The objective of the research was to develop and validate a multifaceted model such as a fuzzy Analytical Hierarchy Process (AHP) model that considers both qualitative and quantitative elements with relative significance in assessing the likelihood of falls and aid in the design of NASA Ground Support Operations in aerospace environments. The model represented linguistic variables that quantified significant risk factor levels. Multiple risk factors that contribute to falls in NASA Ground Support Operations are task related, human/personal, environmental, and organizational. Six subject matter experts were asked to participate in a voting system involving a survey where they judge risk factors using the fundamental pairwise comparison scale. The results were analyzed and synthesize using Expert Choice Software, which produced the relative weights for the risk factors. The following are relative weights for these risk factors: Task Related (0.314), Human/Personal (0.307), Environmental (0.248), and Organizational (0.130). The overall inconsistency ratio for all risk factors was 0.07, which indicates the model results were acceptable. The results show that task related risk factors are the highest cause for falls and the organizational risk are the lowest cause for falls in NASA Ground Support Operations. The multiple risk factors weights were validated by having two teams of subject matter experts create priority vectors separately and confirm the weights are valid. The fuzzy AHP model usability was utilizing fifteen subjects in a repeated measures analysis. The subjects were asked to evaluate three scenarios in NASA KSC Ground Support Operations regarding various case studies and historical data. The three scenarios were Shuttle Landing Facility (SLF), Launch Complex Payloads (LCP), and Vehicle Assembly Building (VAB). The Kendall Coefficient of Concordance for assessment agreement between and within the subjects was 1.00. Therefore, the appraisers are applying essentially the same standard when evaluating the scenarios. In addition, a NASA subject matter expert was requested to evaluate the three scenarios also. The predicted value was compared to accepted value. The results from the subject matter expert for the model usability confirmed that the predicted value and accepted value for the likelihood rating were similar. The percentage error for the three scenarios was 0%, 33%, 0% respectively. Multiple descriptive statistics for a 95% confidence interval and t-test are the following: coefficient of variation (21.36), variance (0.251), mean (2.34), and standard deviation (0.501). Model validation was the guarantee of agreement with the NASA standard. Model validation process was partitioned into three components: reliability, objectivity, and consistency. The model was validated by comparing the fuzzy AHP model to NASA accepted model. The results indicate there was minimal variability with fuzzy AHP modeling. As a result, the fuzzy AHP model is confirmed valid. Future research includes developing fall protection guidelines.

Safety of workers in the construction environment remains one of the greatest challenges faced by the construction industry today. Activity-based hazard identification and prevention is limited because construction safety information and knowledge tends to be scattered and fragmented throughout safety regulations, accident records, and experience. With the advancement of information technology in the building and construction industry, a missing link between effective activity-level construction planning and Building Information Modeling (BIM) becomes more evident. The objectives of this study are 1) to formalize the safety management knowledge and to integrate safety aspects into BIM, and 2) to facilitate activity-based hazard identification and prevention in construction planning. To start with, a Construction Safety Ontology is created to organize, store, and re-use construction safety knowledge. Secondly, activity-based workspace visualization and congestion identification methods are investigated to study the hazards caused by the interaction between activities. Computational algorithms are created to process and retrieve activity-based workspace parameters through location tracking data of workers collected by remote sensing technology. Lastly, by introducing workspace parameters into ontology and connecting the ontology with BIM, automated workspace analysis along with job hazard analysis are explored. Results indicate that potential safety hazards can be identified, recorded, analyzed, and prevented in BIM. This study integrates aspects of construction safety into current BIM workflow, which enables performing hazard identification and prevention early in the project planning phase.