The COVID-19 pandemic has put labor-intensive industries at risk, among which the construction industry is a typical one. Practitioners in the construction industry are facing high probabilities of COVID-19 transmission, while their knowledge, attitudes, and practices (KAP) are critical to the prevention of virus spread. This study seeks to investigate the KAP of construction industry practitioners in China through an online questionnaire survey conducted from 15 to 30 June 2020. A total of 702 effective responses were received and analyzed. The results revealed that: (1) although an overwhelming percentage of respondents had the correct knowledge about COVID-19, there were significant respondents (15% of all) who were unsure or wrong about the human-to-human transmission of the virus; (2) practitioners generally showed an optimistic attitude about winning the battle against the COVID-19 pandemic and were satisfied with the governments' contingency measures; (3) practitioners tended to actively take preventive measures, although checking body temperature, wearing face masks, and keeping safe social distance still needs to be reinforced. This research is among the first to identify the KAP of construction industry practitioners toward the COVID-19 pandemic in China. Results presented here have implications for enhancing strategies to reduce and prevent COVID-19 spread in the construction industry.

This paper adopts a novel approach of Support Vector Machine (SVM) to forecast residential housing prices. as one type of machine learning algorithm, the proposed SVM encompasses a larger set of variables that are recognized as price-influencing and meanwhile enables recognizing the geographical pattern of housing price dynamics. The analytical framework consists of two steps. The first step is to identify the supporting vectors (SVs) to price variances using the stepwise multi-regression approach; and then it is to forecast the housing price variances by employing the SVs identified by the first step as well as other variables postulated by the hedonic price theory, where the housing prices in Taipei City are empirically examined to verify the designed framework. Results computed by nonparametric estimation confirm that the prediction power of using SVM in housing price forecasting is of high accuracy. Further studies are suggested to extract the geographical weights using kernel density estimates to reflect price responses to local quantiles of hedonic attributes.

Building information modelling (BIM) plays an important role in furthering value-creation of construction projects by advocating the inter-firm cooperation. When implementing BIM, however, individual firms inherently safeguard their self-interests regardless of the fact that inter-firm cooperation might reap joint BIM benefits for a project overall, which epitomizes a typical problem of moral hazards in project-based organizations. This paper develops an outcome-linked benefit sharing model that considers sharing joint BIM benefits among stakeholders including designers, contractors, and clients for tracking moral hazards therein. By modeling stakeholders' behaviors as evolutionary games within a principal-agent formalism, it has been deducted that (1) designers/contractors could be incentivized to cooperate had each stakeholder received a share higher than the quotient of BIM costs over value-creation in the design/construction phase; and (2) how joint BIM benefits can be more than noncooperation outcomes is key for clients to support BIM implementation.