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Despite the extensive research in project risk management and availability of several techniques and tools, quantifying uncertainty in project schedules remains a challenge. Current risk analysis models suffer from several shortcomings that need to be addressed to provide more reliable and valid schedules. This paper aims to present a dynamic decision support system with the purpose of providing project managers with necessary tool for making real-time informed decisions.

The proposed approach incorporates the widely accepted critical path method (CPM) calculations in a Bayesian network (BN). BN is employed to conduct inferencing and causal analysis and provide probabilistic results, which can improve the decision-making process. Time parameters of each activity in the CPM network is modeled by a set of simulation nodes in the BN. Prior probability distribution of activities duration is extracted from experts using a fuzzy analytical solution.

The model proposed in this paper is able to address some key outstanding issues of current project scheduling techniques through: (1) modeling the causality among different sources of schedule uncertainty, (2) minimizing uncertainty in experts' evaluations, (3) assessing effects of unknown risk factors and (4) using actual activity data for learning the behavior of project and predicting crew productivity.

The purposed methodology provides a framework for the new generation of project schedule analysis tools that are better informed by available knowledge and data, and hence, more reliable and useful.

This paper aims to examine how neighborhood characteristics (income, population composition) and individual building attributes (ownership) affect the recovery period of single-family housing and determine their correlations with property abandonment and changes in residential land use after natural disaster.

This empirical study focuses on Valley Fire, one of the California’s most destructive wildfires in 2015, and uses assessor, community, demographic and sales data to measure recovery of a panel of single-family houses located in Lake County in California between 2012 and 2020. Several regression and correlation models will be developed to test different hypotheses.

This study found that: Recovery period is longer than what expected in most existing literature; ownership status significantly affects recovery period; income level is not a significant factor for shortening the recovery period; and minorities may need more assistance for constant recovery. Findings of this research will help identify at risk communities to avoid uneven housing recovery and lower the rate of property abandonment.

Housing recovery is key to revitalizing communities following major natural disasters. The sociodemographic characteristics of each neighborhood have significant impact on the duration of recovery and possible property abandonment. Understanding how home and neighborhood characteristics affect recovery will help planners prevent long-lasting adverse effects of natural disasters.