Due to the more complex structure, greater size, and longer constructing period, most construction projects are facing more and greater risks than before and need more effective methods and tools to manage risks. However, because of the complex mathematic formulas, the difficulty of collecting data, or low profit margin etc., professionals are finding that few risk management methods and tools are sufficient, and they typically rely on their experience in practical construction-project risk management. To solve these problems, we present Building Information Modeling (BIM) as a new technology and the future of the Architectural, Engineering and Construction (AEC) industry. This combined with Earned Value Analysis (EVA) for providing a BIM-based construction project cost and schedule risk early warning model (BIM-CPCSREWM) to better and effectively manage construction project risks. Herein we will present a background and a description of the proposed model, develop the system prototype of BIM-CPCSREWM, and utilize a practical medicine construction project to illustrate the model’s validity.

The building performance simulation (BPS) based on physical models is a popular method to estimate the expected energy-savings of energy-efficient building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. Incomplete information generally comes from the information that is missing, such as the U-value of part building components, due to incomplete documentation or component deterioration over time. It also comes from the case-specific incomplete information due to different documentation systems. Motivated by the available big data of real-life building performance datasets (BPDs), a data-driven approach is proposed to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach constructed a Performance Modelling with Data Imputation (PMDI) with integrated backpropagation neural networks, fuzzy C-means clustering, principal component analysis, and trimmed scores regression. An empirical study was conducted on real-life buildings in Sweden, and the results validated that the PMDI method can model the performance ranges of energy-efficient retrofitting for family house buildings with more than 90% confidence. For a target building in Stockholm, the suggested retrofitting measure is expected to save energy by 12,017~17,292 KWh/year.

Research on information models for the construction schedule management based on the IFC standard

Purpose: This study explores citizens’ acceptance of prefabricated housing (PH) from the perspective of socio-demographic and psychological factors, aiming to reveal the formation of PH acceptance and realize a sustainable development of PH. Design/methodology/approach: This study proposed hypotheses drawing on procedural justice theory and uncertainty management theory. A survey of 245 respondents was conducted, and the collected data was analyzed in a stepwise multiple regression model. Then, the psychological influencing mechanism was demonstrated using a mediation model. Findings: Results of the data analysis manifested that citizens’ acceptance of PH was influenced by socio-demographic and psychological factors, where psychological factors had more significant effects on acceptance than socio-demographic factors. The psychological mechanism was examined by verifying the mediating role of uncertainty between procedural justice and the acceptance of PH. Furthermore, a scientific strategy for developing PH was proposed based on this empirical study. Originality/value: This study extends the knowledge of procedural justice theory by investigating people’s acceptance in the PH context. This study is also one of the first studies to unveil the psychological mechanism toward a high-cost product with invisible technological innovation. This study contributes to the literature by introducing uncertainty management theory to a controversial issue, examining and expanding its application in a complicated context. Moreover, results highlight the positive influence of fair processes on controversial issues.

Against the dual-carbon background, civil aviation is in urgent need of low-carbon and green transformation. Carbon emissions from airports are one of the main environmental concerns in civil aviation, so the early realization of airport carbon peak and carbon neutrality will help accelerate the construction of green civil aviation and assist in the low-carbon transformation and upgrading of civil aviation. According to research, the terminal building, aircraft, and ramp area are the main sources of airport carbon emissions. Taking Harbin Taiping International Airport as an example, this study first measured the carbon emissions from the terminal building and ramp area of the airport in the past five years by using the emission factor method; then, we measured the carbon emissions from aircraft in the airport in the past five years by using the ICAO method and finally predicted the trend of carbon emissions from aircraft and the possibility of reaching the peak carbon emissions of the airport in the coming years by using scenario analysis and the Monte Carlo simulation method. The results show that the total carbon emissions of Harbin Taiping International Airport will be 458,800 tonnes in 2023 and up to 581,100 tonnes in 2035; under the scenarios of green development and technological innovation, the airport’s carbon emissions can reach their peak by 2035, which will be lower and reached earlier under the scenario of technological innovation; the airport can improve energy use efficiency, increase the utilization of renewable energy sources, establish a carbon emission monitoring system, and actively participate in the market for carbon emission monitoring systems. Airports can systematically build a development path for airport carbon peaking by improving energy efficiency, increasing the utilization rate of renewable energy, establishing a carbon emission monitoring system, actively participating in carbon trading in the market, etc., so as to reduce carbon emissions from airports and promote the transformation and upgrading of civil aviation into a green and low-carbon sector.