The global climate change has resulted in huge flood damages, which seriously hinders the sustainable development of rural economy and society and causes famers’ livelihood problems. In flood-prone areas, it is imperative to actively study short and long-term strategies and solve farmers’ livelihood problems accordingly. Following the sustainable development analysis framework proposed by the Department for International Development (DFID), this study collects empirical data of 360 rural households in six sample villages in the Jialing River Basin of Sichuan Province, China through a village-to-household field questionnaire and applies the Multinominal Logit Model (MNL) to explore the influence of farmer households’ capital on livelihood strategy choice. Research results show that: (1) In human capital category, the education level of the household head has a significant positive impact on the livelihood strategies of farmers’ families; (2) In physical capital category, farmer households with larger space have more funds to choose among flood adaptation strategies; (3) In natural capital category, house location and the sale of family property for cash have the greatest negative impact on farmers’ livelihood strategies; (4) Rural households with more credit opportunities in financial capital are more willing to obtain emergency relief funds; (5) Farmers’ families helped by the village for a long time will probably not choose to move to avoid floods, but are more likely to choose buying flood insurance. This study provides an empirical reference for effective short and long term prevention and mitigation strategies design and application in rural in flood-prone areas.

With the popularization and rise in BIM technology usage, BIM education for undergraduate students in architecture, engineering, and construction (AEC) related disciplines has emerged as a priority. This study assesses the BIM learning outcomes of students participating in the National BIM Graduation Design Innovation Competition of Colleges and Universities. In total, 2777 valid questionnaire responses were obtained for this study. The Cronbach’s alpha coefficient method and principal component factor analysis method were used to verify the reliability of the data set (Cronbach’s alpha = 0.962, KMO = 0.965). The t-test (ANOVA) was used to verify that gender, school type, major, grade, study duration and use BIM related software, as well as other demographic attributes, displayed significant inter-group differences. Seven common factors affecting BIM learning performance were obtained by exploratory factor analysis: (1) ability of the instructor, (2) school (college) atmosphere, (3) teamwork, (4) individual ability, (5) understanding of BIM industry applications, (6) social environment incentives, and (7) achievement demand. Finally, the results of an ordered logistic regression revealed that the demographic attributes of participants, the comprehensive ability of the instructor, teamwork, individual ability, and achievement demand significantly affects BIM learning performance. Based on these findings, this paper puts forward suggestions for improving BIM learning performance and provides theoretical support for BIM education and learning in AEC related undergraduate majors.