Construction projects are at risk of dispute. Dispute prevention is thus expected to avoid that cost. This paper aims to establish a model predicting the occurrence of disputes and identifying strategies regarding resource allocation in dispute avoidance. Factors in the model were identified by a literature review and conceptualised into aetiology and progression of dispute formation based on an analogy with epidemiological investigations. The model structure was established upon this analogy, validated against Bradford Hill criteria, and then quantified by conducting a Bayesian network analysis with samples returned from a questionnaire survey. The model shows high accuracy rates with both complete and incomplete input data. Finally, a sensitivity analysis was conducted to simulate the results of potential management measures available to be taken. This predictive model not only serves the prediction function but also traces back to factors causing disputes, and hence can be used to assist decision making before and during a construction process. At the centre of the model sits the causal mechanism investigation, but the existing methods only shed lights on correlational research without causal consideration. This paper devised an epidemiological investigation which covers from causal proposition to causal examination and contributes greatly to model establishment.

The aim of this paper is to understand the formation mechanism of and establish a predictive model for payment issues faced by contractors in China’s construction industry. Factors critical to such issues were first identified under different categories from literature and verified by a Delphi survey. This paper identified critical factors under three categories, i.e., culture, client’s financial management, and interactions and processes. A focus group was then conducted to understand the roles played by each category of factors. Specifically, the cultural factors explained the origination of payment. By taking an analogy, the current unhealthy culture acted as pathogens of payment issues as a disease in the body of the construction industry; the financial management capability of the client acted as the defense system; and the interactive and process factors played the role as trigger and catalyst. It was followed by a logical deduction assisted by another Delphi survey to understand the associations among the identified factors. The model structure was hence constructed upon the factors and their associations and was then quantified by Bayesian belief network parameter learning with quantitative data collected from a questionnaire survey. As such, the formation mechanism of payment issues was investigated, based on which a Bayesian classifier was established from the perspective of Chinese contractors. The model demonstrated a high accuracy rate of more than 90%. This paper systematically investigated the formation mechanism of payment issues in China’s construction industry and revealed that root causes of payment issues could not be eliminated in a short term. The predictive model endows contractors with advantages in proactively evaluating if they are capable of tackling the risks of potential payment issues without disturbing the current power balance of the industry.

In developing countries, decision-making regarding old rural houses significantly relies on expert site investigations, which are criticized for being resource-demanding. This paper aims to construct an efficient Bayesian classifier for house safety and habitability risk evaluations, enabling people with none-civil-engineering backgrounds to make judgements comparable with experts so that house risk levels can be checked regularly at low costs. An initial list of critical risk factors for house safety and habitability was identified with a literature review and verified by expert discussions, field surveys, and Pearson’s Chi-square test of independence with 864 questionnaire samples. The model was constructed according to the causal mechanism between the verified factors and quantified using Bayesian belief network parameter learning. The model reached relatively high accuracy rates, ranging from 91.3% to 100.0% under different situations, including crosschecks with unused expert judgement samples with full input data, crosschecks with unused expert judgement samples with missing input data, and those involving local residents’ judgement. Model sensitivity analyses revealed walls; purlins and roof trusses; and foundations as the three most critical factors for safety and insulation and waterproofing; water and electricity; and fire safety for habitability. The identified list of critical factors contributes to the rural house evaluation and management strategies for developing countries. In addition, the established Bayesian classifier enables regular house checks on a regular and economical basis.

To address the learning, teaching, and functional issues in a pandemic, large-sized class, and virtual learning environment, a two-week-long serious game was designed to act as the main welcome activity. Three hundred twenty students played through four scenarios with the pandemic as their background, and a narrative device contributed to an immersive learning environment. The game was deployed over two weeks, using a range of reports, video content, online collaborative platforms, and novelty applied mass communication theory, which effectively enhanced inter- and intra-team interaction and prepared the students for their project management team-based experiential learning. This case study will walk readers through the processes and theories used to create the game and ensure its operational, social, and pedagogic benefits.