Building Information Modeling (BIM) education may accelerate the process of adopting BIM in construction projects. The education community has been examining the best ways of introducing BIM into the curricula. However, individuals in different positions, such as project managers and BIM managers, may require different BIM skills in practice. Thus, understanding BIM skills could help to better formulate the education program for college students and industry professionals. The authors explored this topic by addressing two research questions: 1) What are the BIM skills possessed by individuals that increase the likelihood of having the titles “project manager” and “BIM manager”? 2) How do these skill-sets differ between project managers and BIM managers? These questions are addressed through an analysis of the LinkedIn profiles of architecture, engineering, construction, and operations (AECO) professionals. Data collection involved gathering endorsed skills, number of endorsements, current position, past positions, and years of work experiences from LinkedIn profiles of AECO professionals. This article identified BIM skills and other skills correlated with BIM skills that increase the likelihood of an individual to own the titles of “project manager” and “BIM manager.” This analysis showed that the number of skills shared between project managers and BIM managers were greater than the number of unique skills possessed by either position. While the two positions shared certain skills, subsequent analysis suggested that many of those skills were correlated with different skills. This may suggest that, while there is overlap in the skills possessed between individuals in each position, the way in which they use those skillsets may differ.
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.