This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
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.
Studies about the data quality of National Bridge Inventory (NBI) reveal missing, erroneous, and logically conflicting data. Existing data quality programs lack a focus on detecting the logical inconsistencies within NBI and between NBI and external data sources. For example, within NBI, the structural condition ratings of some bridges improve over a period while having no improvement activity or maintenance funds recorded in relevant attributes documented in NBI. An example of logical inconsistencies between NBI and external data sources is that some bridges are not located within 100 meters of any roads extracted from Google Map. Manual detection of such logical errors is tedious and error-prone. This paper proposes a systematical “hypothesis testing” approach for automatically detecting logical inconsistencies within NBI and between NBI and external data sources. Using this framework, the authors detected logical inconsistencies in the NBI data of two sample states for revealing suspicious data items in NBI. The results showed that about 1% of bridges were not located within 100 meters of any actual roads, and few bridges showed improvements in the structural evaluation without any reported maintenance records.
Tolerance analysis of prefabricated components poses challenges to effective quality control of accelerated construction projects in urban areas. In busy urban environments, accelerated construction methods quickly assemble prefabricated components to achieve workflows that are more efficient and reduce impacts of construction on urban traffic and business. Accelerated constructions also bring challenges of “fit-up:” misalignments between components can occur due to less detailed tolerance assessments of components. Conventional tolerance checking approaches, such as manual mock-up, cannot provide detailed geometric assessments in a timely manner. This paper proposes the integration of an adaptive 3D imaging and spatial pattern analysis methods to achieve detailed and frequent “fit-up” analysis of prefabricated components. The adaptive 3D imaging methods progressively adjust imaging parameters of a laser scanner according to the geometric complexities of prefabricated components captured in data collected so far. The spatial pattern analysis methods automatically analyze deviations of prefabricated components from as-designed models to derive tolerance networks that capture relationships between tolerances of components and identify risks of misalignments.