of any effort by policy-makers, researchers, practitioners and the like to manage construction and demolition (C&D) waste. This paper develops and tests an S-curve model to indicate accumulative waste generation as a project progresses. Using 37,148 disposal records generated from 138 building projects in Hong Kong in four consecutive years from January 2011 to June 2015, a wide range of potential S-curve models are examined, and as a result, the formula that best fits the historical data set is found. The S-curve model is then further linked to project characteristics using artificial neural networks (ANNs) so that it can be used to forecast waste generation in future construction projects. It was found that, among the S-curve models, cumulative logistic distribution is the best formula to fit the historical data. Meanwhile, contract sum, location, public-private nature, and duration can be used to forecast construction waste generation. The study provides contractors with not only an S-curve model to forecast overall waste generation before a project commences, but also with a detailed baseline to benchmark and manage waste during the course of construction. The major contribution of this paper is to the body of knowledge in the field of construction waste generation forecasting. By examining it with an S-curve model, the study elevates construction waste management to a level equivalent to project cost management where the model has already been readily accepted as a standard tool.

Rural infrastructure is of vital importance for agricultural growth, economic development, and poverty alleviation, particularly in developing countries such as China. In line with the implementation of a Coordinated Urban-Rural Development Strategy, infrastructure investment in China has consciously been tilted to rural areas. An urgent need exists to assess whether the investment has induced the benefits as expected. Existing research on rural infrastructure investment assessment focuses primarily on economic return while neglecting its social and ecological benefits. This paper identifies a set of critical assessment indicators (CAIs) that can be used to evaluate the multifaceted benefits of rural infrastructure investment in China. Research data were collected through a questionnaire survey given to three groups of experts, including government officers, professionals, and business practitioners who are working in China’s housing and urban-rural development sector. Monte Carlo simulation (MCS) is used...